Merge branch 'master' of https://github.com/CoolProp/CoolProp

Doxyfile

@@ -2049,7 +2049,7 @@ TAGFILES               =
 # tag file that is based on the input files it reads. See section "Linking to
 # external documentation" for more information about the usage of tag files.
 
-GENERATE_TAGFILE       = CoolPropDoxyLink.tag
+GENERATE_TAGFILE       = Web/_static/doxygen/CoolPropDoxyLink.tag
 
 # If the ALLEXTERNALS tag is set to YES all external class will be listed in the
 # class index. If set to NO only the inherited external classes will be listed.

Web/coolprop/wrappers/Csharp/index.rst

@@ -67,7 +67,7 @@ Once mono c# is installed, you can run the builder and tests using::
     # Move into the folder you just created
     cd CoolProp
     # Make a build folder
-    mkdir -p build/Csharp && cd build
+    mkdir build && cd build
     # Build the makefile using CMake
     cmake .. -DCOOLPROP_CSHARP_MODULE=ON -DBUILD_TESTING=ON
     # Make the C# files (by default files will be generated in folder install_root/Csharp relative to CMakeLists.txt file)

Web/develop/buildbot.rst

@@ -1,4 +1,8 @@
 
+********
+Buildbot
+********
+
 Setting MIME type handler
 =========================
 

Web/develop/cmake.rst

@@ -0,0 +1,9 @@
+.. _cmake:
+
+*****
+CMake
+*****
+
+CMake is a very powerful cross-platform system for generating automated build systems.  On Unix it builds makefiles which it then runs, on windows, it normally builds projects for Visual Studio (though it can also build Makefiles using MinGW on windows).
+
+CMake is used to build each of the wrappers for CoolProp.

Web/fluid_properties/HumidAir.rst

@@ -190,7 +190,8 @@ Values here are obtained at documentation build-time using the Humid Air Propert
  
 .. ipython::
 
-    In [1]: execfile('Validation/HAValidation.py')
+    In [1]: execfile('fluid_properties/Validation/HAValidation.py')
+    
     
 .. 
 

Web/fluid_properties/Incompressibles.bib

@@ -0,0 +1,241 @@
+% This file was created with JabRef 2.10.
+% Encoding: ASCII
+
+
+@Book{ASHRAE2001,
+  Title                    = {{2001 ASHRAE Handbook: Fundamentals}},
+  Author                   = {{American Society of Heating, Refrigerating and Air-Conditioning Engineers}},
+  Publisher                = {{ASHRAE}},
+  Year                     = {2001},
+  Volume                   = {111},
+
+  ISBN                     = {9781883413880},
+  Owner                    = {jowr},
+  Timestamp                = {2014.09.17}
+}
+
+@TechReport{Kauffeld2001,
+  Title                    = {{RP-1166---Behavior of Ice Slurries in Thermal Storage Systems}},
+  Author                   = {Michael Kauffeld},
+  Institution              = {{Danish Technological Institute}},
+  Year                     = {2001},
+  Note                     = {{Sponsored by ASHRAE Technical Committee 6.9 Thermal Storage}},
+
+  Owner                    = {jowr},
+  Timestamp                = {2014.09.22}
+}
+
+@Book{Melinder2010,
+  Title                    = {{Properties of Secondary Working Fluids for Indirect Systems}},
+  Author                   = {{\AA}ke Melinder},
+  Publisher                = {IIF-IIR Publishing},
+  Year                     = {2010},
+
+  Owner                    = {jowr},
+  Timestamp                = {2014.09.17}
+}
+
+@Article{Patek2006,
+  Title                    = {{A computationally effective formulation of the thermodynamic properties of LiBr-H2O solutions from 273 to 500 K over full composition range}},
+  Author                   = {Jaroslav P\'atek and Jaroslav Klomfar},
+  Journal                  = {International Journal of Refrigeration},
+  Year                     = {2006},
+
+  Month                    = {June},
+  Number                   = {4},
+  Pages                    = {566--578},
+  Volume                   = {29},
+
+  Doi                      = {10.1016/j.ijrefrig.2005.10.007},
+  Owner                    = {jowr},
+  Timestamp                = {2013.11.22}
+}
+
+@InBook{Preisegger2010,
+  Title                    = {VDI Heat Atlas},
+  Author                   = {Ewald Preisegger and Felix Flohr and Gernot Krakat and Andreas Gl{\"u}ck and Dietmar Hunold},
+  Chapter                  = {D4 Properties of Industrial Heat Transfer Media},
+  Editor                   = {Peter Stephan},
+  Pages                    = {419--512},
+  Publisher                = {Springer},
+  Year                     = {2010},
+
+  Address                  = {Berlin Heidelberg},
+  Edition                  = {2nd},
+
+  Doi                      = {10.1007/978-3-540-77877-6_20},
+  Owner                    = {jowr},
+  Timestamp                = {2013.06.18}
+}
+
+@Book{Schmidt1979,
+  Title                    = {Properties of Water and Steam in SI-Units},
+  Author                   = {Ernst Schmidt},
+  Publisher                = {Springer},
+  Year                     = {1979},
+  Edition                  = {2nd},
+
+  Owner                    = {jowr},
+  Timestamp                = {2014.09.22}
+}
+
+@Manual{Skovrup2013,
+  Title                    = {{SecCool Properties v1.33}},
+  Author                   = {Morten Juel Skovrup},
+  Organization             = {IPU Refrigeration and Energy Technology},
+  Year                     = {2013},
+
+  Owner                    = {jowr},
+  Timestamp                = {2014.09.17},
+  Url                      = {http://en.ipu.dk/Indhold/refrigeration-and-energy-technology/seccool.aspx}
+}
+
+@TechReport{Zavoico2001,
+  Title                    = {{Solar Power Tower Design Basis Document}},
+  Author                   = {Alexis B. Zavoico},
+  Institution              = {Sandia National Laboratories},
+  Year                     = {2001},
+  Month                    = {July},
+
+  Owner                    = {jowr},
+  Timestamp                = {2013.10.23},
+  Url                      = {http://prod.sandia.gov/techlib/access-control.cgi/2001/012100.pdf}
+}
+
+@Manual{Dynalene2014,
+  Title                    = {{Technical Data Sheet}},
+  Organization             = {{Dynalene Inc.}},
+  Year                     = {2014},
+
+  Owner                    = {jowr},
+  Timestamp                = {2014.09.22}
+}
+
+@Manual{Therminol2014,
+  Title                    = {{Therminol Heat Transfer Reference Disk v5.1}},
+  Organization             = {{Eastman Chemical Company}},
+  Year                     = {2014},
+
+  Owner                    = {jowr},
+  Timestamp                = {2014.09.17},
+  Url                      = {http://www.therminol.com/resources/therminol-reference-disk}
+}
+
+@Manual{Paratherm2013,
+  Title                    = {{Thermal Properties Calculator v6.4}},
+  Organization             = {{Paratherm Ltd.}},
+  Year                     = {2013},
+
+  Owner                    = {jowr},
+  Timestamp                = {2014.09.22},
+  Url                      = {http://paracalc.paratherm.com}
+}
+
+@Manual{Arteco2010,
+  Title                    = {{Technical Information}},
+  Organization             = {{Arteco NV/SA}},
+  Year                     = {2010},
+
+  Owner                    = {jowr},
+  Timestamp                = {2014.09.22}
+}
+
+@Manual{3M2007,
+  Title                    = {{Technical Information}},
+  Organization             = {{3M Company}},
+  Year                     = {2007},
+
+  Owner                    = {jowr},
+  Timestamp                = {2014.09.22}
+}
+
+@Manual{PKS2005,
+  Title                    = {{Technical Data Sheet}},
+  Organization             = {{pro K{\"u}hlsole GmbH}},
+  Year                     = {2005},
+
+  Owner                    = {jowr},
+  Timestamp                = {2014.09.22}
+}
+
+@Manual{Chevron2004,
+  Title                    = {{Technical Data Sheet}},
+  Organization             = {{Chevron Products Company}},
+  Year                     = {2004},
+
+  Owner                    = {jowr},
+  Timestamp                = {2014.09.22}
+}
+
+@Manual{Aspen2001,
+  Title                    = {{Technical Data Sheet}},
+  Organization             = {{Aspen Petroleum AB}},
+  Year                     = {2001},
+
+  Owner                    = {jowr},
+  Timestamp                = {2014.09.22}
+}
+
+@Manual{Clariant2000,
+  Title                    = {{Technical Data Sheet}},
+  Organization             = {{Clariant GmbH}},
+  Year                     = {2000},
+
+  Owner                    = {jowr},
+  Timestamp                = {2014.09.22}
+}
+
+@Manual{Hydro2000,
+  Title                    = {{Technical Information}},
+  Organization             = {{Hydro Chemicals}},
+  Year                     = {2000},
+
+  Owner                    = {jowr},
+  Timestamp                = {2014.09.22}
+}
+
+@Manual{Sulzer1999,
+  Title                    = {{Technical Information}},
+  Organization             = {{Sulzer Chemtech AG}},
+  Year                     = {1999},
+
+  Owner                    = {jowr},
+  Timestamp                = {2014.09.22}
+}
+
+@Manual{Tyfoprop1999,
+  Title                    = {{Technical Information}},
+  Organization             = {{Tyforop Chemie Gmbh}},
+  Year                     = {1999},
+
+  Owner                    = {jowr},
+  Timestamp                = {2014.09.22}
+}
+
+@Manual{Kemira1998,
+  Title                    = {{Technical Data Sheet}},
+  Organization             = {{Kemira Chemicals OY}},
+  Year                     = {1998},
+
+  Owner                    = {jowr},
+  Timestamp                = {2014.09.22}
+}
+
+@Manual{Dow1997,
+  Title                    = {{Technical Data Sheet}},
+  Organization             = {{The Dow Chemical Company}},
+  Year                     = {1997},
+
+  Owner                    = {jowr},
+  Timestamp                = {2014.09.22}
+}
+
+@Manual{Hoechst1995,
+  Title                    = {{Technical Information}},
+  Organization             = {{Hoechst AG}},
+  Year                     = {1995},
+
+  Owner                    = {jowr},
+  Timestamp                = {2014.09.22}
+}
+

Web/fluid_properties/Incompressibles.rst

@@ -1,18 +1,22 @@
 
-.. |degC| replace:: :math:`^\circ\!\!` C
 
 .. _Incompressibles:
 
 Incompressible Fluids
 =====================
 
-In CoolProp, the incompressible fluids are divided into four major groups.
+
+General Introduction
+--------------------
+
+In CoolProp, the incompressible fluids are divided into three major groups.
 
 * :ref:`Pure fluids <Pure>`.
 * :ref:`Mass-based binary mixtures <MassMix>`.
-.. * :ref:`Mole-based binary mixtures <MoleMix>`.
 * :ref:`Volume-based binary mixtures <VoluMix>`.
 
+.. * :ref:`Mole-based binary mixtures <MoleMix>`.
+
 The pure fluids and mass-based binary mixtures are by far the most common fluids
 in this library. While the pure fluids contain data for many different kinds of
 incompressible liquids, almost all of the binary mixtures are aqueous solutions.
@@ -30,17 +34,36 @@ documents with all the
 You can read more about these reports in a dedicated
 :ref:`section<FittingReports>` called :ref:`Fitting Reports<FittingReports>` below.
 
+All incompressible fluids have an arbitrary reference state for enthalpy and entropy.
+During initialisation, the reference state is defined as a temperature of 20 °C
+and a pressure of 1 atm according to the U.S. National Institute of Standards and
+Technology ([NIST](http://www.nist.gov)).
+
+.. math::
+   T_\text{ref} &=  293.15\:\text{K}  &=     68\:\text{°F} \\
+   p_\text{ref} &=  101325\:\text{Pa} &= 14.696\:\text{psi} \\
+   h_\text{ref} &=  0\:\text{KJ}\,\text{kg}^{-1} & \\
+   s_\text{ref} &=  0\:\text{KJ}\,\text{kg}^{-1}\,\text{K}^{-1} & \\
+
+If you use a mixture, the reference state gets updated each time you change the
+composition.
+
+
+Pure Fluid Examples
+-------------------
+
 Incompressible fluids only allow  for a limited subset of input variables. The
-following input pairs are supported: :math:`f(p,T)`, :math:`f(h,p)`, :math:`f(\rho,T)`,
-:math:`f(p,u)` and :math:`f(p,s)`. All functions iterate on :math:`f(p,T)` calls
+following input pairs are supported: :math:`f(p,T)`, :math:`f(p,h)`, :math:`f(p,\rho)`,
+:math:`f(p,u)` and :math:`f(p,s)`. Some fluids also provide saturation state
+information as :math:`f(Q,T)` with :math:`Q=0`. All functions iterate on :math:`f(p,T)` calls
 internally, which makes this combination by far the fastest. However, also the
 other inputs should be fast compared to the full Helmholtz-based EOS implemented
 for then compressible fluids.
 
-A call to the top-level function ``PropsSI`` can provide : density, heat capacity,
-internal energy, enthalpy, entropy, viscosity and thermal conductivity. Hence,
-the available output keys are: ``D``, ``C``, ``U``, ``H``, ``S``, ``V``, ``L``,
-``Tmin``, ``Tmax`` and ``Psat``.
+A call to the top-level function ``PropsSI`` can provide : temperature, pressure,
+density, heat capacity, internal energy, enthalpy, entropy, viscosity and
+thermal conductivity. Hence, the available output keys are: ``T``, ``P``, ``D``,
+``C``, ``U``, ``H``, ``S``, ``V``, ``L``, ``Tmin`` and ``Tmax``.
 
 .. ipython::
 
@@ -52,20 +75,10 @@ the available output keys are: ``D``, ``C``, ``U``, ``H``, ``S``, ``V``, ``L``,
     #Specific heat capacity of Downtherm Q at 500 K and 1 atm
     In [1]: PropsSI('C','T',500,'P',101325,'INCOMP::DowQ')
 
-    #Internal energy of Downtherm Q at 500 K and 1 atm
-    In [1]: PropsSI('U','T',500,'P',101325,'INCOMP::DowQ')
+    In [1]: PropsSI('C','D',809.0659,'P',101325,'INCOMP::DowQ')
 
-    #Enthalpy of Downtherm Q at 500 K and 1 atm
-    In [1]: PropsSI('H','T',500,'P',101325,'INCOMP::DowQ')
-
-    #Entropy of Downtherm Q at 500 K and 1 atm
-    In [1]: PropsSI('S','T',500,'P',101325,'INCOMP::DowQ')
-
-    #Viscosity of Downtherm Q at 500 K and 1 atm
-    In [1]: PropsSI('V','T',500,'P',101325,'INCOMP::DowQ')
-
-    #Thermal conductivity of Downtherm Q at 500 K and 1 atm
-    In [1]: PropsSI('L','T',500,'P',101325,'INCOMP::DowQ')
+    #Saturation pressure of Downtherm Q at 500 K
+    In [1]: PropsSI('P','T',500,'Q',0,'INCOMP::DowQ')
 
     #Minimum temperature for Downtherm Q
     In [1]: PropsSI('Tmin','T',0,'P',0,'INCOMP::DowQ')
@@ -74,65 +87,40 @@ the available output keys are: ``D``, ``C``, ``U``, ``H``, ``S``, ``V``, ``L``,
     In [1]: PropsSI('Tmax','T',0,'P',0,'INCOMP::DowQ')
 
 
-..    #Vapour pressure of Downtherm Q at 500 K, note the dummy pressure to work around https://github.com/CoolProp/CoolProp/issues/145
-    In [1]: PropsSI('Psat','T',500,'P',1e8,'INCOMP::DowQ')
+
+Mixture Examples
+----------------
+
+Almost the same syntax can be used for mixtures. Please note that the mixture
+interface developed for CoolProp 5 has not been ported to the incompressible
+fluids, yet. For now, you have to use the ``PropsSI`` function with a special
+composition notation. Depending on your fluid, you have to supply either the
+:ref:`mass fraction<MassMix>` or the :ref:`volume fraction<VoluMix>` as additional
+parameter. This is done via the fluid name by appending a dash and the
+fraction of the substance other than water. The fraction notation can be in the
+form of percent, ``LiBr-23%``, or as a fraction like in ``LiBr-0.23`` or
+``LiBr[0.23]``, which corresponds to the new mixture syntax in CoolProp5.
+
+..  In addition to the properties available for the pure fluids (``D``, ``C``,
+  ``U``, ``H``, ``S``, ``V``, ``L``,``Tmin`` and ``Tmax``, some mixtures also
+  provide the freezing temperature ``Tfreeze`` as a function of composition.
+
+
+.. ipython::
+
+    In [1]: from CoolProp.CoolProp import PropsSI
+
+    #Density of a lithium bromide solution at 300 K and 1 atm.
+    In [1]: PropsSI('D','T',300,'P',101325,'INCOMP::LiBr[0.23]')
+
+    #Specific heat capacity of a lithium bromide solution at 300 K and 1 atm
+    In [1]: PropsSI('C','T',300,'P',101325,'INCOMP::LiBr-0.23%')
 
 
 
-Pure Fluids
------------
-
-For refrigeration applications, 8 fluids were implemented from Aake Melinder's
-book "Properties of Secondary Working Fluids for Indirect Systems" published in 2010
-by IIR :cite:`Melinder-BOOK-2010` with coefficients obtained from a fit between
--80 |degC| and +100 |degC|: DEB, HCM, HFE, PMS1, PMS2, SAB, HCB and TCO.
-
-Some additional secondary cooling fluids are based on data compiled by Morten
-Juel Skovrup in his `SecCool software <http://en.ipu.dk/Indhold/refrigeration-and-energy-technology/seccool.aspx>`_
-provided by his employer `IPU <http://en.ipu.dk>`_. Fits have been made for the
-manufacturer data stored in the software. The Aspen Temper fluids (AS10, AS20,
-AS30, AS40, AS55) are a blend of potassium formate and sodiumpropionate and the
-Zitrec S group (ZS10, ZS25, ZS40, ZS45 and ZS55) consists mainly of potassium
-acetate and potassium formate.
-
-There are also a few high temperature heat transfer fluids with individual
-temperature ranges. Please refer to the table below for a complete overview.
-For these fluids, information from commercial data sheets was used to obtain
-coefficients.
-
-.. _Pure:
-
-.. csv-table:: All incompressible pure fluids included in CoolProp
-   :widths: 10, 35, 25, 15, 15
-   :header-rows: 1
-   :file: ../_static/fluid_properties/incompressible/table/pure-fluids.csv
 
 
 
-Aqueous Mixtures - Solutions and Brines
----------------------------------------
-
-
-.. _MassMix:
-
-.. csv-table:: All incompressible mass-based binary mixtures included in CoolProp
-   :widths: 10, 30, 20, 10, 10, 10, 10
-   :header-rows: 1
-   :file: ../_static/fluid_properties/incompressible/table/mass-based-fluids.csv
-
-.. .. _MoleMix:
-
-.. .. csv-table:: All incompressible mole-based binary mixtures included in CoolProp
-   :widths: 10, 30, 20, 10, 10, 10, 10
-   :header-rows: 1
-   :file: ../_static/fluid_properties/incompressible/table/mole-based-fluids.csv
-
-.. _VoluMix:
-
-.. csv-table:: All incompressible volume-based binary mixtures included in CoolProp
-   :widths: 10, 30, 20, 10, 10, 10, 10
-   :header-rows: 1
-   :file: ../_static/fluid_properties/incompressible/table/volume-based-fluids.csv
 
 
 
@@ -178,85 +166,12 @@ Equations
 
 Exp or log for visc, other poly or log poly
 
+.. math::
 
+    x(T) &= \sum_{i=0}^n C[i] \cdot T^i \\
+    x(T) &= \exp\left( \frac{C[0]}{T+C[1]} - C[2] \right) \\
+    x(T) &= \exp\left( \log  \left( \left(T+C[0]\right)^{-1} + \left( T+C[0] \right)^{-2} \right) *C[1]+C[2] \right) \\
 
-Incompressible Liquids
-----------------------
-There is also a selection of incompressible liquids implemented.  These only allow for calls with
-temperature and pressure as input and provide only a subset of thermophysical properties, namely:
-density, heat capacity, internal energy, enthalpy, entropy, viscosity and thermal conductivity.
-Hence, the available output keys for the ``Props`` function are: "D", "C", "U", "H", "S", "V", "L",
-"Tmin", "Tmax" and "Psat". An internal iteration allows us to use enthalpy and pressure as inputs,
-but be aware of the reduced computational efficiency.
-
-.. ipython::
-
-    In [1]: from CoolProp.CoolProp import PropsSI
-
-    #Density of HFE-7100 at 300 K and 1 atm.
-    In [1]: PropsSI('D','T',300,'P',101325,'INCOMP::HFE')
-
-
-For refrigeration applications, 8 fluids were implemented from Aake Melinder "Properties of
-Secondary Working Fluids for Indirect Systems" published in 2010 by IIR and coefficients are
-obtained from a fit between -80 and +100 degrees Celsius.
-
-==========================   ===================================================
-Fluid Name                   Description
-==========================   ===================================================
-``DEB``                      Diethyl Benzene
-``HCM``                      Hydrocarbon Mixture (Therminol D12 Solutia)
-``HFE``                      Hydrofluoroether HFE-7100
-``PMS1``                     Polydimethylsiloxan 1.
-``PMS2``                     Polydimethylsiloxan 2.
-``SAB``                      Synthetic alkyl benzene
-``HCB``                      Hydrocarbon blend (Dynalene MV)
-``TCO``                      Terpene from citrus oils
-==========================   ===================================================
-
-Some additional secondary cooling fluids are based on data compiled by Morten Juel Skovrup in
-his `SecCool software <http://en.ipu.dk/Indhold/refrigeration-and-energy-technology/seccool.aspx>`_
-provided by his employer `IPU <http://en.ipu.dk>`_. Fits have been made according to the manufacturer
-data stored in the sodtware. The Aspen Temper fluids are a blend of potassium formate and sodiumpropionate
-and the Zitrec S group consists mainly of potassium acetate and potassium formate.
-
-==========================   ===================================================
-Fluid Name                   Description
-==========================   ===================================================
-``AS10``                     Aspen Temper -10 (-10 to +27.5 C)
-``AS20``                     Aspen Temper -20 (-20 to +27.5 C)
-``AS30``                     Aspen Temper -30 (-30 to +27.5 C)
-``AS40``                     Aspen Temper -40 (-40 to +27.5 C)
-``AS55``                     Aspen Temper -55 (-55 to +27.5 C)
-``ZS10``                     Zitrec S -10 (-10 to +85 C)
-``ZS25``                     Zitrec S -25 (-25 to +85 C)
-``ZS40``                     Zitrec S -40 (-40 to +85 C)
-``ZS45``                     Zitrec S -45 (-45 to +85 C)
-``ZS55``                     Zitrec S -55 (-55 to +85 C)
-==========================   ===================================================
-
-There are also a few high temperature heat transfer fluids with individual temperature ranges. Please
-refer to the file IncompLiquid.h for a complete overview. For these fluids, information from commercial
-data sheets was used to obtain coefficients.
-
-==========================   ===================================================
-Fluid Name                   Description
-==========================   ===================================================
-``TD12``                     Therminol D12 (-85 to +230 C)
-``TVP1``                     Therminol VP-1 (+12 to +397 C)
-``T72``                      Therminol 72 (-10 to +380 C)
-``T66``                      Therminol 66 (0 to +345 C)
-``DowJ``                     Dowtherm J (-80 to +345 C)
-``DowQ``                     Dowtherm Q (-35 to +360 C)
-``TX22``                     Texatherm 22 (0 to +350 C)
-``NaK``                      Nitrate Salt Blend (+300 to +600 C)
-``XLT``                      Syltherm XLT (-100 to +260 C)
-``HC10``                     Dynalene HC-10 (-10 to +218 C)
-``HC20``                     Dynalene HC-20 (-20 to +210 C)
-``HC30``                     Dynalene HC-30 (-30 to +210 C)
-``HC40``                     Dynalene HC-40 (-40 to +200 C)
-``HC50``                     Dynalene HC-50 (-50 to +210 C)
-==========================   ===================================================
 
 All fluids are implemented with polynomials for density and heat capacity with typically 4 coefficients
 and hence a third order polynomial. Thermal conductivity is a second order polynomial and viscosity and
@@ -277,6 +192,8 @@ vapour pressure are exponential functions.
     \mu     &= \exp\left( \frac{C_{\mu}[0]}{T+C_{\mu}[1]} - C_{\mu}[2] \right) \\
     p_{sat}  &= \exp\left( \frac{C_{sat}[0]}{T+C_{sat}[1]} - C_{sat}[2] \right) \\
 
+In some cases, the fit quality for the
+
 
 Brines and Solutions
 --------------------
@@ -388,5 +305,76 @@ then yields the final factor :math:`D` to be multiplied with the other coefficie
 
 
 
-.. bibliography:: ../../CoolPropBibTeXLibrary.bib
-  :style: unsrt
+
+
+
+
+The Different Fluids
+--------------------
+
+The fluids implemented in CoolProp cover a wide range of industrial heat
+transfer media. This database has initially been developed with refrigeration
+systems in mind. That is why the majority of fluids are secondary refrigerants
+with application temperatures close to the freezing point of water. Besides those,
+there is also incompressible water, high temperature heat transfer oils and a
+molten salt mixture for extreme temperatures.
+
+Besides the different technical data sheets and calculation tools provided by
+manufactures, two specific publications provided a lot of data used for the
+incompressible fluids: Åke Melinder's book *Properties of Secondary Working
+Fluids for Indirect Systems* :cite:`Melinder2010` has inspired both, the work on
+pure fluids and aqueous solutions. The second major source of inspiration is the
+`SecCool software <http://en.ipu.dk/Indhold/refrigeration-and-energy-technology/seccool.aspx>`_
+:cite:`Skovrup2013` software, which contains data compiled by Morten Juel
+Skovrup. It is provided free of charge by his employer `IPU <http://en.ipu.dk>`_.
+
+
+.. _Pure:
+
+.. csv-table:: All incompressible pure fluids included in CoolProp
+   :widths: 10, 35, 15, 20, 20
+   :header-rows: 1
+   :file: ../_static/fluid_properties/incompressible/table/pure-fluids.csv
+
+
+There are also a number of water-based mixtures implemented in CoolProp. Most of them
+are secondary heat transfer fluids, but there are also aqueous solutions of
+ammonia :cite:`Melinder2010`, :download:`MAM<../_static/fluid_properties/incompressible/report/MAM_fitreport.pdf>`,
+and lithium bromide :cite:`Patek2006`, :download:`LiBr<../_static/fluid_properties/incompressible/report/LiBr_fitreport.pdf>`.
+
+
+.. _MassMix:
+
+.. csv-table:: All incompressible mass-based binary mixtures included in CoolProp
+   :widths: 10, 30, 12, 12, 12, 12, 12
+   :header-rows: 1
+   :file: ../_static/fluid_properties/incompressible/table/mass-based-fluids.csv
+
+.. .. _MoleMix:
+
+.. .. csv-table:: All incompressible mole-based binary mixtures included in CoolProp
+   :widths: 10, 30, 12, 12, 12, 12, 12
+   :header-rows: 1
+   :file: ../_static/fluid_properties/incompressible/table/mole-based-fluids.csv
+
+.. _VoluMix:
+
+.. csv-table:: All incompressible volume-based binary mixtures included in CoolProp
+   :widths: 10, 30, 12, 12, 12, 12, 12
+   :header-rows: 1
+   :file: ../_static/fluid_properties/incompressible/table/volume-based-fluids.csv
+
+
+For slurry ice, the concentration :math:`x` refers to the solid content and the
+heat capacity includes the heat of fusion. It might be necessary to adjust the
+solid content during heat transfer. The implementation is based on the data
+available in `SecCool<http://en.ipu.dk/Indhold/refrigeration-and-energy-technology/seccool.aspx>`_,
+which was originally recorded at the `Danish Technological Institute (DTI) <http://www.dti.dk/>`_.
+
+
+References
+----------
+
+.. bibliography:: Incompressibles.bib
+   :filter: docname in docnames
+   :style: unsrt

Web/fluid_properties/Validation/HAValidation.py

@@ -214,10 +214,10 @@ print "Isothermal Compressibility of water (kT) [1/Pa]"
 from CoolProp.HumidAirProp import HAProps_Aux
 import numpy as np
 Tv=np.linspace(-60,300,13)+273.15
-Pv=[101325,200,500,1000]
+Pv=[101325,200000,500000,1000000]
 variables="%-10s"%('T')
 for p in Pv:
-    variables+="%-20s"%("p = %-0.3f kPa "%(p))
+    variables+="%-20s"%("p = %-0.3f Pa "%(p))
 print variables
 #Build the actual table
 for T in Tv:
@@ -235,10 +235,10 @@ print "Molar volume of saturated liquid water or ice (vbar_ws) [m^3/mol_H2O]"
 from CoolProp.HumidAirProp import HAProps_Aux
 import numpy as np
 Tv=np.linspace(-60,300,13)+273.15
-Pv=[101325,200,500,1000]
+Pv=[101325,200000,500000,1000000]
 variables="%-10s"%('T')
 for p in Pv:
-    variables+="%-20s"%("p = %-0.3f kPa "%(p))
+    variables+="%-20s"%("p = %-0.3f Pa "%(p))
 print variables
 #Build the actual table
 for T in Tv:
@@ -256,10 +256,10 @@ print "Enhancement factor (f) [no units]"
 from CoolProp.HumidAirProp import HAProps_Aux
 import numpy as np
 Tv=np.array([-60,-40,-20,0,40,80,120,160,200,250,300,350])+273.15
-Pv=[101325,200,500,1000,10000]
+Pv=[101325,200000,500000,1000000,10000000]
 variables="%-10s"%(u'T')
 for p in Pv:
-    variables+="%-20s"%("p = %-0.3f kPa "%(p))
+    variables+="%-20s"%("p = %-0.3f Pa "%(p))
 print variables
 #Build the actual table
 for T in Tv:

Web/parse_bib.py

@@ -1,163 +0,0 @@
-# coding: utf-8
-
-from pybtex.database.input import bibtex
-
-def accent_substitutions(name):
-
-    mapping = [('{\\~n}','\xf1'), # <20>
-               ('{\\`e}','\xe8'), # <20>
-               ("{\\'e}",'\xe9'), # <20>
-               ("{\\'a}",'\xe1'), # <20>
-               ("{\\`a}",'\xe0'), # <20>
-               ("{\\'i}",'\xed'), # <20>
-               ("{\\'i}",'\xed'), # <20>
-               ('{\\\"o}','\xf6'), # <20>
-               ('{\\\"u}','\xfc'), # <20>
-               ('{\\v s}','\x161'), # <20>
-               ]
-    for old, new in mapping:
-        name = name.replace(old, new)
-    return name
-
-def count_substr(s, ss):
-    c = 0
-    for e in s:
-        if e == ss:
-            c += 1
-    return c
-
-def DE(s):
-    try:
-        return s.decode('ascii').encode('utf-8')
-    except UnicodeEncodeError:
-        print 'Decoding error for',s
-
-class BibTeXerClass:
-
-    def __init__(self, fName = '../CoolPropBibTeXLibrary.bib'):
-        parser = bibtex.Parser()
-        bib_data = parser.parse_file(fName)
-        self.entries = bib_data.entries
-
-    def entry2rst(self, key):
-
-        if key.startswith('__'):
-            return ''
-
-        entry = self.entries[key]
-
-        if entry is None:
-            return ''
-
-        try:
-            authors = '; '.join([accent_substitutions(unicode(author).decode('ascii').encode('utf-8')) for author in entry.persons['author']])
-        except UnicodeEncodeError:
-            print 'Decoding error for',[author for author in entry.persons['author']]
-
-        if authors.find('{') > -1 or authors.find('}') > -1:
-            print authors
-            raise ValueError("authors [{authors:s}] may not have '{{' or '}}' character".format(authors = authors))
-
-        fields = entry.fields
-
-        # Strip off the opening and closing brackets
-        fields['title'] = fields['title'].strip()
-        if fields['title'].startswith('{') and fields['title'].endswith('}'):
-            fields['title'] = fields['title'][1:len(entry.fields['title'])-1]
-
-        f = fields
-        for key in f:
-            f[key] = DE(f[key])
-        authors = str(authors)
-
-        if entry.type == 'article':
-            if 'journal' not in fields: fields['journal'] = ''
-            if 'volume' not in fields: fields['volume'] = ''
-            if 'pages' not in fields: fields['pages'] = ''
-
-            return authors + ', ' + f['year'] + ', ' + f['title'] + ', *' + f['journal'] + '*, ' + f['volume'] + ':' + f['pages']
-
-        elif entry.type == 'conference':
-            if 'journal' not in f: f['journal'] = ''
-            return authors + ', ' + f['year'] + ', ' + f['title'] + ', *' + f['booktitle'] + '*'
-
-        elif entry.type == 'mastersthesis':
-            return authors + ', ' + f['year'] + ', ' + f['title'] + ', *' + f['school'] + '*'
-
-        elif entry.type == 'unpublished':
-            return authors + ', ' + f['year'] + ', ' + f['title'] + ', note: ' + f['note']
-
-        elif entry.type == 'book':
-            return authors + ', ' + f['year'] + ', *' + f['title'] + '*, ' + f['publisher']
-
-        elif entry.type == 'techreport':
-            return authors + ', ' + f['year'] + ', *' + f['title'] + '*, ' + f['institution']
-
-        else:
-            print entry
-
-    def entry2HTML(self, key):
-
-        if key.startswith('__'):
-            return ''
-
-        entry = self.entries[key]
-
-        if entry is None:
-            return ''
-
-        try:
-            authors = '; '.join([accent_substitutions(unicode(author).decode('ascii').encode('utf-8')) for author in entry.persons['author']])
-        except UnicodeEncodeError:
-            print 'Decoding error for',[author for author in entry.persons['author']]
-
-        if authors.find('{') > -1 or authors.find('}') > -1:
-            print authors
-            raise ValueError("authors [{authors:s}] may not have '{{' or '}}' character".format(authors = authors))
-
-        fields = entry.fields
-
-        # Strip off the opening and closing brackets
-        fields['title'] = fields['title'].strip()
-        if fields['title'].startswith('{') and fields['title'].endswith('}'):
-            fields['title'] = fields['title'][1:len(entry.fields['title'])-1]
-
-        f = fields
-        for key in f:
-            f[key] = DE(f[key])
-        authors = str(authors)
-
-        if entry.type == 'article':
-            if 'journal' not in fields: fields['journal'] = ''
-            if 'volume' not in fields: fields['volume'] = ''
-            if 'pages' not in fields: fields['pages'] = ''
-
-            return authors + ', ' + f['year'] + ', ' + f['title'] + ', <i>' + f['journal'] + '</i>, ' + f['volume'] + ':' + f['pages']
-
-        elif entry.type == 'conference':
-            if 'journal' not in f: f['journal'] = ''
-            return authors + ', ' + f['year'] + ', ' + f['title'] + ', <i>' + f['booktitle'] + '</i>'
-
-        elif entry.type == 'mastersthesis':
-            return authors + ', ' + f['year'] + ', ' + f['title'] + ', <i>' + f['school'] + '</i>'
-
-        elif entry.type == 'unpublished':
-            return authors + ', ' + f['year'] + ', ' + f['title'] + ', note: ' + f['note']
-
-        elif entry.type == 'book':
-            return authors + ', ' + f['year'] + ', <i>' + f['title'] + '</i>, ' + f['publisher']
-
-        elif entry.type == 'techreport':
-            return authors + ', ' + f['year'] + ', <i>' + f['title'] + '</i>, ' + f['institution']
-
-        else:
-            print entry
-
-    def findentry(self, key):
-        for entry in self.entries:
-            if entry['key'] == key:
-                return entry
-
-if __name__=='__main__':
-    B = BibTeXerClass()
-    print B.entry2rst('Mulero-JPCRD-2012')

dev/fluids/Propylene.json

@@ -1,7 +1,7 @@
 {
   "ALIASES": [
     "propylene", 
-    "PROPYLENE",
+    "PROPYLENE", 
     "PROPYLEN"
   ], 
   "ANCILLARIES": {
@@ -512,5 +512,61 @@
       "rhomolar": 1.021729100294701e-06, 
       "rhomolar_units": "mol/m^3"
     }
+  }, 
+  "TRANSPORT": {
+    "conductivity": {
+      "BibTeX": "Huber-IECR-2003", 
+      "f_int": {
+        "T_reducing": 1.0, 
+        "T_reducing_units": "K", 
+        "a": [
+          0.00109939, 
+          3.72539e-07
+        ], 
+        "t": [
+          0, 
+          1
+        ]
+      }, 
+      "psi": {
+        "a": [
+          1.3521, 
+          -0.123177
+        ], 
+        "rhomolar_reducing": 5309.0, 
+        "rhomolar_reducing_units": "mol/m^3", 
+        "t": [
+          0, 
+          1
+        ]
+      }, 
+      "q_D": 1999999999.9999998, 
+      "q_D_units": "m", 
+      "reference_fluid": "Propane", 
+      "type": "ECS"
+    }, 
+    "viscosity": {
+      "BibTeX": "Huber-IECR-2003", 
+      "epsilon_over_k": 298.9, 
+      "epsilon_over_k_units": "K", 
+      "psi": {
+        "a": [
+          1.33962, 
+          -0.256307, 
+          0.0468211
+        ], 
+        "rhomolar_reducing": 5309.0, 
+        "rhomolar_reducing_units": "mol/m^3", 
+        "t": [
+          0, 
+          1, 
+          2
+        ]
+      }, 
+      "reference_fluid": "Propane", 
+      "sigma_eta": 4.678e-10, 
+      "sigma_eta_units": "m", 
+      "type": "ECS"
+    }
   }
 }

dev/fluids/R116.json

@@ -399,5 +399,61 @@
       "rhomolar": 18.4372956441027, 
       "rhomolar_units": "mol/m^3"
     }
+  }, 
+  "TRANSPORT": {
+    "conductivity": {
+      "BibTeX": "Huber-IECR-2003", 
+      "f_int": {
+        "T_reducing": 1.0, 
+        "T_reducing_units": "K", 
+        "a": [
+          0.00132, 
+          0.0
+        ], 
+        "t": [
+          0, 
+          1
+        ]
+      }, 
+      "psi": {
+        "a": [
+          1.1804, 
+          -0.0539975
+        ], 
+        "rhomolar_reducing": 4518.0, 
+        "rhomolar_reducing_units": "mol/m^3", 
+        "t": [
+          0, 
+          1
+        ]
+      }, 
+      "q_D": 1999999999.9999998, 
+      "q_D_units": "m", 
+      "reference_fluid": "R134a", 
+      "type": "ECS"
+    }, 
+    "viscosity": {
+      "BibTeX": "Huber-IECR-2003", 
+      "epsilon_over_k": 226.16, 
+      "epsilon_over_k_units": "K", 
+      "psi": {
+        "a": [
+          1.21996, 
+          -0.0647835, 
+          0.0
+        ], 
+        "rhomolar_reducing": 4518.0, 
+        "rhomolar_reducing_units": "mol/m^3", 
+        "t": [
+          0, 
+          1, 
+          2
+        ]
+      }, 
+      "reference_fluid": "R134a", 
+      "sigma_eta": 5.249e-10, 
+      "sigma_eta_units": "m", 
+      "type": "ECS"
+    }
   }
 }

dev/fluids/R124.json

@@ -419,7 +419,7 @@
   }, 
   "TRANSPORT": {
     "conductivity": {
-      "BibTeX": "", 
+      "BibTeX": "Huber-IECR-2003", 
       "f_int": {
         "T_reducing": 1.0, 
         "T_reducing_units": "K", 
@@ -437,34 +437,38 @@
           1.0898, 
           -0.0154229
         ], 
-        "rhomolar_reducing": 4103.279546177282, 
+        "rhomolar_reducing": 4103.0, 
         "rhomolar_reducing_units": "mol/m^3", 
         "t": [
           0, 
           1
         ]
       }, 
+      "q_D": 1999999999.9999998, 
+      "q_D_units": "m", 
       "reference_fluid": "Propane", 
       "type": "ECS"
     }, 
     "viscosity": {
-      "BibTeX": "", 
+      "BibTeX": "Huber-IECR-2003", 
       "epsilon_over_k": 275.8, 
       "epsilon_over_k_units": "K", 
       "psi": {
         "a": [
           1.04253, 
-          0.00138528
+          0.00138528, 
+          0.0
         ], 
-        "rhomolar_reducing": 4103.279546177282, 
+        "rhomolar_reducing": 4103.0, 
         "rhomolar_reducing_units": "mol/m^3", 
         "t": [
           0, 
-          1
+          1, 
+          2
         ]
       }, 
       "reference_fluid": "Propane", 
-      "sigma_eta": 5.501e-10, 
+      "sigma_eta": 5.501000000000001e-10, 
       "sigma_eta_units": "m", 
       "type": "ECS"
     }

dev/fluids/R13.json

@@ -449,5 +449,61 @@
       "rhomolar": 0.0011096803080017864, 
       "rhomolar_units": "mol/m^3"
     }
+  }, 
+  "TRANSPORT": {
+    "conductivity": {
+      "BibTeX": "Huber-IECR-2003", 
+      "f_int": {
+        "T_reducing": 1.0, 
+        "T_reducing_units": "K", 
+        "a": [
+          0.00107447, 
+          6.42373e-07
+        ], 
+        "t": [
+          0, 
+          1
+        ]
+      }, 
+      "psi": {
+        "a": [
+          1.1394, 
+          -0.0365562
+        ], 
+        "rhomolar_reducing": 5580.0, 
+        "rhomolar_reducing_units": "mol/m^3", 
+        "t": [
+          0, 
+          1
+        ]
+      }, 
+      "q_D": 2860117379.217243, 
+      "q_D_units": "m", 
+      "reference_fluid": "Propane", 
+      "type": "ECS"
+    }, 
+    "viscosity": {
+      "BibTeX": "Huber-IECR-2003", 
+      "epsilon_over_k": 204.0, 
+      "epsilon_over_k_units": "K", 
+      "psi": {
+        "a": [
+          0.97618, 
+          0.0148047, 
+          0.0
+        ], 
+        "rhomolar_reducing": 5580.0, 
+        "rhomolar_reducing_units": "mol/m^3", 
+        "t": [
+          0, 
+          1, 
+          2
+        ]
+      }, 
+      "reference_fluid": "Propane", 
+      "sigma_eta": 4.971e-10, 
+      "sigma_eta_units": "m", 
+      "type": "ECS"
+    }
   }
 }

dev/fluids/R14.json

@@ -468,5 +468,61 @@
       "rhomolar": 11.391239450514538, 
       "rhomolar_units": "mol/m^3"
     }
+  }, 
+  "TRANSPORT": {
+    "conductivity": {
+      "BibTeX": "Huber-IECR-2003", 
+      "f_int": {
+        "T_reducing": 1.0, 
+        "T_reducing_units": "K", 
+        "a": [
+          0.00119864, 
+          1.90048e-07
+        ], 
+        "t": [
+          0, 
+          1
+        ]
+      }, 
+      "psi": {
+        "a": [
+          1.0442, 
+          0.0
+        ], 
+        "rhomolar_reducing": 7109.0, 
+        "rhomolar_reducing_units": "mol/m^3", 
+        "t": [
+          0, 
+          1
+        ]
+      }, 
+      "q_D": 4413724919.008148, 
+      "q_D_units": "m", 
+      "reference_fluid": "Nitrogen", 
+      "type": "ECS"
+    }, 
+    "viscosity": {
+      "BibTeX": "Huber-IECR-2003", 
+      "epsilon_over_k": 164.44, 
+      "epsilon_over_k_units": "K", 
+      "psi": {
+        "a": [
+          1.10941, 
+          -0.0630268, 
+          0.0
+        ], 
+        "rhomolar_reducing": 7109.0, 
+        "rhomolar_reducing_units": "mol/m^3", 
+        "t": [
+          0, 
+          1, 
+          2
+        ]
+      }, 
+      "reference_fluid": "Nitrogen", 
+      "sigma_eta": 4.543e-10, 
+      "sigma_eta_units": "m", 
+      "type": "ECS"
+    }
   }
 }

dev/fluids/R141b.json

@@ -382,5 +382,61 @@
       "rhomolar": 0.0046023582649346376, 
       "rhomolar_units": "mol/m^3"
     }
+  }, 
+  "TRANSPORT": {
+    "conductivity": {
+      "BibTeX": "Huber-IECR-2003", 
+      "f_int": {
+        "T_reducing": 1.0, 
+        "T_reducing_units": "K", 
+        "a": [
+          0.000521722, 
+          2.92456e-06
+        ], 
+        "t": [
+          0, 
+          1
+        ]
+      }, 
+      "psi": {
+        "a": [
+          1.0867, 
+          -0.0216469
+        ], 
+        "rhomolar_reducing": 3933.0, 
+        "rhomolar_reducing_units": "mol/m^3", 
+        "t": [
+          0, 
+          1
+        ]
+      }, 
+      "q_D": 1999999999.9999998, 
+      "q_D_units": "m", 
+      "reference_fluid": "Propane", 
+      "type": "ECS"
+    }, 
+    "viscosity": {
+      "BibTeX": "Huber-IECR-2003", 
+      "epsilon_over_k": 370.44, 
+      "epsilon_over_k_units": "K", 
+      "psi": {
+        "a": [
+          0.92135, 
+          0.041091, 
+          0.0
+        ], 
+        "rhomolar_reducing": 3933.0, 
+        "rhomolar_reducing_units": "mol/m^3", 
+        "t": [
+          0, 
+          1, 
+          2
+        ]
+      }, 
+      "reference_fluid": "Propane", 
+      "sigma_eta": 5.493000000000001e-10, 
+      "sigma_eta_units": "m", 
+      "type": "ECS"
+    }
   }
 }

dev/fluids/R142b.json

@@ -382,5 +382,61 @@
       "rhomolar": 0.0030613593463350687, 
       "rhomolar_units": "mol/m^3"
     }
+  }, 
+  "TRANSPORT": {
+    "conductivity": {
+      "BibTeX": "Huber-IECR-2003", 
+      "f_int": {
+        "T_reducing": 1.0, 
+        "T_reducing_units": "K", 
+        "a": [
+          0.000940725, 
+          9.88196e-07
+        ], 
+        "t": [
+          0, 
+          1
+        ]
+      }, 
+      "psi": {
+        "a": [
+          1.0749, 
+          -0.0177916
+        ], 
+        "rhomolar_reducing": 4438.0, 
+        "rhomolar_reducing_units": "mol/m^3", 
+        "t": [
+          0, 
+          1
+        ]
+      }, 
+      "q_D": 1624288967.5044746, 
+      "q_D_units": "m", 
+      "reference_fluid": "Propane", 
+      "type": "ECS"
+    }, 
+    "viscosity": {
+      "BibTeX": "Huber-IECR-2003", 
+      "epsilon_over_k": 278.2, 
+      "epsilon_over_k_units": "K", 
+      "psi": {
+        "a": [
+          0.9716, 
+          0.019181, 
+          0.0
+        ], 
+        "rhomolar_reducing": 4438.0, 
+        "rhomolar_reducing_units": "mol/m^3", 
+        "t": [
+          0, 
+          1, 
+          2
+        ]
+      }, 
+      "reference_fluid": "Propane", 
+      "sigma_eta": 5.362000000000001e-10, 
+      "sigma_eta_units": "m", 
+      "type": "ECS"
+    }
   }
 }

dev/fluids/R218.json

@@ -397,5 +397,61 @@
       "rhomolar": 0.0019353026559894213, 
       "rhomolar_units": "mol/m^3"
     }
+  }, 
+  "TRANSPORT": {
+    "conductivity": {
+      "BibTeX": "Huber-IECR-2003", 
+      "f_int": {
+        "T_reducing": 1.0, 
+        "T_reducing_units": "K", 
+        "a": [
+          0.000892659, 
+          1.14912e-06
+        ], 
+        "t": [
+          0, 
+          1
+        ]
+      }, 
+      "psi": {
+        "a": [
+          1.2877, 
+          -0.0758811
+        ], 
+        "rhomolar_reducing": 3340.0, 
+        "rhomolar_reducing_units": "mol/m^3", 
+        "t": [
+          0, 
+          1
+        ]
+      }, 
+      "q_D": 917069412.9838686, 
+      "q_D_units": "m", 
+      "reference_fluid": "Propane", 
+      "type": "ECS"
+    }, 
+    "viscosity": {
+      "BibTeX": "Huber-IECR-2003", 
+      "epsilon_over_k": 266.35, 
+      "epsilon_over_k_units": "K", 
+      "psi": {
+        "a": [
+          1.10225, 
+          -0.00550442, 
+          0.0
+        ], 
+        "rhomolar_reducing": 3340.0, 
+        "rhomolar_reducing_units": "mol/m^3", 
+        "t": [
+          0, 
+          1, 
+          2
+        ]
+      }, 
+      "reference_fluid": "Propane", 
+      "sigma_eta": 5.8e-10, 
+      "sigma_eta_units": "m", 
+      "type": "ECS"
+    }
   }
 }

dev/fluids/R227EA.json

@@ -469,5 +469,61 @@
       "rhomolar": 0.006025348183498283, 
       "rhomolar_units": "mol/m^3"
     }
+  }, 
+  "TRANSPORT": {
+    "conductivity": {
+      "BibTeX": "Huber-IECR-2003", 
+      "f_int": {
+        "T_reducing": 1.0, 
+        "T_reducing_units": "K", 
+        "a": [
+          0.00142313, 
+          8.31496e-09
+        ], 
+        "t": [
+          0, 
+          1
+        ]
+      }, 
+      "psi": {
+        "a": [
+          1.3122, 
+          -0.0874448
+        ], 
+        "rhomolar_reducing": 3370.0, 
+        "rhomolar_reducing_units": "mol/m^3", 
+        "t": [
+          0, 
+          1
+        ]
+      }, 
+      "q_D": 1999999999.9999998, 
+      "q_D_units": "m", 
+      "reference_fluid": "Propane", 
+      "type": "ECS"
+    }, 
+    "viscosity": {
+      "BibTeX": "Huber-IECR-2003", 
+      "epsilon_over_k": 289.34, 
+      "epsilon_over_k_units": "K", 
+      "psi": {
+        "a": [
+          0.76758, 
+          0.254482, 
+          -0.0533748
+        ], 
+        "rhomolar_reducing": 3370.0, 
+        "rhomolar_reducing_units": "mol/m^3", 
+        "t": [
+          0, 
+          1, 
+          2
+        ]
+      }, 
+      "reference_fluid": "Propane", 
+      "sigma_eta": 5.746000000000001e-10, 
+      "sigma_eta_units": "m", 
+      "type": "ECS"
+    }
   }
 }

dev/fluids/R236EA.json

@@ -447,5 +447,61 @@
       "rhomolar": 8.768503197733738, 
       "rhomolar_units": "mol/m^3"
     }
+  }, 
+  "TRANSPORT": {
+    "conductivity": {
+      "BibTeX": "Huber-IECR-2003", 
+      "f_int": {
+        "T_reducing": 1.0, 
+        "T_reducing_units": "K", 
+        "a": [
+          0.00170267, 
+          -4.91063e-07
+        ], 
+        "t": [
+          0, 
+          1
+        ]
+      }, 
+      "psi": {
+        "a": [
+          0.9617, 
+          0.0337897
+        ], 
+        "rhomolar_reducing": 3703.0, 
+        "rhomolar_reducing_units": "mol/m^3", 
+        "t": [
+          0, 
+          1
+        ]
+      }, 
+      "q_D": 1999999999.9999998, 
+      "q_D_units": "m", 
+      "reference_fluid": "R134a", 
+      "type": "ECS"
+    }, 
+    "viscosity": {
+      "BibTeX": "Huber-IECR-2003", 
+      "epsilon_over_k": 318.33, 
+      "epsilon_over_k_units": "K", 
+      "psi": {
+        "a": [
+          1.12216, 
+          -0.0273101, 
+          0.0
+        ], 
+        "rhomolar_reducing": 3703.0, 
+        "rhomolar_reducing_units": "mol/m^3", 
+        "t": [
+          0, 
+          1, 
+          2
+        ]
+      }, 
+      "reference_fluid": "R134a", 
+      "sigma_eta": 5.604e-10, 
+      "sigma_eta_units": "m", 
+      "type": "ECS"
+    }
   }
 }

dev/fluids/R236FA.json

@@ -424,5 +424,61 @@
       "rhomolar": 0.10739880724453099, 
       "rhomolar_units": "mol/m^3"
     }
+  }, 
+  "TRANSPORT": {
+    "conductivity": {
+      "BibTeX": "Huber-IECR-2003", 
+      "f_int": {
+        "T_reducing": 1.0, 
+        "T_reducing_units": "K", 
+        "a": [
+          0.00100946, 
+          1.21255e-06
+        ], 
+        "t": [
+          0, 
+          1
+        ]
+      }, 
+      "psi": {
+        "a": [
+          1.1627, 
+          -0.0437246
+        ], 
+        "rhomolar_reducing": 3626.0, 
+        "rhomolar_reducing_units": "mol/m^3", 
+        "t": [
+          0, 
+          1
+        ]
+      }, 
+      "q_D": 1999999999.9999998, 
+      "q_D_units": "m", 
+      "reference_fluid": "R134a", 
+      "type": "ECS"
+    }, 
+    "viscosity": {
+      "BibTeX": "Huber-IECR-2003", 
+      "epsilon_over_k": 307.24, 
+      "epsilon_over_k_units": "K", 
+      "psi": {
+        "a": [
+          1.10195, 
+          -0.0294253, 
+          0.0
+        ], 
+        "rhomolar_reducing": 3626.0, 
+        "rhomolar_reducing_units": "mol/m^3", 
+        "t": [
+          0, 
+          1, 
+          2
+        ]
+      }, 
+      "reference_fluid": "R134a", 
+      "sigma_eta": 5.644e-10, 
+      "sigma_eta_units": "m", 
+      "type": "ECS"
+    }
   }
 }

dev/fluids/R245fa.json

@@ -580,5 +580,61 @@
       "rhomolar": 0.009673802590130228, 
       "rhomolar_units": "mol/m^3"
     }
+  }, 
+  "TRANSPORT": {
+    "conductivity": {
+      "BibTeX": "Huber-IECR-2003", 
+      "f_int": {
+        "T_reducing": 1.0, 
+        "T_reducing_units": "K", 
+        "a": [
+          0.00164999, 
+          -3.28868e-07
+        ], 
+        "t": [
+          0, 
+          1
+        ]
+      }, 
+      "psi": {
+        "a": [
+          1.1627, 
+          -0.0473491
+        ], 
+        "rhomolar_reducing": 3857.0, 
+        "rhomolar_reducing_units": "mol/m^3", 
+        "t": [
+          0, 
+          1
+        ]
+      }, 
+      "q_D": 1999999999.9999998, 
+      "q_D_units": "m", 
+      "reference_fluid": "R134a", 
+      "type": "ECS"
+    }, 
+    "viscosity": {
+      "BibTeX": "Huber-IECR-2003", 
+      "epsilon_over_k": 329.72, 
+      "epsilon_over_k_units": "K", 
+      "psi": {
+        "a": [
+          1.1529, 
+          -0.044154, 
+          0.0
+        ], 
+        "rhomolar_reducing": 3857.0, 
+        "rhomolar_reducing_units": "mol/m^3", 
+        "t": [
+          0, 
+          1, 
+          2
+        ]
+      }, 
+      "reference_fluid": "R134a", 
+      "sigma_eta": 5.529e-10, 
+      "sigma_eta_units": "m", 
+      "type": "ECS"
+    }
   }
 }

dev/fluids/R32.json

@@ -427,5 +427,61 @@
       "rhomolar": 0.042352544355903056, 
       "rhomolar_units": "mol/m^3"
     }
+  }, 
+  "TRANSPORT": {
+    "conductivity": {
+      "BibTeX": "Huber-IECR-2003", 
+      "f_int": {
+        "T_reducing": 1.0, 
+        "T_reducing_units": "K", 
+        "a": [
+          0.000436654, 
+          1.78134e-06
+        ], 
+        "t": [
+          0, 
+          1
+        ]
+      }, 
+      "psi": {
+        "a": [
+          1.2942, 
+          -0.0924549
+        ], 
+        "rhomolar_reducing": 8150.0, 
+        "rhomolar_reducing_units": "mol/m^3", 
+        "t": [
+          0, 
+          1
+        ]
+      }, 
+      "q_D": 1999999999.9999998, 
+      "q_D_units": "m", 
+      "reference_fluid": "Propane", 
+      "type": "ECS"
+    }, 
+    "viscosity": {
+      "BibTeX": "Huber-IECR-2003", 
+      "epsilon_over_k": 289.65, 
+      "epsilon_over_k_units": "K", 
+      "psi": {
+        "a": [
+          0.7954, 
+          0.0542658, 
+          0.0
+        ], 
+        "rhomolar_reducing": 8150.0, 
+        "rhomolar_reducing_units": "mol/m^3", 
+        "t": [
+          0, 
+          1, 
+          2
+        ]
+      }, 
+      "reference_fluid": "Propane", 
+      "sigma_eta": 4.098e-10, 
+      "sigma_eta_units": "m", 
+      "type": "ECS"
+    }
   }
 }

dev/fluids/RC318.json

@@ -466,5 +466,61 @@
       "rhomolar": 10.156903370453128, 
       "rhomolar_units": "mol/m^3"
     }
+  }, 
+  "TRANSPORT": {
+    "conductivity": {
+      "BibTeX": "Huber-IECR-2003", 
+      "f_int": {
+        "T_reducing": 1.0, 
+        "T_reducing_units": "K", 
+        "a": [
+          0.00135697, 
+          -1.11635e-07
+        ], 
+        "t": [
+          0, 
+          1
+        ]
+      }, 
+      "psi": {
+        "a": [
+          1.5249, 
+          -0.147564
+        ], 
+        "rhomolar_reducing": 3099.0, 
+        "rhomolar_reducing_units": "mol/m^3", 
+        "t": [
+          0, 
+          1
+        ]
+      }, 
+      "q_D": 2808318238.622801, 
+      "q_D_units": "m", 
+      "reference_fluid": "Propane", 
+      "type": "ECS"
+    }, 
+    "viscosity": {
+      "BibTeX": "Huber-IECR-2003", 
+      "epsilon_over_k": 299.76, 
+      "epsilon_over_k_units": "K", 
+      "psi": {
+        "a": [
+          1.21141, 
+          -0.0337573, 
+          0.0
+        ], 
+        "rhomolar_reducing": 3099.0, 
+        "rhomolar_reducing_units": "mol/m^3", 
+        "t": [
+          0, 
+          1, 
+          2
+        ]
+      }, 
+      "reference_fluid": "Propane", 
+      "sigma_eta": 5.947e-10, 
+      "sigma_eta_units": "m", 
+      "type": "ECS"
+    }
   }
 }

dev/incompressible_liquids/CPIncomp/BaseObjects.py

@@ -135,14 +135,21 @@ class IncompressibleData(object):
                       eqnType=self.type, \
                       coeffs=self.coeffs, DEBUG=self.DEBUG)
 
-            elif self.type==IncompressibleData.INCOMPRESSIBLE_LOGEXPONENTIAL and self.data.size>10:
-                if self.DEBUG: print("Poor solution found with log exponential, trying once more with exponential polynomial.")
-                self.type=IncompressibleData.INCOMPRESSIBLE_EXPPOLYNOMIAL
-                self.coeffs = np.zeros((4,6))
-                res,sErr = IncompressibleFitter.fitter(x=x, y=y, z=self.data, \
-                      xbase=xbase, ybase=ybase, \
-                      eqnType=self.type, \
-                      coeffs=self.coeffs, DEBUG=self.DEBUG)
+            elif self.type==IncompressibleData.INCOMPRESSIBLE_LOGEXPONENTIAL:
+                xLen = np.round([len(x)/1.5])
+                yLen = np.round([len(y)/1.5])
+                xLen = np.min([xLen,4])
+                yLen = np.min([yLen,6])
+
+                if (xLen+yLen) > 2:
+                    if self.DEBUG: print("Poor solution found with log exponential, trying once more with exponential polynomial.")
+                    self.type=IncompressibleData.INCOMPRESSIBLE_EXPPOLYNOMIAL
+
+                    self.coeffs = np.zeros((xLen,yLen))
+                    res,sErr = IncompressibleFitter.fitter(x=x, y=y, z=self.data, \
+                          xbase=xbase, ybase=ybase, \
+                          eqnType=self.type, \
+                          coeffs=self.coeffs, DEBUG=self.DEBUG)
 
 #            elif self.type==IncompressibleData.INCOMPRESSIBLE_EXPPOLYNOMIAL:
 #                if self.DEBUG: print("Poor solution found with exponential polynomial, trying once more with normal polynomial.")
@@ -485,7 +492,7 @@ class IncompressibleFitter(object):
             expLog = True
 
         xData = np.array(x_in.flat)
-        if expLog: zData = np.log(z_in.flat)
+        if expLog: zData = np.log(np.clip(z_in.flat,1e-10,IncompressibleData.maxLin))
         else: zData = np.array(z_in.flat)
 
         # Remove np.nan elements

dev/incompressible_liquids/CPIncomp/CoefficientFluids.py

@@ -4,29 +4,29 @@ from CPIncomp.DataObjects import CoefficientData,PureData
 
 
 class NitrateSalt(PureData,CoefficientData):
-    """ 
+    """
     Heat transfer fluid based on 60% NaNO3 and 40% KNO3
     """
     def __init__(self):
         CoefficientData.__init__(self)
-        PureData.__init__(self) 
-        self.name        = "NaK" 
-        self.description = "NitrateSalt"
-        self.reference   = "Solar Power Tower Design Basis Document,  Alexis B. Zavoico, Sandia Labs, USA"
-        
+        PureData.__init__(self)
+        self.name        = "NaK"
+        self.description = "Nitrate salt, heat transfer fluid based on 60% NaNO3 and 40% KNO3"
+        self.reference   = "Zavoico2001"
+
         self.Tmin        = 300 + 273.15
         self.Tmax        = 600 + 273.15
-        self.TminPsat    = self.Tmax 
-        
+        self.TminPsat    = self.Tmax
+
         self.Tbase       = 273.15
-        
+
         #self.temperature.data         = self.getTrange()
         #self.concentration.data       = np.array([     0 ]) # mass fraction
-        
+
         self.density.type = self.density.INCOMPRESSIBLE_POLYNOMIAL
         self.density.source = self.density.SOURCE_COEFFS
         self.density.coeffs = np.array([[2090],[-0.636]])
-        
+
         self.specific_heat.type = self.specific_heat.INCOMPRESSIBLE_POLYNOMIAL
         self.specific_heat.source = self.specific_heat.SOURCE_COEFFS
         self.specific_heat.coeffs = np.array([[1443],[+0.172]])
@@ -38,5 +38,4 @@ class NitrateSalt(PureData,CoefficientData):
         self.viscosity.type = self.viscosity.INCOMPRESSIBLE_POLYNOMIAL
         self.viscosity.source = self.viscosity.SOURCE_COEFFS
         self.viscosity.coeffs = np.array([[22.714],[-0.120],[2.281  * 1e-4],[-1.474 * 1e-7]])/1e3
-        
-        
+

dev/incompressible_liquids/CPIncomp/DataObjects.py

@@ -467,7 +467,7 @@ class CoefficientData(SolutionData):
         if len(array)!=18:
             raise ValueError("The lenght is not equal to 18!")
 
-        self.reference = "Melinder Book"
+        #self.reference = "Melinder Book"
         array = np.array(array)
         tmp = np.zeros((6,4))
 

dev/incompressible_liquids/CPIncomp/DigitalFluids.py

@@ -20,7 +20,7 @@ class HyCool20(PureData,DigitalData):
 
         self.name = "HY20"
         self.description = "HYCOOL 20, Potassium formate"
-        self.reference = "Hydro Chemicals"
+        self.reference = "Hydro2000"
 
         self.Tmax =  50 + 273.15
         self.Tmin = -20 + 273.15
@@ -61,8 +61,8 @@ class HyCool30(PureData,DigitalData):
         PureData.__init__(self)
 
         self.name = "HY30"
-        self.description = "HYCOOL 30, Potassium formate"
-        self.reference = "Hydro Chemicals"
+        self.description = "HyCool 30, Potassium formate"
+        self.reference = "Hydro2000"
 
         self.Tmax =  50 + 273.15
         self.Tmin = -30 + 273.15
@@ -103,8 +103,8 @@ class HyCool40(PureData,DigitalData):
         PureData.__init__(self)
 
         self.name = "HY40"
-        self.description = "HYCOOL 40, Potassium formate"
-        self.reference = "Hydro Chemicals"
+        self.description = "HyCool 40, Potassium formate"
+        self.reference = "Hydro2000"
 
         self.Tmax =  20 + 273.15
         self.Tmin = -40 + 273.15
@@ -139,8 +139,8 @@ class HyCool45(PureData,DigitalData):
         PureData.__init__(self)
 
         self.name = "HY45"
-        self.description = "HYCOOL 45, Potassium formate"
-        self.reference = "Hydro Chemicals"
+        self.description = "HyCool 45, Potassium formate"
+        self.reference = "Hydro2000"
 
         self.Tmax =  20 + 273.15
         self.Tmin = -45 + 273.15
@@ -175,8 +175,8 @@ class HyCool50(PureData,DigitalData):
         PureData.__init__(self)
 
         self.name = "HY50"
-        self.description = "HYCOOL 50, Potassium formate"
-        self.reference = "Hydro Chemicals"
+        self.description = "HyCool 50, Potassium formate"
+        self.reference = "Hydro2000"
 
         self.Tmax =  20 + 273.15
         self.Tmin = -50 + 273.15

dev/incompressible_liquids/CPIncomp/ExampleObjects.py

@@ -5,20 +5,20 @@ from CPIncomp.DataObjects import PureData, SolutionData, DigitalData,\
 
 class PureExample(PureData):
     def __init__(self):
-        PureData.__init__(self) 
+        PureData.__init__(self)
         self.name = "ExamplePure"
         self.description = "Heat transfer fluid TherminolD12 by Solutia"
         self.reference = "Solutia data sheet"
         self.Tmax = 150 + 273.15
         self.Tmin =  50 + 273.15
         self.TminPsat =  self.Tmax
-        
+
         self.density.source           = self.density.SOURCE_DATA
         self.specific_heat.source     = self.specific_heat.SOURCE_DATA
         self.conductivity.source      = self.conductivity.SOURCE_DATA
         self.viscosity.source         = self.viscosity.SOURCE_DATA
         self.saturation_pressure.source = self.saturation_pressure.SOURCE_DATA
-        
+
         self.temperature.data         = np.array([   50,   60,    70,     80,    90,   100,   110,   120,   130,   140,   150])+273.15 # Kelvin
         self.density.data             = np.array([  740,   733,   726,   717,   710,   702,   695,   687,   679,   670,   662])        # kg/m3
         self.specific_heat.data       = np.array([ 2235,  2280,  2326,  2361,  2406,  2445,  2485,  2528,  2571,  2607,  2645])        # J/kg-K
@@ -30,14 +30,14 @@ class PureExample(PureData):
 
 class SolutionExample(SolutionData):
     def __init__(self):
-        SolutionData.__init__(self) 
+        SolutionData.__init__(self)
         self.name = "ExampleSolution"
         self.description = "Ethanol ice slurry"
-        self.reference = "SecCool software"
-        
+        self.reference = "SecCool software,Skovrup2013"
+
         self.temperature.data         = np.array([   -45 ,    -40 ,    -35 ,    -30 ,    -25 ,    -20 ,    -15 ,    -10])+273.15 # Kelvin
         self.concentration.data       = np.array([     5 ,     10 ,     15 ,     20 ,     25 ,     30 ,     35 ])/100.0 # mass fraction
-        
+
         self.density.data             = np.array([
           [1064.0,    1054.6,    1045.3,    1036.3,    1027.4,    1018.6,    1010.0],
           [1061.3,    1052.1,    1043.1,    1034.3,    1025.6,    1017.0,    1008.6],
@@ -47,12 +47,12 @@ class SolutionExample(SolutionData):
           [1040.7,    1033.2,    1025.7,    1018.4,    1011.2,    1004.0,     997.0],
           [1032.3,    1025.3,    1018.5,    1011.7,    1005.1,     998.5,     992.0],
           [1021.5,    1015.3,    1009.2,    1003.1,     997.1,     991.2,     985.4]]) # kg/m3
-        
+
         self.specific_heat.data = np.copy(self.density.data)
-        
+
         self.density.source           = self.density.SOURCE_DATA
         self.specific_heat.source     = self.specific_heat.SOURCE_DATA
-        
+
         self.Tmax = np.max(self.temperature.data)
         self.Tmin = np.min(self.temperature.data)
         self.xmax = np.max(self.concentration.data)
@@ -63,51 +63,51 @@ class SolutionExample(SolutionData):
 
 class DigitalExample(DigitalData):
     def __init__(self):
-        DigitalData.__init__(self) 
+        DigitalData.__init__(self)
 
         self.name = "ExampleDigital"
         self.description = "some fluid"
         self.reference = "none"
-        
+
         self.Tmin = 273.00;
         self.Tmax = 500.00;
         self.xmax = 1.0
         self.xmin = 0.0
         self.xid         = self.ifrac_mass
         self.TminPsat = self.Tmin;
-        
+
         self.temperature.data         = self.getTrange()
         self.concentration.data       = self.getxrange()
-        
+
         def funcRho(T,x):
             return T + x*100.0 + T*(x+0.5)
         self.density.xData,self.density.yData,self.density.data = self.getArray(dataID="D", func=funcRho, x_in=self.temperature.data, y_in=self.concentration.data,DEBUG=self.density.DEBUG)
         self.density.source           = self.density.SOURCE_EQUATION
-        
+
         def funcCp(T,x):
             return T + x*50.0 + T*(x+0.6)
         self.specific_heat.xData,self.specific_heat.yData,self.specific_heat.data = self.getArray(dataID="C", func=funcCp, x_in=self.temperature.data, y_in=self.concentration.data,DEBUG=self.specific_heat.DEBUG)
         self.specific_heat.source     = self.specific_heat.SOURCE_EQUATION
-        
+
 class DigitalExamplePure(PureData,DigitalData):
     def __init__(self):
-        DigitalData.__init__(self) 
-        PureData.__init__(self) 
+        DigitalData.__init__(self)
+        PureData.__init__(self)
 
         self.name = "ExampleDigitalPure"
         self.description = "water at 100 bar"
         self.reference = "none"
-        
+
         self.Tmin = 280.00;
         self.Tmax = 500.00;
 
         self.TminPsat = self.Tmin;
-        
+
         self.temperature.data         = self.getTrange()
         self.concentration.data       = self.getxrange()
-        
+
         import CoolProp.CoolProp as CP
-        
+
         def funcD(T,x):
             return CP.PropsSI('D','T',T,'P',1e7,'water')
         def funcC(T,x):
@@ -118,34 +118,34 @@ class DigitalExamplePure(PureData,DigitalData):
             return CP.PropsSI('V','T',T,'P',1e7,'water')
         def funcP(T,x):
             return CP.PropsSI('P','T',T,'Q',0.0,'water')
-        
+
         self.density.xData,self.density.yData,self.density.data = self.getArray(dataID="D", func=funcD, x_in=self.temperature.data, y_in=self.concentration.data,DEBUG=self.density.DEBUG)
         self.density.source           = self.density.SOURCE_EQUATION
 
         self.specific_heat.xData,self.specific_heat.yData,self.specific_heat.data = self.getArray(dataID="C", func=funcC, x_in=self.temperature.data, y_in=self.concentration.data,DEBUG=self.specific_heat.DEBUG)
         self.specific_heat.source     = self.specific_heat.SOURCE_EQUATION
-        
+
         self.conductivity.xData,self.conductivity.yData,self.conductivity.data = self.getArray(dataID="L", func=funcL, x_in=self.temperature.data, y_in=self.concentration.data,DEBUG=self.conductivity.DEBUG)
         self.conductivity.source           = self.conductivity.SOURCE_EQUATION
-        
+
         self.viscosity.xData,self.viscosity.yData,self.viscosity.data = self.getArray(dataID="V", func=funcV, x_in=self.temperature.data, y_in=self.concentration.data,DEBUG=self.viscosity.DEBUG)
         self.viscosity.source           = self.viscosity.SOURCE_EQUATION
-        
+
         self.saturation_pressure.xData,self.saturation_pressure.yData,self.saturation_pressure.data = self.getArray(dataID="P", func=funcP, x_in=self.temperature.data, y_in=self.concentration.data,DEBUG=self.saturation_pressure.DEBUG)
         self.saturation_pressure.source           = self.saturation_pressure.SOURCE_EQUATION
-              
-        
-        
-        
-        
+
+
+
+
+
 
 class SecCoolExample(CoefficientData):
-    """ 
+    """
     Ethanol-Water mixture according to Melinder book
     Source: SecCool Software
-    """ 
+    """
     def __init__(self):
-        CoefficientData.__init__(self) 
+        CoefficientData.__init__(self)
         self.name = "ExampleSecCool"
         self.description = "Methanol solution"
         #self.reference = "SecCool software"
@@ -155,17 +155,17 @@ class SecCoolExample(CoefficientData):
         self.xmin = 0.0
         self.xid         = self.ifrac_mass
         self.TminPsat =  20 + 273.15
-    
+
         self.Tbase =  -4.48 + 273.15
         self.xbase =  31.57 / 100.0
-       
+
         self.density.type = self.density.INCOMPRESSIBLE_POLYNOMIAL
         self.density.coeffs = self.convertSecCoolArray(np.array([
-           960.24665800, 
-          -1.2903839100, 
-          -0.0161042520, 
-          -0.0001969888, 
-           1.131559E-05, 
+           960.24665800,
+          -1.2903839100,
+          -0.0161042520,
+          -0.0001969888,
+           1.131559E-05,
            9.181999E-08,
           -0.4020348270,
           -0.0162463989,
@@ -176,12 +176,12 @@ class SecCoolExample(CoefficientData):
            0.0001101514,
           -2.320217E-07,
            7.794999E-08,
-           9.937483E-06, 
+           9.937483E-06,
           -1.346886E-06,
            4.141999E-08]))
-        
-        
-        
+
+
+
         self.specific_heat.type = self.specific_heat.INCOMPRESSIBLE_POLYNOMIAL
         self.specific_heat.coeffs = self.convertSecCoolArray(np.array([
            3822.9712300,
@@ -189,97 +189,97 @@ class SecCoolExample(CoefficientData):
            0.0678775826,
            0.0022413893,
           -0.0003045332,
-          -4.758000E-06, 
-           2.3501449500, 
-           0.1788839410, 
-           0.0006828000, 
-           0.0002101166, 
-          -9.812000E-06, 
-          -0.0004724176, 
-          -0.0003317949, 
-           0.0001002032, 
-          -5.306000E-06, 
-           4.242194E-05, 
-           2.347190E-05, 
+          -4.758000E-06,
+           2.3501449500,
+           0.1788839410,
+           0.0006828000,
+           0.0002101166,
+          -9.812000E-06,
+          -0.0004724176,
+          -0.0003317949,
+           0.0001002032,
+          -5.306000E-06,
+           4.242194E-05,
+           2.347190E-05,
           -1.894000E-06]))
 
         self.conductivity.type = self.conductivity.INCOMPRESSIBLE_POLYNOMIAL
         self.conductivity.coeffs = self.convertSecCoolArray(np.array([
-           0.4082066700, 
-          -0.0039816870, 
-           1.583368E-05, 
-          -3.552049E-07, 
-          -9.884176E-10, 
-           4.460000E-10, 
-           0.0006629321, 
-          -2.686475E-05, 
-           9.039150E-07, 
-          -2.128257E-08, 
-          -5.562000E-10, 
-           3.685975E-07, 
-           7.188416E-08, 
-          -1.041773E-08, 
-           2.278001E-10, 
-           4.703395E-08, 
-           7.612361E-11, 
+           0.4082066700,
+          -0.0039816870,
+           1.583368E-05,
+          -3.552049E-07,
+          -9.884176E-10,
+           4.460000E-10,
+           0.0006629321,
+          -2.686475E-05,
+           9.039150E-07,
+          -2.128257E-08,
+          -5.562000E-10,
+           3.685975E-07,
+           7.188416E-08,
+          -1.041773E-08,
+           2.278001E-10,
+           4.703395E-08,
+           7.612361E-11,
           -2.734000E-10]))
 
         self.viscosity.type = self.viscosity.INCOMPRESSIBLE_EXPPOLYNOMIAL
         self.viscosity.coeffs = self.convertSecCoolArray(np.array([
-           1.4725525500, 
-           0.0022218998, 
-          -0.0004406139, 
-           6.047984E-06, 
-          -1.954730E-07, 
-          -2.372000E-09, 
-          -0.0411841566, 
-           0.0001784479, 
-          -3.564413E-06, 
-           4.064671E-08, 
-           1.915000E-08, 
-           0.0002572862, 
-          -9.226343E-07, 
-          -2.178577E-08, 
-          -9.529999E-10, 
-          -1.699844E-06, 
-          -1.023552E-07, 
+           1.4725525500,
+           0.0022218998,
+          -0.0004406139,
+           6.047984E-06,
+          -1.954730E-07,
+          -2.372000E-09,
+          -0.0411841566,
+           0.0001784479,
+          -3.564413E-06,
+           4.064671E-08,
+           1.915000E-08,
+           0.0002572862,
+          -9.226343E-07,
+          -2.178577E-08,
+          -9.529999E-10,
+          -1.699844E-06,
+          -1.023552E-07,
            4.482000E-09]))
 
         self.T_freeze.type = self.T_freeze.INCOMPRESSIBLE_POLYOFFSET
         self.T_freeze.coeffs = np.array([
            27.755555600/100.0,
-          -22.973221700+273.15, 
-          -1.1040507200*100.0, 
-          -0.0120762281*100.0*100.0, 
+          -22.973221700+273.15,
+          -1.1040507200*100.0,
+          -0.0120762281*100.0*100.0,
           -9.343458E-05*100.0*100.0*100.0])
-        
+
         self.density.source           = self.density.SOURCE_COEFFS
         self.specific_heat.source     = self.specific_heat.SOURCE_COEFFS
         self.conductivity.source      = self.conductivity.SOURCE_COEFFS
         self.viscosity.source         = self.viscosity.SOURCE_COEFFS
         self.T_freeze.source          = self.T_freeze.SOURCE_COEFFS
-        
-        
+
+
 class MelinderExample(CoefficientData):
-    """ 
+    """
     Methanol-Water mixture according to Melinder book
     Source: Book
-    """ 
+    """
     def __init__(self):
-        CoefficientData.__init__(self) 
+        CoefficientData.__init__(self)
         self.name = "ExampleMelinder"
         self.description = "Methanol solution"
-        self.reference = "Melinder-BOOK-2010"
+        self.reference = "Melinder2010"
         self.Tmax =  40 + 273.15
         self.Tmin = -50 + 273.15
         self.xmax = 0.6
         self.xmin = 0.0
         self.xid         = self.ifrac_mass
         self.TminPsat =  self.Tmax
-    
+
         self.Tbase =   3.5359 + 273.15;
         self.xbase =  30.5128 / 100.0
-       
+
         coeffs = np.array([
         [-26.29           , 958.1           ,3887           ,   0.4175            ,   1.153           ],
         [ -0.000002575    ,  -0.4151        ,   7.201       ,   0.0007271         ,  -0.03866         ],
@@ -300,7 +300,6 @@ class MelinderExample(CoefficientData):
         [ -0.0000000005407,  -0.0000001325  ,   0.000007373 ,  -0.0000000000004573,  -0.0000000009105 ],
         [  0.00000002363  ,  -0.00000007727 ,   0.000006433 ,  -0.0000000002033   ,  -0.0000000008472 ]
         ])
-        
+
         self.setMelinderMatrix(coeffs)
-        
-        
+

dev/incompressible_liquids/CPIncomp/MelinderFluids.py

@@ -5,15 +5,15 @@ from CPIncomp.BaseObjects import IncompressibleFitter
 
 
 class DEBLiquidClass(CoefficientData,PureData):
-    """ 
+    """
     Pure fluid according to Melinder's book
-    """ 
+    """
     def __init__(self):
         CoefficientData.__init__(self)
-        PureData.__init__(self) 
+        PureData.__init__(self)
         self.name = "DEB"
         self.description = "Diethylbenzene mixture - Dowtherm J Dow Chemical Co."
-        self.reference = "Melinder-BOOK-2010"
+        self.reference = "Melinder2010"
 
         self.Tmin = -80.0 + 273.15
         self.Tmax = 100.0 + 273.15
@@ -30,7 +30,7 @@ class DEBLiquidClass(CoefficientData,PureData):
 
         self.conductivity.type = self.conductivity.INCOMPRESSIBLE_POLYNOMIAL
         _,_,self.conductivity.coeffs = IncompressibleFitter.shapeArray(np.array([0.000189132,-2.06364e-07]))
-        
+
         self.density.source           = self.density.SOURCE_COEFFS
         self.specific_heat.source     = self.specific_heat.SOURCE_COEFFS
         self.conductivity.source      = self.conductivity.SOURCE_COEFFS
@@ -38,15 +38,15 @@ class DEBLiquidClass(CoefficientData,PureData):
 
 
 class HCMLiquidClass(CoefficientData,PureData):
-    """ 
+    """
     Pure fluid according to Melinder's book
-    """ 
+    """
     def __init__(self):
-        CoefficientData.__init__(self) 
-        PureData.__init__(self) 
+        CoefficientData.__init__(self)
+        PureData.__init__(self)
         self.name = "HCM"
         self.description = "Hydrocarbon mixture (synthetic) - Therminol D12 (Gilotherm D12) Solutia"
-        self.reference = "Melinder-BOOK-2010"
+        self.reference = "Melinder2010"
 
         self.Tmin = -80.0 + 273.15
         self.Tmax = 100.0 + 273.15
@@ -63,7 +63,7 @@ class HCMLiquidClass(CoefficientData,PureData):
 
         self.conductivity.type = self.conductivity.INCOMPRESSIBLE_POLYNOMIAL
         _,_,self.conductivity.coeffs = IncompressibleFitter.shapeArray(np.array([0.000153716,-1.51212e-07]))
-        
+
         self.density.source           = self.density.SOURCE_COEFFS
         self.specific_heat.source     = self.specific_heat.SOURCE_COEFFS
         self.conductivity.source      = self.conductivity.SOURCE_COEFFS
@@ -71,15 +71,15 @@ class HCMLiquidClass(CoefficientData,PureData):
 
 
 class HFELiquidClass(CoefficientData,PureData):
-    """ 
+    """
     Pure fluid according to Melinder's book
-    """ 
+    """
     def __init__(self):
-        CoefficientData.__init__(self) 
-        PureData.__init__(self) 
+        CoefficientData.__init__(self)
+        PureData.__init__(self)
         self.name = "HFE"
         self.description = "Hydrofluoroether - HFE-7100 3M Novec"
-        self.reference = "Melinder-BOOK-2010"
+        self.reference = "Melinder2010"
 
         self.Tmin = -80.0 + 273.15
         self.Tmax = 100.0 + 273.15
@@ -96,7 +96,7 @@ class HFELiquidClass(CoefficientData,PureData):
 
         self.conductivity.type = self.conductivity.INCOMPRESSIBLE_POLYNOMIAL
         _,_,self.conductivity.coeffs = IncompressibleFitter.shapeArray(np.array([9.92958e-05,-8.33333e-08]))
-        
+
         self.density.source           = self.density.SOURCE_COEFFS
         self.specific_heat.source     = self.specific_heat.SOURCE_COEFFS
         self.conductivity.source      = self.conductivity.SOURCE_COEFFS
@@ -104,15 +104,15 @@ class HFELiquidClass(CoefficientData,PureData):
 
 
 class PMS1LiquidClass(CoefficientData,PureData):
-    """ 
+    """
     Pure fluid according to Melinder's book
-    """ 
+    """
     def __init__(self):
-        CoefficientData.__init__(self) 
-        PureData.__init__(self) 
+        CoefficientData.__init__(self)
+        PureData.__init__(self)
         self.name = "PMS1"
         self.description = "Polydimethylsiloxan 1. - Baysilone KT3"
-        self.reference = "Melinder-BOOK-2010"
+        self.reference = "Melinder2010"
 
         self.Tmin = -80.0 + 273.15
         self.Tmax = 100.0 + 273.15
@@ -129,7 +129,7 @@ class PMS1LiquidClass(CoefficientData,PureData):
 
         self.conductivity.type = self.conductivity.INCOMPRESSIBLE_POLYNOMIAL
         _,_,self.conductivity.coeffs = IncompressibleFitter.shapeArray(np.array([0.000207526,-2.84167e-07]))
-        
+
         self.density.source           = self.density.SOURCE_COEFFS
         self.specific_heat.source     = self.specific_heat.SOURCE_COEFFS
         self.conductivity.source      = self.conductivity.SOURCE_COEFFS
@@ -137,15 +137,15 @@ class PMS1LiquidClass(CoefficientData,PureData):
 
 
 class PMS2LiquidClass(CoefficientData,PureData):
-    """ 
+    """
     Pure fluid according to Melinder's book
-    """ 
+    """
     def __init__(self):
-        CoefficientData.__init__(self) 
-        PureData.__init__(self) 
+        CoefficientData.__init__(self)
+        PureData.__init__(self)
         self.name = "PMS2"
         self.description = "Polydimethylsiloxan 2. - Syltherm XLT Dow Corning Co."
-        self.reference = "Melinder-BOOK-2010"
+        self.reference = "Melinder2010"
 
         self.Tmin = -80.0 + 273.15
         self.Tmax = 100.0 + 273.15
@@ -162,7 +162,7 @@ class PMS2LiquidClass(CoefficientData,PureData):
 
         self.conductivity.type = self.conductivity.INCOMPRESSIBLE_POLYNOMIAL
         _,_,self.conductivity.coeffs = IncompressibleFitter.shapeArray(np.array([0.000172305,-2.11212e-07]))
-        
+
         self.density.source           = self.density.SOURCE_COEFFS
         self.specific_heat.source     = self.specific_heat.SOURCE_COEFFS
         self.conductivity.source      = self.conductivity.SOURCE_COEFFS
@@ -170,15 +170,15 @@ class PMS2LiquidClass(CoefficientData,PureData):
 
 
 class SABLiquidClass(CoefficientData,PureData):
-    """ 
+    """
     Pure fluid according to Melinder's book
-    """ 
+    """
     def __init__(self):
-        CoefficientData.__init__(self) 
-        PureData.__init__(self) 
+        CoefficientData.__init__(self)
+        PureData.__init__(self)
         self.name = "SAB"
         self.description = "Synthetic alkyl benzene - Marlotherm X"
-        self.reference = "Melinder-BOOK-2010"
+        self.reference = "Melinder2010"
 
         self.Tmin = -80.0 + 273.15
         self.Tmax = 100.0 + 273.15
@@ -195,7 +195,7 @@ class SABLiquidClass(CoefficientData,PureData):
 
         self.conductivity.type = self.conductivity.INCOMPRESSIBLE_POLYNOMIAL
         _,_,self.conductivity.coeffs = IncompressibleFitter.shapeArray(np.array([0.000208374,-2.61667e-07]))
-        
+
         self.density.source           = self.density.SOURCE_COEFFS
         self.specific_heat.source     = self.specific_heat.SOURCE_COEFFS
         self.conductivity.source      = self.conductivity.SOURCE_COEFFS
@@ -203,15 +203,15 @@ class SABLiquidClass(CoefficientData,PureData):
 
 
 class HCBLiquidClass(CoefficientData,PureData):
-    """ 
+    """
     Pure fluid according to Melinder's book
-    """ 
+    """
     def __init__(self):
-        CoefficientData.__init__(self) 
-        PureData.__init__(self) 
+        CoefficientData.__init__(self)
+        PureData.__init__(self)
         self.name = "HCB"
         self.description = "Hydrocarbon blend - Dynalene MV"
-        self.reference = "Melinder-BOOK-2010"
+        self.reference = "Melinder2010"
 
         self.Tmin = -80.0 + 273.15
         self.Tmax = 100.0 + 273.15
@@ -228,7 +228,7 @@ class HCBLiquidClass(CoefficientData,PureData):
 
         self.conductivity.type = self.conductivity.INCOMPRESSIBLE_POLYNOMIAL
         _,_,self.conductivity.coeffs = IncompressibleFitter.shapeArray(np.array([0.000203186,-2.3869e-07]))
-        
+
         self.density.source           = self.density.SOURCE_COEFFS
         self.specific_heat.source     = self.specific_heat.SOURCE_COEFFS
         self.conductivity.source      = self.conductivity.SOURCE_COEFFS
@@ -236,15 +236,15 @@ class HCBLiquidClass(CoefficientData,PureData):
 
 
 class TCOLiquidClass(CoefficientData,PureData):
-    """ 
+    """
     Pure fluid according to Melinder's book
-    """ 
+    """
     def __init__(self):
-        CoefficientData.__init__(self) 
-        PureData.__init__(self) 
+        CoefficientData.__init__(self)
+        PureData.__init__(self)
         self.name = "TCO"
         self.description = "Terpene from citrus oils - d-Limonene"
-        self.reference = "Melinder-BOOK-2010"
+        self.reference = "Melinder2010"
 
         self.Tmin = -80.0 + 273.15
         self.Tmax = 100.0 + 273.15
@@ -261,7 +261,7 @@ class TCOLiquidClass(CoefficientData,PureData):
 
         self.conductivity.type = self.conductivity.INCOMPRESSIBLE_POLYNOMIAL
         _,_,self.conductivity.coeffs = IncompressibleFitter.shapeArray(np.array([0.000174156,-1.85052e-07]))
-        
+
         self.density.source           = self.density.SOURCE_COEFFS
         self.specific_heat.source     = self.specific_heat.SOURCE_COEFFS
         self.conductivity.source      = self.conductivity.SOURCE_COEFFS
@@ -275,18 +275,18 @@ class TCOLiquidClass(CoefficientData,PureData):
 
 
 
-       
-        
+
+
 class EGSolution(CoefficientData):
     def __init__(self):
-        CoefficientData.__init__(self) 
+        CoefficientData.__init__(self)
         self.name = "MEG"
         self.description = "Ethylene Glycol"
-        self.reference = "Melinder-BOOK-2010"
+        self.reference = "Melinder2010"
 
         self.Tmin     =-100 + 273.15
         self.Tmax     = 100 + 273.15
-        self.TminPsat = self.Tmax 
+        self.TminPsat = self.Tmax
 
         self.xmin     = 0.0
         self.xmax     = 0.6
@@ -317,18 +317,18 @@ class EGSolution(CoefficientData):
         ])
 
         self.setMelinderMatrix(coeffs)
-        
+
 
 class PGSolution(CoefficientData):
     def __init__(self):
-        CoefficientData.__init__(self) 
+        CoefficientData.__init__(self)
         self.name = "MPG"
         self.description = "Propylene Glycol"
-        self.reference = "Melinder-BOOK-2010"
+        self.reference = "Melinder2010"
 
         self.Tmin     =-100 + 273.15
         self.Tmax     = 100 + 273.15
-        self.TminPsat = self.Tmax 
+        self.TminPsat = self.Tmax
 
         self.xmin     = 0.0
         self.xmax     = 0.6
@@ -359,18 +359,18 @@ class PGSolution(CoefficientData):
         ])
 
         self.setMelinderMatrix(coeffs)
-        
+
 
 class EASolution(CoefficientData):
     def __init__(self):
-        CoefficientData.__init__(self) 
+        CoefficientData.__init__(self)
         self.name = "MEA"
         self.description = "Ethyl Alcohol (Ethanol)"
-        self.reference = "Melinder-BOOK-2010"
+        self.reference = "Melinder2010"
 
         self.Tmin     =-100 + 273.15
         self.Tmax     =  40 + 273.15
-        self.TminPsat = self.Tmax 
+        self.TminPsat = self.Tmax
 
         self.xmin     = 0.0
         self.xmax     = 0.6
@@ -401,18 +401,18 @@ class EASolution(CoefficientData):
         ])
 
         self.setMelinderMatrix(coeffs)
-        
+
 
 class MASolution(CoefficientData):
     def __init__(self):
-        CoefficientData.__init__(self) 
+        CoefficientData.__init__(self)
         self.name = "MMA"
         self.description = "Methyl Alcohol (Methanol)"
-        self.reference = "Melinder-BOOK-2010"
+        self.reference = "Melinder2010"
 
         self.Tmin     =-100 + 273.15
         self.Tmax     =  40 + 273.15
-        self.TminPsat = self.Tmax 
+        self.TminPsat = self.Tmax
 
         self.xmin     = 0.0
         self.xmax     = 0.6
@@ -444,18 +444,18 @@ class MASolution(CoefficientData):
         ])
 
         self.setMelinderMatrix(coeffs)
-        
+
 
 class GLSolution(CoefficientData):
     def __init__(self):
-        CoefficientData.__init__(self) 
+        CoefficientData.__init__(self)
         self.name = "MGL"
         self.description = "Glycerol"
-        self.reference = "Melinder-BOOK-2010"
+        self.reference = "Melinder2010"
 
         self.Tmin     =-100 + 273.15
         self.Tmax     =  40 + 273.15
-        self.TminPsat = self.Tmax 
+        self.TminPsat = self.Tmax
 
         self.xmin     = 0.0
         self.xmax     = 0.6
@@ -486,18 +486,18 @@ class GLSolution(CoefficientData):
         ])
 
         self.setMelinderMatrix(coeffs)
-        
+
 
 class AMSolution(CoefficientData):
     def __init__(self):
-        CoefficientData.__init__(self) 
+        CoefficientData.__init__(self)
         self.name = "MAM"
         self.description = "Ammonia (NH3)"
-        self.reference = "Melinder-BOOK-2010"
+        self.reference = "Melinder2010"
 
         self.Tmin     =-100 + 273.15
         self.Tmax     =  30 + 273.15
-        self.TminPsat = self.Tmax 
+        self.TminPsat = self.Tmax
 
         self.xmin     = 0.0
         self.xmax     = 0.3
@@ -528,18 +528,18 @@ class AMSolution(CoefficientData):
         ])
 
         self.setMelinderMatrix(coeffs)
-        
+
 
 class KCSolution(CoefficientData):
     def __init__(self):
-        CoefficientData.__init__(self) 
+        CoefficientData.__init__(self)
         self.name = "MKC"
         self.description = "Potassium Carbonate (K2CO3)"
-        self.reference = "Melinder-BOOK-2010"
+        self.reference = "Melinder2010"
 
         self.Tmin     =-100 + 273.15
         self.Tmax     =  40 + 273.15
-        self.TminPsat = self.Tmax 
+        self.TminPsat = self.Tmax
 
         self.xmin     = 0.0
         self.xmax     = 0.4
@@ -570,18 +570,18 @@ class KCSolution(CoefficientData):
         ])
 
         self.setMelinderMatrix(coeffs)
-        
+
 
 class CASolution(CoefficientData):
     def __init__(self):
-        CoefficientData.__init__(self) 
+        CoefficientData.__init__(self)
         self.name = "MCA"
         self.description = "Calcium Chloride (CaCl2)"
-        self.reference = "Melinder-BOOK-2010"
+        self.reference = "Melinder2010"
 
         self.Tmin     =-100.0 + 273.15
         self.Tmax     =  40.0 + 273.15
-        self.TminPsat = self.Tmax 
+        self.TminPsat = self.Tmax
 
         self.xmin     = 0.0
         self.xmax     = 0.3
@@ -612,18 +612,18 @@ class CASolution(CoefficientData):
         ])
 
         self.setMelinderMatrix(coeffs)
-        
+
 
 class MGSolution(CoefficientData):
     def __init__(self):
-        CoefficientData.__init__(self) 
+        CoefficientData.__init__(self)
         self.name = "MMG"
         self.description = "(MgCl2)"
-        self.reference = "Melinder-BOOK-2010"
+        self.reference = "Melinder2010"
 
         self.Tmin     =-100.0 + 273.15
         self.Tmax     =  40.0 + 273.15
-        self.TminPsat = self.Tmax 
+        self.TminPsat = self.Tmax
 
         self.xmin     = 0.0
         self.xmax     = 0.3
@@ -654,18 +654,18 @@ class MGSolution(CoefficientData):
         ])
 
         self.setMelinderMatrix(coeffs)
-        
+
 
 class NASolution(CoefficientData):
     def __init__(self):
-        CoefficientData.__init__(self) 
+        CoefficientData.__init__(self)
         self.name = "MNA"
         self.description = "Sodium Chloride (NaCl)"
-        self.reference = "Melinder-BOOK-2010"
+        self.reference = "Melinder2010"
 
         self.Tmin     =-100.0 + 273.15
         self.Tmax     =  40.0 + 273.15
-        self.TminPsat = self.Tmax 
+        self.TminPsat = self.Tmax
 
         self.xmin     = 0.0
         self.xmax     = 0.23
@@ -696,18 +696,18 @@ class NASolution(CoefficientData):
         ])
 
         self.setMelinderMatrix(coeffs)
-        
+
 
 class KASolution(CoefficientData):
     def __init__(self):
-        CoefficientData.__init__(self) 
+        CoefficientData.__init__(self)
         self.name = "MKA"
         self.description = "Potassium Acetate (CH3CO2K)"
-        self.reference = "Melinder-BOOK-2010"
+        self.reference = "Melinder2010"
 
         self.Tmin     =-100.0 + 273.15
         self.Tmax     =  40.0 + 273.15
-        self.TminPsat = self.Tmax 
+        self.TminPsat = self.Tmax
 
         self.xmin     = 0.0
         self.xmax     = 0.45
@@ -738,18 +738,18 @@ class KASolution(CoefficientData):
         ])
 
         self.setMelinderMatrix(coeffs)
-        
+
 
 class KFSolution(CoefficientData):
     def __init__(self):
-        CoefficientData.__init__(self) 
+        CoefficientData.__init__(self)
         self.name = "MKF"
         self.description = "Potassium Formate (CHKO2)"
-        self.reference = "Melinder-BOOK-2010"
+        self.reference = "Melinder2010"
 
         self.Tmin     =-100.0 + 273.15
         self.Tmax     =  40.0 + 273.15
-        self.TminPsat = self.Tmax 
+        self.TminPsat = self.Tmax
 
         self.xmin     = 0.0
         self.xmax     = 0.48
@@ -780,18 +780,18 @@ class KFSolution(CoefficientData):
         ])
 
         self.setMelinderMatrix(coeffs)
-        
+
 
 class LISolution(CoefficientData):
     def __init__(self):
-        CoefficientData.__init__(self) 
+        CoefficientData.__init__(self)
         self.name = "MLI"
         self.description = "Lithium Chloride (LiCl)"
-        self.reference = "Melinder-BOOK-2010"
+        self.reference = "Melinder2010"
 
         self.Tmin     =-100.0 + 273.15
         self.Tmax     =  40.0 + 273.15
-        self.TminPsat = self.Tmax 
+        self.TminPsat = self.Tmax
 
         self.xmin     = 0.0
         self.xmax     = 0.24
@@ -822,4 +822,4 @@ class LISolution(CoefficientData):
         ])
 
         self.setMelinderMatrix(coeffs)
-        
+

dev/incompressible_liquids/CPIncomp/PureFluids.py

@@ -1,13 +1,13 @@
 from __future__ import division, print_function
 import numpy as np
 from CPIncomp.DataObjects import PureData
-   
+
 class TherminolD12(PureData):
-    """ 
+    """
     Heat transfer fluid Therminol D12 by Solutia
     """
     def __init__(self):
-        PureData.__init__(self) 
+        PureData.__init__(self)
         self.density.source           = self.density.SOURCE_DATA
         self.specific_heat.source     = self.specific_heat.SOURCE_DATA
         self.conductivity.source      = self.conductivity.SOURCE_DATA
@@ -17,22 +17,22 @@ class TherminolD12(PureData):
         self.density.data             = np.array([+8.35000E+2, +8.32000E+2, +8.28000E+2, +8.25000E+2, +8.22000E+2, +8.18000E+2, +8.15000E+2, +8.11000E+2, +8.08000E+2, +8.05000E+2, +8.01000E+2, +7.98000E+2, +7.94000E+2, +7.91000E+2, +7.87000E+2, +7.84000E+2, +7.80000E+2, +7.77000E+2, +7.73000E+2, +7.70000E+2, +7.66000E+2, +7.62000E+2, +7.59000E+2, +7.55000E+2, +7.52000E+2, +7.48000E+2, +7.44000E+2, +7.41000E+2, +7.37000E+2, +7.33000E+2, +7.29000E+2, +7.26000E+2, +7.22000E+2, +7.18000E+2, +7.14000E+2, +7.10000E+2, +7.06000E+2, +7.03000E+2, +6.99000E+2, +6.95000E+2, +6.91000E+2, +6.87000E+2, +6.82000E+2, +6.78000E+2, +6.74000E+2, +6.70000E+2, +6.66000E+2, +6.61000E+2, +6.57000E+2, +6.53000E+2, +6.48000E+2, +6.44000E+2, +6.39000E+2, +6.35000E+2, +6.30000E+2, +6.25000E+2, +6.20000E+2, +6.16000E+2, +6.11000E+2, +6.06000E+2, +6.00000E+2, +5.95000E+2, +5.90000E+2, +5.84000E+2])         # kg/m3
         self.specific_heat.data       = np.array([+1.69400E+0, +1.71200E+0, +1.73100E+0, +1.75000E+0, +1.76800E+0, +1.78700E+0, +1.80600E+0, +1.82400E+0, +1.84300E+0, +1.86200E+0, +1.88100E+0, +1.90000E+0, +1.91900E+0, +1.93800E+0, +1.95700E+0, +1.97700E+0, +1.99600E+0, +2.01500E+0, +2.03500E+0, +2.05400E+0, +2.07300E+0, +2.09300E+0, +2.11300E+0, +2.13200E+0, +2.15200E+0, +2.17200E+0, +2.19100E+0, +2.21100E+0, +2.23100E+0, +2.25100E+0, +2.27100E+0, +2.29100E+0, +2.31200E+0, +2.33200E+0, +2.35200E+0, +2.37300E+0, +2.39300E+0, +2.41400E+0, +2.43400E+0, +2.45500E+0, +2.47600E+0, +2.49600E+0, +2.51700E+0, +2.53800E+0, +2.55900E+0, +2.58000E+0, +2.60200E+0, +2.62300E+0, +2.64400E+0, +2.66600E+0, +2.68700E+0, +2.70900E+0, +2.73100E+0, +2.75300E+0, +2.77500E+0, +2.79700E+0, +2.82000E+0, +2.84200E+0, +2.86500E+0, +2.88800E+0, +2.91100E+0, +2.93500E+0, +2.95900E+0, +2.98300E+0])*1000.   # J/kg-K
         self.conductivity.data        = np.array([+1.24400E-1, +1.23800E-1, +1.23200E-1, +1.22500E-1, +1.21900E-1, +1.21300E-1, +1.20600E-1, +1.20000E-1, +1.19300E-1, +1.18600E-1, +1.18000E-1, +1.17300E-1, +1.16600E-1, +1.15900E-1, +1.15200E-1, +1.14500E-1, +1.13700E-1, +1.13000E-1, +1.12200E-1, +1.11500E-1, +1.10700E-1, +1.10000E-1, +1.09200E-1, +1.08400E-1, +1.07600E-1, +1.06800E-1, +1.06000E-1, +1.05200E-1, +1.04400E-1, +1.03500E-1, +1.02700E-1, +1.01900E-1, +1.01000E-1, +1.00100E-1, +9.93000E-2, +9.84000E-2, +9.75000E-2, +9.66000E-2, +9.57000E-2, +9.48000E-2, +9.39000E-2, +9.29000E-2, +9.20000E-2, +9.10000E-2, +9.01000E-2, +8.91000E-2, +8.82000E-2, +8.72000E-2, +8.62000E-2, +8.52000E-2, +8.42000E-2, +8.32000E-2, +8.22000E-2, +8.12000E-2, +8.01000E-2, +7.91000E-2, +7.80000E-2, +7.70000E-2, +7.59000E-2, +7.48000E-2, +7.38000E-2, +7.27000E-2, +7.16000E-2, +7.05000E-2])         # W/m-K
-        self.viscosity.data           = np.array([+3.59000E-1, +1.77000E-1, +9.59000E-2, +5.64000E-2, +3.55000E-2, +2.36000E-2, +1.65000E-2, +1.20000E-2, +9.07000E-3, +7.06000E-3, +5.63000E-3, +4.60000E-3, +3.82000E-3, +3.24000E-3, +2.78000E-3, +2.41000E-3, +2.12000E-3, +1.88000E-3, +1.69000E-3, +1.52000E-3, +1.38000E-3, +1.26000E-3, +1.16000E-3, +1.07000E-3, +9.88000E-4, +9.18000E-4, +8.56000E-4, +8.00000E-4, +7.50000E-4, +7.05000E-4, +6.64000E-4, +6.26000E-4, +5.92000E-4, +5.61000E-4, +5.31000E-4, +5.04000E-4, +4.79000E-4, +4.56000E-4, +4.35000E-4, +4.14000E-4, +3.95000E-4, +3.78000E-4, +3.61000E-4, +3.45000E-4, +3.30000E-4, +3.16000E-4, +3.03000E-4, +2.90000E-4, +2.78000E-4, +2.67000E-4, +2.57000E-4, +2.46000E-4, +2.37000E-4, +2.27000E-4, +2.19000E-4, +2.10000E-4, +2.02000E-4, +1.95000E-4, +1.87000E-4, +1.80000E-4, +1.74000E-4, +1.67000E-4, +1.61000E-4, +1.56000E-4])         # Pa-s 
-        self.saturation_pressure.data = np.array([+4.75000E-9, +2.07000E-8, +8.08000E-8, +2.81000E-7, +8.86000E-7, +2.56000E-6, +6.82000E-6, +1.70000E-5, +3.96000E-5, +8.75000E-5, +1.84000E-4, +3.68000E-4, +7.06000E-4, +1.30000E-3, +2.33000E-3, +4.02000E-3, +6.75000E-3, +1.10000E-2, +1.76000E-2, +2.73000E-2, +4.16000E-2, +6.21000E-2, +9.10000E-2, +1.31000E-1, +1.86000E-1, +2.59000E-1, +3.56000E-1, +4.84000E-1, +6.48000E-1, +8.59000E-1, +1.13000E+0, +1.46000E+0, +1.88000E+0, +2.39000E+0, +3.01000E+0, +3.77000E+0, +4.68000E+0, +5.76000E+0, +7.05000E+0, +8.57000E+0, +1.03000E+1, +1.24000E+1, +1.48000E+1, +1.76000E+1, +2.08000E+1, +2.44000E+1, +2.85000E+1, +3.32000E+1, +3.84000E+1, +4.43000E+1, +5.09000E+1, +5.83000E+1, +6.64000E+1, +7.55000E+1, +8.55000E+1, +9.65000E+1, +1.09000E+2, +1.22000E+2, +1.36000E+2, +1.52000E+2, +1.69000E+2, +1.88000E+2, +2.08000E+2, +2.29000E+2])*1000.   # Pa 
+        self.viscosity.data           = np.array([+3.59000E-1, +1.77000E-1, +9.59000E-2, +5.64000E-2, +3.55000E-2, +2.36000E-2, +1.65000E-2, +1.20000E-2, +9.07000E-3, +7.06000E-3, +5.63000E-3, +4.60000E-3, +3.82000E-3, +3.24000E-3, +2.78000E-3, +2.41000E-3, +2.12000E-3, +1.88000E-3, +1.69000E-3, +1.52000E-3, +1.38000E-3, +1.26000E-3, +1.16000E-3, +1.07000E-3, +9.88000E-4, +9.18000E-4, +8.56000E-4, +8.00000E-4, +7.50000E-4, +7.05000E-4, +6.64000E-4, +6.26000E-4, +5.92000E-4, +5.61000E-4, +5.31000E-4, +5.04000E-4, +4.79000E-4, +4.56000E-4, +4.35000E-4, +4.14000E-4, +3.95000E-4, +3.78000E-4, +3.61000E-4, +3.45000E-4, +3.30000E-4, +3.16000E-4, +3.03000E-4, +2.90000E-4, +2.78000E-4, +2.67000E-4, +2.57000E-4, +2.46000E-4, +2.37000E-4, +2.27000E-4, +2.19000E-4, +2.10000E-4, +2.02000E-4, +1.95000E-4, +1.87000E-4, +1.80000E-4, +1.74000E-4, +1.67000E-4, +1.61000E-4, +1.56000E-4])         # Pa-s
+        self.saturation_pressure.data = np.array([+4.75000E-9, +2.07000E-8, +8.08000E-8, +2.81000E-7, +8.86000E-7, +2.56000E-6, +6.82000E-6, +1.70000E-5, +3.96000E-5, +8.75000E-5, +1.84000E-4, +3.68000E-4, +7.06000E-4, +1.30000E-3, +2.33000E-3, +4.02000E-3, +6.75000E-3, +1.10000E-2, +1.76000E-2, +2.73000E-2, +4.16000E-2, +6.21000E-2, +9.10000E-2, +1.31000E-1, +1.86000E-1, +2.59000E-1, +3.56000E-1, +4.84000E-1, +6.48000E-1, +8.59000E-1, +1.13000E+0, +1.46000E+0, +1.88000E+0, +2.39000E+0, +3.01000E+0, +3.77000E+0, +4.68000E+0, +5.76000E+0, +7.05000E+0, +8.57000E+0, +1.03000E+1, +1.24000E+1, +1.48000E+1, +1.76000E+1, +2.08000E+1, +2.44000E+1, +2.85000E+1, +3.32000E+1, +3.84000E+1, +4.43000E+1, +5.09000E+1, +5.83000E+1, +6.64000E+1, +7.55000E+1, +8.55000E+1, +9.65000E+1, +1.09000E+2, +1.22000E+2, +1.36000E+2, +1.52000E+2, +1.69000E+2, +1.88000E+2, +2.08000E+2, +2.29000E+2])*1000.   # Pa
         self.Tmin        = np.min(self.temperature.data)
         self.Tmax        = np.max(self.temperature.data)
         self.TminPsat    = self.Tmin
-        self.name        = "TD12" 
+        self.name        = "TD12"
         self.description = "TherminolD12"
-        self.reference   = "Therminol Heat Transfer Reference Disk"
+        self.reference   = "Therminol2014"
         self.reshapeAll()
 
 class TherminolVP1(PureData):
-    """ 
+    """
     Heat transfer fluid Therminol VP-1 by Solutia
     """
     def __init__(self):
-        PureData.__init__(self) 
+        PureData.__init__(self)
         self.density.source           = self.density.SOURCE_DATA
         self.specific_heat.source     = self.specific_heat.SOURCE_DATA
         self.conductivity.source      = self.conductivity.SOURCE_DATA
@@ -42,23 +42,23 @@ class TherminolVP1(PureData):
         self.density.data             = np.array([+1.07000E+3, +1.07000E+3, +1.06000E+3, +1.06000E+3, +1.05000E+3, +1.05000E+3, +1.05000E+3, +1.04000E+3, +1.04000E+3, +1.03000E+3, +1.03000E+3, +1.03000E+3, +1.02000E+3, +1.02000E+3, +1.01000E+3, +1.01000E+3, +1.01000E+3, +1.00000E+3, +9.97000E+2, +9.93000E+2, +9.88000E+2, +9.84000E+2, +9.80000E+2, +9.76000E+2, +9.72000E+2, +9.67000E+2, +9.63000E+2, +9.59000E+2, +9.55000E+2, +9.50000E+2, +9.46000E+2, +9.42000E+2, +9.37000E+2, +9.33000E+2, +9.29000E+2, +9.24000E+2, +9.20000E+2, +9.15000E+2, +9.11000E+2, +9.06000E+2, +9.02000E+2, +8.98000E+2, +8.93000E+2, +8.89000E+2, +8.84000E+2, +8.79000E+2, +8.75000E+2, +8.70000E+2, +8.65000E+2, +8.60000E+2, +8.56000E+2, +8.51000E+2, +8.46000E+2, +8.41000E+2, +8.36000E+2, +8.31000E+2, +8.25000E+2, +8.20000E+2, +8.15000E+2, +8.10000E+2, +8.04000E+2, +7.99000E+2, +7.93000E+2, +7.88000E+2, +7.82000E+2, +7.76000E+2, +7.70000E+2, +7.65000E+2, +7.59000E+2, +7.52000E+2, +7.46000E+2, +7.40000E+2, +7.33000E+2, +7.27000E+2, +7.20000E+2, +7.13000E+2, +7.06000E+2, +6.99000E+2])         # kg/m3
         self.specific_heat.data       = np.array([+1.52300E+0, +1.53700E+0, +1.55200E+0, +1.56600E+0, +1.58100E+0, +1.59600E+0, +1.61000E+0, +1.62400E+0, +1.63900E+0, +1.65300E+0, +1.66800E+0, +1.68200E+0, +1.69600E+0, +1.71000E+0, +1.72400E+0, +1.73900E+0, +1.75300E+0, +1.76700E+0, +1.78100E+0, +1.79500E+0, +1.80900E+0, +1.82200E+0, +1.83600E+0, +1.85000E+0, +1.86400E+0, +1.87800E+0, +1.89100E+0, +1.90500E+0, +1.91900E+0, +1.93200E+0, +1.94600E+0, +1.95900E+0, +1.97300E+0, +1.98600E+0, +2.00000E+0, +2.01300E+0, +2.02700E+0, +2.04000E+0, +2.05400E+0, +2.06700E+0, +2.08000E+0, +2.09300E+0, +2.10700E+0, +2.12000E+0, +2.13300E+0, +2.14700E+0, +2.16000E+0, +2.17300E+0, +2.18600E+0, +2.19900E+0, +2.21300E+0, +2.22600E+0, +2.23900E+0, +2.25200E+0, +2.26600E+0, +2.27900E+0, +2.29300E+0, +2.30600E+0, +2.31900E+0, +2.33300E+0, +2.34700E+0, +2.36000E+0, +2.37400E+0, +2.38800E+0, +2.40200E+0, +2.41600E+0, +2.43100E+0, +2.44600E+0, +2.46000E+0, +2.47600E+0, +2.49100E+0, +2.50700E+0, +2.52300E+0, +2.54000E+0, +2.55800E+0, +2.57600E+0, +2.59500E+0, +2.61500E+0])*1000.   # J/kg-K
         self.conductivity.data        = np.array([+1.37000E-1, +1.36600E-1, +1.36100E-1, +1.35600E-1, +1.35200E-1, +1.34700E-1, +1.34200E-1, +1.33600E-1, +1.33100E-1, +1.32600E-1, +1.32000E-1, +1.31500E-1, +1.30900E-1, +1.30400E-1, +1.29800E-1, +1.29200E-1, +1.28600E-1, +1.28000E-1, +1.27400E-1, +1.26800E-1, +1.26200E-1, +1.25600E-1, +1.24900E-1, +1.24300E-1, +1.23600E-1, +1.22900E-1, +1.22300E-1, +1.21600E-1, +1.20900E-1, +1.20200E-1, +1.19500E-1, +1.18700E-1, +1.18000E-1, +1.17300E-1, +1.16500E-1, +1.15800E-1, +1.15000E-1, +1.14200E-1, +1.13500E-1, +1.12700E-1, +1.11900E-1, +1.11100E-1, +1.10300E-1, +1.09400E-1, +1.08600E-1, +1.07800E-1, +1.06900E-1, +1.06000E-1, +1.05200E-1, +1.04300E-1, +1.03400E-1, +1.02500E-1, +1.01600E-1, +1.00700E-1, +9.98000E-2, +9.89000E-2, +9.79000E-2, +9.70000E-2, +9.60000E-2, +9.51000E-2, +9.41000E-2, +9.31000E-2, +9.21000E-2, +9.11000E-2, +9.01000E-2, +8.91000E-2, +8.81000E-2, +8.71000E-2, +8.60000E-2, +8.50000E-2, +8.39000E-2, +8.29000E-2, +8.18000E-2, +8.07000E-2, +7.96000E-2, +7.85000E-2, +7.74000E-2, +7.63000E-2])         # W/m-K
-        self.viscosity.data           = np.array([+5.48000E-3, +4.68000E-3, +4.05000E-3, +3.54000E-3, +3.12000E-3, +2.78000E-3, +2.49000E-3, +2.24000E-3, +2.04000E-3, +1.86000E-3, +1.70000E-3, +1.57000E-3, +1.45000E-3, +1.34000E-3, +1.25000E-3, +1.16000E-3, +1.09000E-3, +1.02000E-3, +9.62000E-4, +9.06000E-4, +8.56000E-4, +8.10000E-4, +7.68000E-4, +7.29000E-4, +6.93000E-4, +6.60000E-4, +6.30000E-4, +6.01000E-4, +5.75000E-4, +5.51000E-4, +5.28000E-4, +5.06000E-4, +4.86000E-4, +4.67000E-4, +4.50000E-4, +4.33000E-4, +4.18000E-4, +4.03000E-4, +3.89000E-4, +3.76000E-4, +3.64000E-4, +3.52000E-4, +3.41000E-4, +3.30000E-4, +3.20000E-4, +3.10000E-4, +3.01000E-4, +2.93000E-4, +2.84000E-4, +2.77000E-4, +2.69000E-4, +2.62000E-4, +2.55000E-4, +2.48000E-4, +2.42000E-4, +2.36000E-4, +2.30000E-4, +2.25000E-4, +2.19000E-4, +2.14000E-4, +2.09000E-4, +2.04000E-4, +2.00000E-4, +1.96000E-4, +1.91000E-4, +1.87000E-4, +1.83000E-4, +1.80000E-4, +1.76000E-4, +1.72000E-4, +1.69000E-4, +1.66000E-4, +1.62000E-4, +1.59000E-4, +1.56000E-4, +1.53000E-4, +1.51000E-4, +1.48000E-4])         # Pa-s 
-        self.saturation_pressure.data = np.array([+5.76000E-4, +9.86000E-4, +1.65000E-3, +2.68000E-3, +4.27000E-3, +6.67000E-3, +1.02000E-2, +1.53000E-2, +2.26000E-2, +3.29000E-2, +4.71000E-2, +6.65000E-2, +9.26000E-2, +1.27000E-1, +1.73000E-1, +2.32000E-1, +3.09000E-1, +4.07000E-1, +5.30000E-1, +6.85000E-1, +8.77000E-1, +1.11000E+0, +1.40000E+0, +1.76000E+0, +2.18000E+0, +2.70000E+0, +3.31000E+0, +4.03000E+0, +4.88000E+0, +5.88000E+0, +7.05000E+0, +8.40000E+0, +9.96000E+0, +1.18000E+1, +1.38000E+1, +1.62000E+1, +1.89000E+1, +2.19000E+1, +2.53000E+1, +2.92000E+1, +3.35000E+1, +3.84000E+1, +4.37000E+1, +4.97000E+1, +5.63000E+1, +6.37000E+1, +7.17000E+1, +8.06000E+1, +9.03000E+1, +1.01000E+2, +1.13000E+2, +1.25000E+2, +1.39000E+2, +1.54000E+2, +1.70000E+2, +1.87000E+2, +2.06000E+2, +2.26000E+2, +2.48000E+2, +2.71000E+2, +2.96000E+2, +3.23000E+2, +3.51000E+2, +3.82000E+2, +4.14000E+2, +4.48000E+2, +4.85000E+2, +5.24000E+2, +5.64000E+2, +6.08000E+2, +6.54000E+2, +7.02000E+2, +7.53000E+2, +8.06000E+2, +8.62000E+2, +9.21000E+2, +9.83000E+2, +1.05000E+3])*1000.   # Pa 
+        self.viscosity.data           = np.array([+5.48000E-3, +4.68000E-3, +4.05000E-3, +3.54000E-3, +3.12000E-3, +2.78000E-3, +2.49000E-3, +2.24000E-3, +2.04000E-3, +1.86000E-3, +1.70000E-3, +1.57000E-3, +1.45000E-3, +1.34000E-3, +1.25000E-3, +1.16000E-3, +1.09000E-3, +1.02000E-3, +9.62000E-4, +9.06000E-4, +8.56000E-4, +8.10000E-4, +7.68000E-4, +7.29000E-4, +6.93000E-4, +6.60000E-4, +6.30000E-4, +6.01000E-4, +5.75000E-4, +5.51000E-4, +5.28000E-4, +5.06000E-4, +4.86000E-4, +4.67000E-4, +4.50000E-4, +4.33000E-4, +4.18000E-4, +4.03000E-4, +3.89000E-4, +3.76000E-4, +3.64000E-4, +3.52000E-4, +3.41000E-4, +3.30000E-4, +3.20000E-4, +3.10000E-4, +3.01000E-4, +2.93000E-4, +2.84000E-4, +2.77000E-4, +2.69000E-4, +2.62000E-4, +2.55000E-4, +2.48000E-4, +2.42000E-4, +2.36000E-4, +2.30000E-4, +2.25000E-4, +2.19000E-4, +2.14000E-4, +2.09000E-4, +2.04000E-4, +2.00000E-4, +1.96000E-4, +1.91000E-4, +1.87000E-4, +1.83000E-4, +1.80000E-4, +1.76000E-4, +1.72000E-4, +1.69000E-4, +1.66000E-4, +1.62000E-4, +1.59000E-4, +1.56000E-4, +1.53000E-4, +1.51000E-4, +1.48000E-4])         # Pa-s
+        self.saturation_pressure.data = np.array([+5.76000E-4, +9.86000E-4, +1.65000E-3, +2.68000E-3, +4.27000E-3, +6.67000E-3, +1.02000E-2, +1.53000E-2, +2.26000E-2, +3.29000E-2, +4.71000E-2, +6.65000E-2, +9.26000E-2, +1.27000E-1, +1.73000E-1, +2.32000E-1, +3.09000E-1, +4.07000E-1, +5.30000E-1, +6.85000E-1, +8.77000E-1, +1.11000E+0, +1.40000E+0, +1.76000E+0, +2.18000E+0, +2.70000E+0, +3.31000E+0, +4.03000E+0, +4.88000E+0, +5.88000E+0, +7.05000E+0, +8.40000E+0, +9.96000E+0, +1.18000E+1, +1.38000E+1, +1.62000E+1, +1.89000E+1, +2.19000E+1, +2.53000E+1, +2.92000E+1, +3.35000E+1, +3.84000E+1, +4.37000E+1, +4.97000E+1, +5.63000E+1, +6.37000E+1, +7.17000E+1, +8.06000E+1, +9.03000E+1, +1.01000E+2, +1.13000E+2, +1.25000E+2, +1.39000E+2, +1.54000E+2, +1.70000E+2, +1.87000E+2, +2.06000E+2, +2.26000E+2, +2.48000E+2, +2.71000E+2, +2.96000E+2, +3.23000E+2, +3.51000E+2, +3.82000E+2, +4.14000E+2, +4.48000E+2, +4.85000E+2, +5.24000E+2, +5.64000E+2, +6.08000E+2, +6.54000E+2, +7.02000E+2, +7.53000E+2, +8.06000E+2, +8.62000E+2, +9.21000E+2, +9.83000E+2, +1.05000E+3])*1000.   # Pa
         self.Tmin        = np.min(self.temperature.data)
         self.Tmax        = np.max(self.temperature.data)
-        self.TminPsat    = self.Tmin 
-        self.name        = "TVP1" 
+        self.TminPsat    = self.Tmin
+        self.name        = "TVP1"
         self.description = "TherminolVP1"
-        self.reference   = "Therminol Heat Transfer Reference Disk"
+        self.reference   = "Therminol2014"
         self.reshapeAll()
-    
-    
+
+
 class Therminol66(PureData):
-    """ 
+    """
     Heat transfer fluid Therminol 66 by Solutia
     """
     def __init__(self):
-        PureData.__init__(self) 
+        PureData.__init__(self)
         self.density.source           = self.density.SOURCE_DATA
         self.specific_heat.source     = self.specific_heat.SOURCE_DATA
         self.conductivity.source      = self.conductivity.SOURCE_DATA
@@ -72,19 +72,19 @@ class Therminol66(PureData):
         self.saturation_pressure.data = np.array([    np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN, 1.0000E+01, 2.0000E+01, 3.0000E+01, 5.0000E+01, 8.0000E+01, 1.2000E+02, 1.8000E+02, 2.7000E+02, 4.0000E+02, 5.8000E+02, 8.3000E+02, 1.1700E+03, 1.6200E+03, 2.2300E+03, 3.0200E+03, 4.0600E+03, 5.3900E+03, 7.1000E+03, 9.2500E+03, 1.1950E+04, 1.5310E+04, 1.9460E+04, 2.4550E+04, 3.0730E+04, 3.8220E+04, 4.7200E+04, 5.7940E+04, 7.0680E+04, 8.5740E+04, 1.0342E+05, 1.2409E+05, 1.4813E+05])
         self.Tmin        = np.min(self.temperature.data)
         self.Tmax        = np.max(self.temperature.data)
-        self.TminPsat    = 70+273.15 
-        self.name        = "T66" 
+        self.TminPsat    = 70+273.15
+        self.name        = "T66"
         self.description = "Therminol66"
-        self.reference   = "Therminol Heat Transfer Reference Disk"
-        self.reshapeAll()   
-    
-    
+        self.reference   = "Therminol2014"
+        self.reshapeAll()
+
+
 class Therminol72(PureData):
-    """ 
+    """
     Heat transfer fluid Therminol 72 by Solutia
     """
     def __init__(self):
-        PureData.__init__(self) 
+        PureData.__init__(self)
         self.density.source           = self.density.SOURCE_DATA
         self.specific_heat.source     = self.specific_heat.SOURCE_DATA
         self.conductivity.source      = self.conductivity.SOURCE_DATA
@@ -94,24 +94,24 @@ class Therminol72(PureData):
         self.density.data             = np.array([+1.11000E+3, +1.10000E+3, +1.10000E+3, +1.09000E+3, +1.09000E+3, +1.08000E+3, +1.08000E+3, +1.07000E+3, +1.07000E+3, +1.07000E+3, +1.06000E+3, +1.06000E+3, +1.05000E+3, +1.05000E+3, +1.04000E+3, +1.04000E+3, +1.03000E+3, +1.03000E+3, +1.02000E+3, +1.02000E+3, +1.02000E+3, +1.01000E+3, +1.01000E+3, +1.00000E+3, +9.97000E+2, +9.93000E+2, +9.88000E+2, +9.84000E+2, +9.79000E+2, +9.74000E+2, +9.70000E+2, +9.65000E+2, +9.61000E+2, +9.56000E+2, +9.52000E+2, +9.47000E+2, +9.43000E+2, +9.38000E+2, +9.34000E+2, +9.29000E+2, +9.25000E+2, +9.20000E+2, +9.16000E+2, +9.11000E+2, +9.06000E+2, +9.02000E+2, +8.98000E+2, +8.93000E+2, +8.89000E+2, +8.84000E+2, +8.80000E+2, +8.75000E+2, +8.71000E+2, +8.66000E+2, +8.62000E+2, +8.57000E+2, +8.53000E+2, +8.48000E+2, +8.44000E+2, +8.39000E+2, +8.34000E+2, +8.30000E+2, +8.25000E+2, +8.21000E+2, +8.16000E+2, +8.12000E+2, +8.07000E+2, +8.03000E+2, +7.98000E+2, +7.94000E+2, +7.89000E+2, +7.85000E+2, +7.80000E+2, +7.76000E+2, +7.71000E+2, +7.66000E+2, +7.62000E+2, +7.57000E+2, +7.53000E+2])         # kg/m3
         self.specific_heat.data       = np.array([+1.47100E+0, +1.48400E+0, +1.49800E+0, +1.51200E+0, +1.52500E+0, +1.53900E+0, +1.55200E+0, +1.56600E+0, +1.57900E+0, +1.59300E+0, +1.60600E+0, +1.62000E+0, +1.63400E+0, +1.64700E+0, +1.66100E+0, +1.67400E+0, +1.68800E+0, +1.70100E+0, +1.71500E+0, +1.72800E+0, +1.74200E+0, +1.75500E+0, +1.76900E+0, +1.78300E+0, +1.79600E+0, +1.81000E+0, +1.82300E+0, +1.83700E+0, +1.85000E+0, +1.86400E+0, +1.87700E+0, +1.89100E+0, +1.90500E+0, +1.91800E+0, +1.93200E+0, +1.94500E+0, +1.95900E+0, +1.97200E+0, +1.98600E+0, +1.99900E+0, +2.01300E+0, +2.02600E+0, +2.04000E+0, +2.05400E+0, +2.06700E+0, +2.08100E+0, +2.09400E+0, +2.10800E+0, +2.12100E+0, +2.13500E+0, +2.14800E+0, +2.16200E+0, +2.17600E+0, +2.18900E+0, +2.20300E+0, +2.21600E+0, +2.23000E+0, +2.24300E+0, +2.25700E+0, +2.27000E+0, +2.28400E+0, +2.29700E+0, +2.31100E+0, +2.32500E+0, +2.33800E+0, +2.35200E+0, +2.36500E+0, +2.37900E+0, +2.39200E+0, +2.40600E+0, +2.41900E+0, +2.43300E+0, +2.44600E+0, +2.46000E+0, +2.47400E+0, +2.48700E+0, +2.50100E+0, +2.51400E+0, +2.52800E+0])*1000.   # J/kg-K
         self.conductivity.data        = np.array([+1.43200E-1, +1.42600E-1, +1.42000E-1, +1.41400E-1, +1.40800E-1, +1.40200E-1, +1.39600E-1, +1.39000E-1, +1.38400E-1, +1.37800E-1, +1.37100E-1, +1.36500E-1, +1.35900E-1, +1.35300E-1, +1.34700E-1, +1.34100E-1, +1.33500E-1, +1.32900E-1, +1.32300E-1, +1.31700E-1, +1.31100E-1, +1.30500E-1, +1.29900E-1, +1.29300E-1, +1.28700E-1, +1.28000E-1, +1.27400E-1, +1.26800E-1, +1.26200E-1, +1.25600E-1, +1.25000E-1, +1.24400E-1, +1.23800E-1, +1.23200E-1, +1.22600E-1, +1.22000E-1, +1.21400E-1, +1.20800E-1, +1.20200E-1, +1.19600E-1, +1.18900E-1, +1.18300E-1, +1.17700E-1, +1.17100E-1, +1.16500E-1, +1.15900E-1, +1.15300E-1, +1.14700E-1, +1.14100E-1, +1.13500E-1, +1.12900E-1, +1.12300E-1, +1.11700E-1, +1.11100E-1, +1.10500E-1, +1.09800E-1, +1.09200E-1, +1.08600E-1, +1.08000E-1, +1.07400E-1, +1.06800E-1, +1.06200E-1, +1.05600E-1, +1.05000E-1, +1.04400E-1, +1.03800E-1, +1.03200E-1, +1.02600E-1, +1.02000E-1, +1.01400E-1, +1.00700E-1, +1.00100E-1, +9.95000E-2, +9.89000E-2, +9.83000E-2, +9.77000E-2, +9.71000E-2, +9.65000E-2, +9.59000E-2])         # W/m-K
-        self.viscosity.data           = np.array([+3.83000E-1, +1.19000E-1, +5.92000E-2, +3.60000E-2, +2.44000E-2, +1.77000E-2, +1.35000E-2, +1.07000E-2, +8.68000E-3, +7.21000E-3, +6.09000E-3, +5.21000E-3, +4.52000E-3, +3.96000E-3, +3.50000E-3, +3.12000E-3, +2.79000E-3, +2.52000E-3, +2.28000E-3, +2.08000E-3, +1.90000E-3, +1.75000E-3, +1.61000E-3, +1.49000E-3, +1.38000E-3, +1.29000E-3, +1.20000E-3, +1.12000E-3, +1.05000E-3, +9.86000E-4, +9.28000E-4, +8.74000E-4, +8.25000E-4, +7.79000E-4, +7.38000E-4, +6.99000E-4, +6.64000E-4, +6.31000E-4, +6.00000E-4, +5.72000E-4, +5.45000E-4, +5.20000E-4, +4.97000E-4, +4.75000E-4, +4.55000E-4, +4.36000E-4, +4.18000E-4, +4.01000E-4, +3.85000E-4, +3.70000E-4, +3.55000E-4, +3.42000E-4, +3.29000E-4, +3.17000E-4, +3.05000E-4, +2.95000E-4, +2.84000E-4, +2.74000E-4, +2.65000E-4, +2.56000E-4, +2.47000E-4, +2.39000E-4, +2.31000E-4, +2.24000E-4, +2.17000E-4, +2.10000E-4, +2.03000E-4, +1.97000E-4, +1.91000E-4, +1.85000E-4, +1.80000E-4, +1.75000E-4, +1.69000E-4, +1.65000E-4, +1.60000E-4, +1.55000E-4, +1.51000E-4, +1.47000E-4, +1.43000E-4])         # Pa-s 
-        self.saturation_pressure.data = np.array([+9.60000E-1, +1.05000E+0, +1.14000E+0, +1.24000E+0, +1.35000E+0, +1.47000E+0, +1.60000E+0, +1.74000E+0, +1.89000E+0, +2.06000E+0, +2.24000E+0, +2.44000E+0, +2.65000E+0, +2.88000E+0, +3.14000E+0, +3.41000E+0, +3.71000E+0, +4.03000E+0, +4.39000E+0, +4.77000E+0, +5.18000E+0, +5.63000E+0, +6.12000E+0, +6.66000E+0, +7.23000E+0, +7.86000E+0, +8.54000E+0, +9.27000E+0, +1.01000E+1, +1.10000E+1, +1.19000E+1, +1.29000E+1, +1.40000E+1, +1.52000E+1, +1.65000E+1, +1.80000E+1, +1.95000E+1, +2.12000E+1, +2.30000E+1, +2.49000E+1, +2.71000E+1, +2.94000E+1, +3.19000E+1, +3.46000E+1, +3.75000E+1, +4.07000E+1, +4.42000E+1, +4.79000E+1, +5.20000E+1, +5.64000E+1, +6.11000E+1, +6.63000E+1, +7.19000E+1, +7.79000E+1, +8.45000E+1, +9.15000E+1, +9.92000E+1, +1.08000E+2, +1.17000E+2, +1.26000E+2, +1.37000E+2, +1.48000E+2, +1.61000E+2, +1.74000E+2, +1.89000E+2, +2.04000E+2, +2.21000E+2, +2.40000E+2, +2.60000E+2, +2.81000E+2, +3.04000E+2, +3.30000E+2, +3.57000E+2, +3.86000E+2, +4.18000E+2, +4.53000E+2, +4.90000E+2, +5.30000E+2, +5.74000E+2])*1000.   # Pa 
+        self.viscosity.data           = np.array([+3.83000E-1, +1.19000E-1, +5.92000E-2, +3.60000E-2, +2.44000E-2, +1.77000E-2, +1.35000E-2, +1.07000E-2, +8.68000E-3, +7.21000E-3, +6.09000E-3, +5.21000E-3, +4.52000E-3, +3.96000E-3, +3.50000E-3, +3.12000E-3, +2.79000E-3, +2.52000E-3, +2.28000E-3, +2.08000E-3, +1.90000E-3, +1.75000E-3, +1.61000E-3, +1.49000E-3, +1.38000E-3, +1.29000E-3, +1.20000E-3, +1.12000E-3, +1.05000E-3, +9.86000E-4, +9.28000E-4, +8.74000E-4, +8.25000E-4, +7.79000E-4, +7.38000E-4, +6.99000E-4, +6.64000E-4, +6.31000E-4, +6.00000E-4, +5.72000E-4, +5.45000E-4, +5.20000E-4, +4.97000E-4, +4.75000E-4, +4.55000E-4, +4.36000E-4, +4.18000E-4, +4.01000E-4, +3.85000E-4, +3.70000E-4, +3.55000E-4, +3.42000E-4, +3.29000E-4, +3.17000E-4, +3.05000E-4, +2.95000E-4, +2.84000E-4, +2.74000E-4, +2.65000E-4, +2.56000E-4, +2.47000E-4, +2.39000E-4, +2.31000E-4, +2.24000E-4, +2.17000E-4, +2.10000E-4, +2.03000E-4, +1.97000E-4, +1.91000E-4, +1.85000E-4, +1.80000E-4, +1.75000E-4, +1.69000E-4, +1.65000E-4, +1.60000E-4, +1.55000E-4, +1.51000E-4, +1.47000E-4, +1.43000E-4])         # Pa-s
+        self.saturation_pressure.data = np.array([+9.60000E-1, +1.05000E+0, +1.14000E+0, +1.24000E+0, +1.35000E+0, +1.47000E+0, +1.60000E+0, +1.74000E+0, +1.89000E+0, +2.06000E+0, +2.24000E+0, +2.44000E+0, +2.65000E+0, +2.88000E+0, +3.14000E+0, +3.41000E+0, +3.71000E+0, +4.03000E+0, +4.39000E+0, +4.77000E+0, +5.18000E+0, +5.63000E+0, +6.12000E+0, +6.66000E+0, +7.23000E+0, +7.86000E+0, +8.54000E+0, +9.27000E+0, +1.01000E+1, +1.10000E+1, +1.19000E+1, +1.29000E+1, +1.40000E+1, +1.52000E+1, +1.65000E+1, +1.80000E+1, +1.95000E+1, +2.12000E+1, +2.30000E+1, +2.49000E+1, +2.71000E+1, +2.94000E+1, +3.19000E+1, +3.46000E+1, +3.75000E+1, +4.07000E+1, +4.42000E+1, +4.79000E+1, +5.20000E+1, +5.64000E+1, +6.11000E+1, +6.63000E+1, +7.19000E+1, +7.79000E+1, +8.45000E+1, +9.15000E+1, +9.92000E+1, +1.08000E+2, +1.17000E+2, +1.26000E+2, +1.37000E+2, +1.48000E+2, +1.61000E+2, +1.74000E+2, +1.89000E+2, +2.04000E+2, +2.21000E+2, +2.40000E+2, +2.60000E+2, +2.81000E+2, +3.04000E+2, +3.30000E+2, +3.57000E+2, +3.86000E+2, +4.18000E+2, +4.53000E+2, +4.90000E+2, +5.30000E+2, +5.74000E+2])*1000.   # Pa
         self.Tmin        = np.min(self.temperature.data)
         self.Tmax        = np.max(self.temperature.data)
-        self.TminPsat    = self.Tmin 
-        self.name        = "T72" 
+        self.TminPsat    = self.Tmin
+        self.name        = "T72"
         self.description = "Therminol72"
-        self.reference   = "Therminol Heat Transfer Reference Disk"
+        self.reference   = "Therminol2014"
         self.reshapeAll()
-    
-    
-    
+
+
+
 class DowthermJ(PureData):
-    """ 
+    """
     Heat transfer fluid Dowtherm J by Dow Chemicals
     """
     def __init__(self):
-        PureData.__init__(self) 
+        PureData.__init__(self)
         self.density.source           = self.density.SOURCE_DATA
         self.specific_heat.source     = self.specific_heat.SOURCE_DATA
         self.conductivity.source      = self.conductivity.SOURCE_DATA
@@ -122,17 +122,17 @@ class DowthermJ(PureData):
         self.specific_heat.data       = np.array([+1.58400E+0, +1.59400E+0, +1.61600E+0, +1.63900E+0, +1.66300E+0, +1.68800E+0, +1.71400E+0, +1.74100E+0, +1.76900E+0, +1.79800E+0, +1.82800E+0, +1.85900E+0, +1.89000E+0, +1.92300E+0, +1.95500E+0, +1.98900E+0, +2.02300E+0, +2.05800E+0, +2.09300E+0, +2.12900E+0, +2.16500E+0, +2.20200E+0, +2.23900E+0, +2.27700E+0, +2.31500E+0, +2.35300E+0, +2.39200E+0, +2.39700E+0, +2.43200E+0, +2.47200E+0, +2.51200E+0, +2.55300E+0, +2.59400E+0, +2.63600E+0, +2.68000E+0, +2.72400E+0, +2.76900E+0, +2.81600E+0, +2.86600E+0, +2.91900E+0, +2.97600E+0, +3.04000E+0, +3.11500E+0, +3.20800E+0, +3.26500E+0])*1000.   # J/kg-K
         self.conductivity.data        = np.array([+1.48500E-1, +1.47500E-1, +1.45300E-1, +1.43200E-1, +1.41100E-1, +1.39000E-1, +1.36800E-1, +1.34700E-1, +1.32600E-1, +1.30500E-1, +1.28400E-1, +1.26200E-1, +1.24100E-1, +1.22000E-1, +1.19900E-1, +1.17700E-1, +1.15600E-1, +1.13500E-1, +1.11400E-1, +1.09300E-1, +1.07100E-1, +1.05000E-1, +1.02900E-1, +1.00800E-1, +9.87000E-2, +9.65000E-2, +9.44000E-2, +9.41000E-2, +9.23000E-2, +9.02000E-2, +8.80000E-2, +8.59000E-2, +8.38000E-2, +8.17000E-2, +7.96000E-2, +7.74000E-2, +7.53000E-2, +7.32000E-2, +7.11000E-2, +6.90000E-2, +6.68000E-2, +6.47000E-2, +6.26000E-2, +6.05000E-2, +5.94000E-2])         # W/m-K
         self.viscosity.data           = np.array([+8.43000E+0, +7.11000E+0, +5.12000E+0, +3.78000E+0, +2.88000E+0, +2.25000E+0, +1.80000E+0, +1.48000E+0, +1.23000E+0, +1.05000E+0, +9.10000E-1, +7.90000E-1, +7.00000E-1, +6.30000E-1, +5.60000E-1, +5.10000E-1, +4.70000E-1, +4.30000E-1, +4.00000E-1, +3.70000E-1, +3.50000E-1, +3.30000E-1, +3.10000E-1, +2.90000E-1, +2.80000E-1, +2.70000E-1, +2.50000E-1, +2.50000E-1, +2.40000E-1, +2.30000E-1, +2.30000E-1, +2.20000E-1, +2.10000E-1, +2.00000E-1, +2.00000E-1, +1.90000E-1, +1.80000E-1, +1.80000E-1, +1.70000E-1, +1.70000E-1, +1.70000E-1, +1.60000E-1, +1.60000E-1, +1.60000E-1, +1.50000E-1])/1000.   # Pa-s
-        self.saturation_pressure.data = np.array([     np.NAN,      np.NAN,      np.NAN,      np.NAN,      np.NAN,      np.NAN,      np.NAN,      np.NAN,      np.NAN,      np.NAN,      np.NAN,      np.NAN,      np.NAN, +5.00000E-3, +1.00000E-2, +2.00000E-2, +3.00000E-2, +5.00000E-2, +8.00000E-2, +1.10000E-1, +1.60000E-1, +2.30000E-1, +3.20000E-1, +4.30000E-1, +5.80000E-1, +7.60000E-1, +9.80000E-1, +1.01000E+0, +1.25000E+0, +1.58000E+0, +1.97000E+0, +2.43000E+0, +2.96000E+0, +3.59000E+0, +4.30000E+0, +5.13000E+0, +6.06000E+0, +7.12000E+0, +8.31000E+0, +9.64000E+0, +1.11300E+1, +1.27900E+1, +1.46400E+1, +1.66900E+1, +1.78000E+1])*1e5     # Pa 
+        self.saturation_pressure.data = np.array([     np.NAN,      np.NAN,      np.NAN,      np.NAN,      np.NAN,      np.NAN,      np.NAN,      np.NAN,      np.NAN,      np.NAN,      np.NAN,      np.NAN,      np.NAN, +5.00000E-3, +1.00000E-2, +2.00000E-2, +3.00000E-2, +5.00000E-2, +8.00000E-2, +1.10000E-1, +1.60000E-1, +2.30000E-1, +3.20000E-1, +4.30000E-1, +5.80000E-1, +7.60000E-1, +9.80000E-1, +1.01000E+0, +1.25000E+0, +1.58000E+0, +1.97000E+0, +2.43000E+0, +2.96000E+0, +3.59000E+0, +4.30000E+0, +5.13000E+0, +6.06000E+0, +7.12000E+0, +8.31000E+0, +9.64000E+0, +1.11300E+1, +1.27900E+1, +1.46400E+1, +1.66900E+1, +1.78000E+1])*1e5     # Pa
         self.Tmin        = np.min(self.temperature.data)
         self.Tmax        = np.max(self.temperature.data)
-        self.TminPsat    = 50 + 273.15 
-        self.name        = "DowJ" 
+        self.TminPsat    = 50 + 273.15
+        self.name        = "DowJ"
         self.description = "DowthermJ"
-        self.reference   = "Dow Chemicals data sheet"
+        self.reference   = "Dow1997"
         self.reshapeAll()
-    
+
 class DowthermQ(PureData):
-    """ 
+    """
     Heat transfer fluid Dowtherm Q by Dow Chemicals
     """
     def __init__(self):
@@ -141,28 +141,28 @@ class DowthermQ(PureData):
         self.specific_heat.source     = self.specific_heat.SOURCE_DATA
         self.conductivity.source      = self.conductivity.SOURCE_DATA
         self.viscosity.source         = self.viscosity.SOURCE_DATA
-        self.saturation_pressure.source = self.saturation_pressure.SOURCE_DATA 
+        self.saturation_pressure.source = self.saturation_pressure.SOURCE_DATA
         self.temperature.data         = np.array([-3.50000E+1, -3.00000E+1, -2.00000E+1, -1.00000E+1, +0.00000E+0, +1.00000E+1, +2.00000E+1, +3.00000E+1, +4.00000E+1, +5.00000E+1, +6.00000E+1, +7.00000E+1, +8.00000E+1, +9.00000E+1, +1.00000E+2, +1.10000E+2, +1.20000E+2, +1.30000E+2, +1.40000E+2, +1.50000E+2, +1.60000E+2, +1.70000E+2, +1.80000E+2, +1.90000E+2, +2.00000E+2, +2.10000E+2, +2.20000E+2, +2.30000E+2, +2.40000E+2, +2.50000E+2, +2.60000E+2, +2.70000E+2, +2.80000E+2, +2.90000E+2, +3.00000E+2, +3.10000E+2, +3.20000E+2, +3.30000E+2, +3.40000E+2, +3.50000E+2, +3.60000E+2])+273.15  # Kelvin
         self.density.data             = np.array([+1.01140E+3, +1.00320E+3, +9.95600E+2, +9.88000E+2, +9.80500E+2, +9.72900E+2, +9.65400E+2, +9.57800E+2, +9.50200E+2, +9.42700E+2, +9.35100E+2, +9.27600E+2, +9.20000E+2, +9.12400E+2, +9.04900E+2, +8.97300E+2, +8.89800E+2, +8.82200E+2, +8.74600E+2, +8.67100E+2, +8.59500E+2, +8.52000E+2, +8.44400E+2, +8.36800E+2, +8.29300E+2, +8.21700E+2, +8.14200E+2, +8.06600E+2, +7.99000E+2, +7.91500E+2, +7.83900E+2, +7.76400E+2, +7.68800E+2, +7.61200E+2, +7.53700E+2, +7.46100E+2, +7.38600E+2, +7.31000E+2, +7.23400E+2, +7.15900E+2, +7.08300E+2])         # kg/m3
         self.specific_heat.data       = np.array([+1.47800E+0, +1.49200E+0, +1.52500E+0, +1.55700E+0, +1.58900E+0, +1.62100E+0, +1.65300E+0, +1.68500E+0, +1.71600E+0, +1.74800E+0, +1.77900E+0, +1.81100E+0, +1.84200E+0, +1.87300E+0, +1.90400E+0, +1.93500E+0, +1.96600E+0, +1.99700E+0, +2.02700E+0, +2.05800E+0, +2.08800E+0, +2.11800E+0, +2.14800E+0, +2.17800E+0, +2.20800E+0, +2.23800E+0, +2.26800E+0, +2.29700E+0, +2.32700E+0, +2.35600E+0, +2.38600E+0, +2.41500E+0, +2.44400E+0, +2.47300E+0, +2.50200E+0, +2.53000E+0, +2.55900E+0, +2.58700E+0, +2.61600E+0, +2.64400E+0, +2.67200E+0])*1000.   # J/kg-K
         self.conductivity.data        = np.array([+1.28000E-1, +1.27700E-1, +1.26600E-1, +1.25500E-1, +1.24400E-1, +1.23200E-1, +1.22000E-1, +1.20800E-1, +1.19500E-1, +1.18300E-1, +1.17000E-1, +1.15600E-1, +1.14300E-1, +1.12900E-1, +1.11500E-1, +1.10100E-1, +1.08700E-1, +1.07200E-1, +1.05800E-1, +1.04300E-1, +1.02800E-1, +1.01300E-1, +9.98000E-2, +9.82000E-2, +9.67000E-2, +9.52000E-2, +9.36000E-2, +9.21000E-2, +9.05000E-2, +8.89000E-2, +8.74000E-2, +8.58000E-2, +8.43000E-2, +8.27000E-2, +8.11000E-2, +7.96000E-2, +7.80000E-2, +7.65000E-2, +7.49000E-2, +7.34000E-2, +7.19000E-2])         # W/m-K
         self.viscosity.data           = np.array([+4.66000E+1, +2.42000E+1, +1.61000E+1, +1.09000E+1, +7.56000E+0, +5.42000E+0, +4.00000E+0, +3.04000E+0, +2.37000E+0, +1.89000E+0, +1.54000E+0, +1.28000E+0, +1.07000E+0, +9.20000E-1, +8.00000E-1, +7.00000E-1, +6.20000E-1, +5.50000E-1, +5.00000E-1, +4.50000E-1, +4.10000E-1, +3.80000E-1, +3.50000E-1, +3.30000E-1, +3.10000E-1, +2.90000E-1, +2.70000E-1, +2.60000E-1, +2.40000E-1, +2.30000E-1, +2.20000E-1, +2.10000E-1, +2.00000E-1, +1.90000E-1, +1.90000E-1, +1.80000E-1, +1.70000E-1, +1.70000E-1, +1.60000E-1, +1.60000E-1, +1.50000E-1])/1000.   # Pa-s
-        self.saturation_pressure.data = np.array([     np.NAN,      np.NAN,      np.NAN,      np.NAN,      np.NAN,      np.NAN,      np.NAN,      np.NAN,      np.NAN,      np.NAN,      np.NAN,      np.NAN,      np.NAN,      np.NAN,      np.NAN,      np.NAN, +5.00000E-3, +1.00000E-2, +2.00000E-2, +3.00000E-2, +5.00000E-2, +7.00000E-2, +9.00000E-2, +1.30000E-1, +1.70000E-1, +2.30000E-1, +3.10000E-1, +4.00000E-1, +5.10000E-1, +6.40000E-1, +8.10000E-1, +1.00000E+0, +1.24000E+0, +1.51000E+0, +1.82000E+0, +2.19000E+0, +2.61000E+0, +3.09000E+0, +3.64000E+0, +4.25000E+0, +4.95000E+0])*1e5     # Pa 
+        self.saturation_pressure.data = np.array([     np.NAN,      np.NAN,      np.NAN,      np.NAN,      np.NAN,      np.NAN,      np.NAN,      np.NAN,      np.NAN,      np.NAN,      np.NAN,      np.NAN,      np.NAN,      np.NAN,      np.NAN,      np.NAN, +5.00000E-3, +1.00000E-2, +2.00000E-2, +3.00000E-2, +5.00000E-2, +7.00000E-2, +9.00000E-2, +1.30000E-1, +1.70000E-1, +2.30000E-1, +3.10000E-1, +4.00000E-1, +5.10000E-1, +6.40000E-1, +8.10000E-1, +1.00000E+0, +1.24000E+0, +1.51000E+0, +1.82000E+0, +2.19000E+0, +2.61000E+0, +3.09000E+0, +3.64000E+0, +4.25000E+0, +4.95000E+0])*1e5     # Pa
         self.Tmin        = np.min(self.temperature.data)
         self.Tmax        = np.max(self.temperature.data)
-        self.TminPsat    = 120 + 273.15 
-        self.name        = "DowQ" 
+        self.TminPsat    = 120 + 273.15
+        self.name        = "DowQ"
         self.description = "DowthermQ"
-        self.reference   = "Dow Chemicals data sheet"
+        self.reference   = "Dow1997"
         self.reshapeAll()
-    
-    
+
+
 class Texatherm22(PureData):
-    """ 
+    """
     Heat transfer fluid Texatherm 22 by Texaco
     """
     def __init__(self):
-        PureData.__init__(self) 
+        PureData.__init__(self)
         self.density.source           = self.density.SOURCE_DATA
         self.specific_heat.source     = self.specific_heat.SOURCE_DATA
         self.conductivity.source      = self.conductivity.SOURCE_DATA
@@ -173,20 +173,20 @@ class Texatherm22(PureData):
         self.specific_heat.data       = np.array([+1.81000E+0, +1.95000E+0, +1.99000E+0, +2.18000E+0, +2.36000E+0, +2.54000E+0, +2.72000E+0, +2.90000E+0, +3.08000E+0])*1e3     # J/kg-K
         self.conductivity.data        = np.array([+1.35000E-1, +1.32000E-1, +1.32000E-1, +1.28000E-1, +1.25000E-1, +1.21000E-1, +1.17100E-1, +1.13000E-1, +1.10000E-1])         # W/m-K
         self.viscosity.data           = np.array([+4.19760E+2,      np.NAN, +2.31688E+1,      np.NAN, +2.09601E+0, +1.26072E+0,      np.NAN,      np.NAN,      np.NAN])/1000.   # Pa-s
-        self.saturation_pressure.data = np.array([     np.NAN, +5.3300E-10, +4.00000E-8, +2.67000E-7, +2.27000E-5, +4.67000E-4, +6.67000E-3, +2.13000E-2, +5.33000E-2])*1e5     # Pa 
+        self.saturation_pressure.data = np.array([     np.NAN, +5.3300E-10, +4.00000E-8, +2.67000E-7, +2.27000E-5, +4.67000E-4, +6.67000E-3, +2.13000E-2, +5.33000E-2])*1e5     # Pa
         self.Tmin        = np.min(self.temperature.data)
         self.Tmax        = np.max(self.temperature.data)
-        self.TminPsat    = 40 + 273.15 
-        self.name        = "TX22" 
+        self.TminPsat    = 40 + 273.15
+        self.name        = "TX22"
         self.description = "Texatherm22"
-        self.reference   = "Texaco data sheet"
+        self.reference   = "Chevron2004"
         self.reshapeAll()
-            
+
 
 class SylthermXLT(PureData):
-    """ 
+    """
     Heat transfer fluid Syltherm XLT by Dow Chemicals
-    """    
+    """
     def __init__(self):
         PureData.__init__(self)
         self.density.source           = self.density.SOURCE_DATA
@@ -200,19 +200,19 @@ class SylthermXLT(PureData):
         self.viscosity.data           = np.array([+7.86100E+1, +5.01300E+1, +3.48600E+1, +2.58300E+1, +2.00400E+1, +1.60800E+1, +1.32200E+1, +1.10500E+1, +9.35600E+0, +7.99400E+0, +6.87900E+0, +5.95600E+0, +5.18400E+0, +4.53500E+0, +3.98600E+0, +3.52100E+0, +3.12600E+0, +2.78800E+0, +2.49900E+0, +2.25000E+0, +2.03500E+0, +1.84900E+0, +1.68700E+0, +1.54500E+0, +1.41900E+0, +1.30900E+0, +1.21000E+0, +1.12200E+0, +1.04300E+0, +9.72000E-1, +9.08000E-1, +8.49000E-1, +7.96000E-1, +7.48000E-1, +7.05000E-1, +6.65000E-1, +6.28000E-1, +5.95000E-1, +5.64000E-1, +5.36000E-1, +5.11000E-1, +4.87000E-1, +4.65000E-1, +4.45000E-1, +4.26000E-1, +4.09000E-1, +3.93000E-1, +3.77000E-1, +3.63000E-1, +3.50000E-1, +3.37000E-1, +3.25000E-1, +3.14000E-1, +3.03000E-1, +2.93000E-1, +2.84000E-1, +2.75000E-1, +2.66000E-1, +2.58000E-1, +2.51000E-1, +2.44000E-1, +2.38000E-1, +2.32000E-1, +2.26000E-1, +2.20000E-1, +2.15000E-1, +2.09000E-1, +2.04000E-1, +1.99000E-1, +1.94000E-1, +1.89000E-1, +1.85000E-1, +1.82000E-1])/1000.   # Pa-s
         self.Tmin        = np.min(self.temperature.data)
         self.Tmax        = np.max(self.temperature.data)
-        self.TminPsat    = self.Tmax 
-        self.name        = "XLT" 
+        self.TminPsat    = self.Tmax
+        self.name        = "XLT"
         self.description = "SylthermXLT"
-        self.reference   = "Dow Chemicals data sheet"
+        self.reference   = "Dow1997"
         self.reshapeAll()
-    
-    
+
+
 class HC50(PureData):
-    """ 
+    """
     Heat transfer fluid Dynalene HC-50
     """
     def __init__(self):
-        PureData.__init__(self) 
+        PureData.__init__(self)
         self.density.source           = self.density.SOURCE_DATA
         self.specific_heat.source     = self.specific_heat.SOURCE_DATA
         self.conductivity.source      = self.conductivity.SOURCE_DATA
@@ -223,22 +223,22 @@ class HC50(PureData):
         self.specific_heat.data       = np.array([+2.56300E+3,+2.58300E+3,+2.60200E+3,+2.62200E+3,+2.64200E+3,+2.66100E+3,+2.68100E+3,+2.70100E+3,+2.72000E+3,+2.74000E+3,+2.76000E+3,+2.78000E+3,+2.79900E+3,+2.81900E+3,+2.83900E+3,+2.85800E+3,+2.87800E+3,+2.89800E+3,+2.91700E+3,+2.93700E+3,+2.95700E+3,+2.97700E+3,+2.99600E+3,+3.01600E+3,+3.03600E+3,+3.05500E+3,+3.07500E+3]) # J/kg-K
         self.conductivity.data        = np.array([+4.35000E+2,+4.45000E+2,+4.55000E+2,+4.65000E+2,+4.75000E+2,+4.85000E+2,+4.95000E+2,+5.05000E+2,+5.15000E+2,+5.25000E+2,+5.35000E+2,+5.45000E+2,+5.55000E+2,+5.65000E+2,+5.75000E+2,+5.85000E+2,+5.95000E+2,+6.05000E+2,+6.15000E+2,+6.25000E+2,+6.35000E+2,+6.45000E+2,+6.55000E+2,+6.65000E+2,+6.75000E+2,+6.85000E+2,+6.94500E+2])/1e3 # W/m-K
         self.viscosity.data           = np.array([+3.84000E-2,+2.04000E-2,+1.25000E-2,+8.40000E-3,+5.99000E-3,+4.70000E-3,+3.80000E-3,+3.20000E-3,+2.70000E-3,+2.40000E-3,+2.10000E-3,+1.80000E-3,+1.60000E-3,+1.50000E-3,+1.30000E-3,+1.20000E-3,+1.10000E-3,+1.00000E-3,+9.40000E-4,+8.70000E-4,+8.10000E-4,+7.60000E-4,+7.10000E-4,+6.60000E-4,+6.20000E-4,+5.80000E-4,+5.50000E-4]) # Pa-s
-        self.saturation_pressure.data = np.array([     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,+1.58579E+3,+1.93053E+3,+3.10264E+3,+5.58475E+3,+9.85950E+3,+1.64785E+4,+2.60622E+4,+3.93691E+4,+5.72954E+4,+8.06687E+4,+1.11695E+5,+1.50995E+5,+2.00637E+5,+2.63380E+5,+3.41290E+5,+4.36438E+5,+5.53649E+5,+6.95681E+5,+8.67360E+5,+1.07282E+6]) # Pa 
+        self.saturation_pressure.data = np.array([     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,+1.58579E+3,+1.93053E+3,+3.10264E+3,+5.58475E+3,+9.85950E+3,+1.64785E+4,+2.60622E+4,+3.93691E+4,+5.72954E+4,+8.06687E+4,+1.11695E+5,+1.50995E+5,+2.00637E+5,+2.63380E+5,+3.41290E+5,+4.36438E+5,+5.53649E+5,+6.95681E+5,+8.67360E+5,+1.07282E+6]) # Pa
         self.Tmin        = np.min(self.temperature.data)
         self.Tmax        = np.max(self.temperature.data)
-        self.TminPsat    = 20+273.15 
+        self.TminPsat    = 20+273.15
         self.name        = "HC50"
         self.description = "Dynalene "+ self.name
-        self.reference   = "Dynalene data sheet"
+        self.reference   = "Dynalene2014"
         self.reshapeAll()
 
-   
+
 class HC40(PureData):
-    """ 
+    """
     Heat transfer fluid Dynalene HC-40
-    """    
+    """
     def __init__(self):
-        PureData.__init__(self) 
+        PureData.__init__(self)
         self.density.source           = self.density.SOURCE_DATA
         self.specific_heat.source     = self.specific_heat.SOURCE_DATA
         self.conductivity.source      = self.conductivity.SOURCE_DATA
@@ -249,22 +249,22 @@ class HC40(PureData):
         self.specific_heat.data       = np.array([+2.80000E+3,+2.82000E+3,+2.84000E+3,+2.87000E+3,+2.89000E+3,+2.91000E+3,+2.93000E+3,+2.96000E+3,+2.98000E+3,+3.00000E+3,+3.03000E+3,+3.05000E+3,+3.07000E+3,+3.09000E+3,+3.12000E+3,+3.14000E+3,+3.16000E+3,+3.19000E+3,+3.21000E+3,+3.23000E+3,+3.25000E+3,+3.27000E+3,+3.28000E+3,+3.30000E+3,+3.32000E+3,+3.35000E+3]) # J/kg-K
         self.conductivity.data        = np.array([+4.49000E+2,+4.59000E+2,+4.69000E+2,+4.79000E+2,+4.89000E+2,+4.99000E+2,+5.09000E+2,+5.19000E+2,+5.29000E+2,+5.39000E+2,+5.49000E+2,+5.59000E+2,+5.69000E+2,+5.79000E+2,+5.89000E+2,+5.99000E+2,+6.09000E+2,+6.19000E+2,+6.29000E+2,+6.39000E+2,+6.49000E+2,+6.54000E+2,+6.59000E+2,+6.69000E+2,+6.79000E+2,+6.89000E+2])/1e3 # W/m-K
         self.viscosity.data           = np.array([+1.49000E-2,+9.20000E-3,+6.50000E-3,+4.90000E-3,+3.90000E-3,+3.20000E-3,+2.70000E-3,+2.30000E-3,+1.96000E-3,+1.70000E-3,+1.50000E-3,+1.40000E-3,+1.20000E-3,+1.10000E-3,+9.90000E-4,+9.10000E-4,+8.30000E-4,+7.70000E-4,+7.10000E-4,+6.60000E-4,+6.10000E-4,+5.90000E-4,+5.70000E-4,+5.30000E-4,+5.00000E-4,+4.70000E-4]) # Pa-s
-        self.saturation_pressure.data = np.array([     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,+1.51685E+3,+2.20632E+3,+3.79212E+3,+6.68791E+3,+1.15142E+4,+1.87537E+4,+2.92338E+4,+4.37817E+4,+6.35007E+4,+8.96318E+4,+1.23416E+5,+1.66853E+5,+2.22701E+5,+2.92338E+5,+3.79212E+5,+4.85391E+5,+6.16391E+5,+7.74971E+5,+9.65955E+5,+1.19417E+6]) # Pa 
+        self.saturation_pressure.data = np.array([     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,+1.51685E+3,+2.20632E+3,+3.79212E+3,+6.68791E+3,+1.15142E+4,+1.87537E+4,+2.92338E+4,+4.37817E+4,+6.35007E+4,+8.96318E+4,+1.23416E+5,+1.66853E+5,+2.22701E+5,+2.92338E+5,+3.79212E+5,+4.85391E+5,+6.16391E+5,+7.74971E+5,+9.65955E+5,+1.19417E+6]) # Pa
         self.Tmin        = np.min(self.temperature.data)
         self.Tmax        = np.max(self.temperature.data)
-        self.TminPsat    = 20+273.15  
+        self.TminPsat    = 20+273.15
         self.name        = "HC40"
         self.description = "Dynalene "+ self.name
-        self.reference   = "Dynalene data sheet"
+        self.reference   = "Dynalene2014"
         self.reshapeAll()
 
-    
+
 class HC30(PureData):
-    """ 
+    """
     Heat transfer fluid Dynalene HC-30
-    """    
+    """
     def __init__(self):
-        PureData.__init__(self) 
+        PureData.__init__(self)
         self.density.source           = self.density.SOURCE_DATA
         self.specific_heat.source     = self.specific_heat.SOURCE_DATA
         self.conductivity.source      = self.conductivity.SOURCE_DATA
@@ -275,22 +275,22 @@ class HC30(PureData):
         self.specific_heat.data       = np.array([+2.96100E+3,+2.98400E+3,+3.00700E+3,+3.03100E+3,+3.05400E+3,+3.07700E+3,+3.10000E+3,+3.12300E+3,+3.14600E+3,+3.16900E+3,+3.19200E+3,+3.21500E+3,+3.23800E+3,+3.26200E+3,+3.28500E+3,+3.30800E+3,+3.33100E+3,+3.35400E+3,+3.37700E+3,+3.40000E+3,+3.42300E+3,+3.44600E+3,+3.46900E+3,+3.49300E+3,+3.51600E+3]) # J/kg-K
         self.conductivity.data        = np.array([+4.69000E+2,+4.79000E+2,+4.89000E+2,+4.99000E+2,+5.09000E+2,+5.19000E+2,+5.29000E+2,+5.39000E+2,+5.49000E+2,+5.59000E+2,+5.69000E+2,+5.79000E+2,+5.89000E+2,+5.99000E+2,+6.09000E+2,+6.19000E+2,+6.29000E+2,+6.39000E+2,+6.49000E+2,+6.59000E+2,+6.69000E+2,+6.79000E+2,+6.89000E+2,+6.99000E+2,+7.09000E+2])/1e3 # W/m-K
         self.viscosity.data           = np.array([+7.00000E-3,+5.50000E-3,+4.50000E-3,+3.70000E-3,+3.00000E-3,+2.50000E-3,+2.20000E-3,+1.90000E-3,+1.60000E-3,+1.40000E-3,+1.30000E-3,+1.10000E-3,+9.90000E-4,+8.90000E-4,+8.00000E-4,+7.30000E-4,+6.70000E-4,+6.10000E-4,+5.70000E-4,+5.20000E-4,+4.80000E-4,+4.50000E-4,+4.20000E-4,+3.90000E-4,+3.70000E-4]) # Pa-s
-        self.saturation_pressure.data = np.array([     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,+1.79264E+3,+2.41317E+3,+3.99896E+3,+7.17055E+3,+1.24795E+4,+2.06153E+4,+3.23364E+4,+4.86770E+4,+7.10160E+4,+9.99740E+4,+1.37895E+5,+1.86158E+5,+2.47522E+5,+3.24743E+5,+4.20580E+5,+5.39170E+5,+6.83960E+5,+8.59087E+5,+1.07145E+6,+1.32517E+6]) # Pa 
+        self.saturation_pressure.data = np.array([     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,+1.79264E+3,+2.41317E+3,+3.99896E+3,+7.17055E+3,+1.24795E+4,+2.06153E+4,+3.23364E+4,+4.86770E+4,+7.10160E+4,+9.99740E+4,+1.37895E+5,+1.86158E+5,+2.47522E+5,+3.24743E+5,+4.20580E+5,+5.39170E+5,+6.83960E+5,+8.59087E+5,+1.07145E+6,+1.32517E+6]) # Pa
         self.Tmin        = np.min(self.temperature.data)
         self.Tmax        = np.max(self.temperature.data)
-        self.TminPsat    = 20+273.15  
+        self.TminPsat    = 20+273.15
         self.name        = "HC30"
         self.description = "Dynalene "+ self.name
-        self.reference   = "Dynalene data sheet"
+        self.reference   = "Dynalene2014"
         self.reshapeAll()
-    
-    
+
+
 class HC20(PureData):
-    """ 
+    """
     Heat transfer fluid Dynalene HC-20
-    """    
+    """
     def __init__(self):
-        PureData.__init__(self) 
+        PureData.__init__(self)
         self.density.source           = self.density.SOURCE_DATA
         self.specific_heat.source     = self.specific_heat.SOURCE_DATA
         self.conductivity.source      = self.conductivity.SOURCE_DATA
@@ -301,22 +301,22 @@ class HC20(PureData):
         self.specific_heat.data       = np.array([+3.11700E+3,+3.14100E+3,+3.16400E+3,+3.18800E+3,+3.21200E+3,+3.23500E+3,+3.25900E+3,+3.28200E+3,+3.30600E+3,+3.33000E+3,+3.35300E+3,+3.37700E+3,+3.40000E+3,+3.42400E+3,+3.44800E+3,+3.47100E+3,+3.49500E+3,+3.51800E+3,+3.54200E+3,+3.56600E+3,+3.58900E+3,+3.61300E+3,+3.63600E+3,+3.66000E+3]) # J/kg-K
         self.conductivity.data        = np.array([+4.83000E+2,+4.93000E+2,+5.03000E+2,+5.13000E+2,+5.23000E+2,+5.33000E+2,+5.43000E+2,+5.53000E+2,+5.63000E+2,+5.73000E+2,+5.83000E+2,+5.93000E+2,+6.03000E+2,+6.13000E+2,+6.23000E+2,+6.33000E+2,+6.43000E+2,+6.53000E+2,+6.63000E+2,+6.73000E+2,+6.83000E+2,+6.93000E+2,+7.03000E+2,+7.13000E+2])/1e3 # W/m-K
         self.viscosity.data           = np.array([+4.50000E-3,+3.60000E-3,+3.00000E-3,+2.50000E-3,+2.10000E-3,+1.80000E-3,+1.60000E-3,+1.40000E-3,+1.20000E-3,+1.10000E-3,+9.50000E-4,+8.50000E-4,+7.70000E-4,+7.00000E-4,+6.30000E-4,+5.80000E-4,+5.40000E-4,+4.90000E-4,+4.60000E-4,+4.30000E-4,+4.00000E-4,+3.70000E-4,+3.50000E-4,+3.30000E-4]) # Pa-s
-        self.saturation_pressure.data = np.array([     np.NAN,     np.NAN,     np.NAN,     np.NAN,+2.06843E+3,+2.75790E+3,+4.55054E+3,+7.99792E+3,+1.37206E+4,+2.24769E+4,+3.52322E+4,+5.29517E+4,+7.72213E+4,+1.08937E+5,+1.50306E+5,+2.04085E+5,+2.71653E+5,+3.57148E+5,+4.62638E+5,+5.93639E+5,+7.52907E+5,+9.46650E+5,+1.18038E+6,+1.45962E+6]) # Pa  
+        self.saturation_pressure.data = np.array([     np.NAN,     np.NAN,     np.NAN,     np.NAN,+2.06843E+3,+2.75790E+3,+4.55054E+3,+7.99792E+3,+1.37206E+4,+2.24769E+4,+3.52322E+4,+5.29517E+4,+7.72213E+4,+1.08937E+5,+1.50306E+5,+2.04085E+5,+2.71653E+5,+3.57148E+5,+4.62638E+5,+5.93639E+5,+7.52907E+5,+9.46650E+5,+1.18038E+6,+1.45962E+6]) # Pa
         self.Tmin        = np.min(self.temperature.data)
         self.Tmax        = np.max(self.temperature.data)
-        self.TminPsat    = 20+273.15  
+        self.TminPsat    = 20+273.15
         self.name        = "HC20"
         self.description = "Dynalene "+ self.name
-        self.reference   = "Dynalene data sheet"
+        self.reference   = "Dynalene2014"
         self.reshapeAll()
-    
+
 
 class HC10(PureData):
-    """ 
+    """
     Heat transfer fluid Dynalene HC-10
-    """    
+    """
     def __init__(self):
-        PureData.__init__(self) 
+        PureData.__init__(self)
         self.density.source           = self.density.SOURCE_DATA
         self.specific_heat.source     = self.specific_heat.SOURCE_DATA
         self.conductivity.source      = self.conductivity.SOURCE_DATA
@@ -327,13 +327,238 @@ class HC10(PureData):
         self.specific_heat.data       = np.array([+3.24600E+3,+3.27100E+3,+3.29600E+3,+3.32000E+3,+3.34500E+3,+3.37000E+3,+3.39500E+3,+3.42000E+3,+3.44400E+3,+3.46900E+3,+3.49400E+3,+3.51900E+3,+3.54400E+3,+3.56800E+3,+3.59300E+3,+3.61800E+3,+3.64300E+3,+3.66800E+3,+3.69200E+3,+3.71700E+3,+3.74200E+3,+3.76700E+3,+3.79200E+3,+3.81100E+3]) # J/kg-K
         self.conductivity.data        = np.array([+4.94000E+2,+5.04000E+2,+5.14000E+2,+5.24000E+2,+5.34000E+2,+5.44000E+2,+5.54000E+2,+5.64000E+2,+5.74000E+2,+5.84000E+2,+5.94000E+2,+6.04000E+2,+6.14000E+2,+6.24000E+2,+6.34000E+2,+6.44000E+2,+6.54000E+2,+6.64000E+2,+6.74000E+2,+6.84000E+2,+6.94000E+2,+7.04000E+2,+7.14000E+2,+7.22000E+2])/1e3 # W/m-K
         self.viscosity.data           = np.array([+3.00000E-3,+2.50000E-3,+2.10000E-3,+1.80000E-3,+1.50000E-3,+1.30000E-3,+1.20000E-3,+1.00000E-3,+9.10000E-4,+8.10000E-4,+7.30000E-4,+6.60000E-4,+6.00000E-4,+5.50000E-4,+5.10000E-4,+4.70000E-4,+4.30000E-4,+4.00000E-4,+3.70000E-4,+3.50000E-4,+3.30000E-4,+3.10000E-4,+2.90000E-4,+2.80000E-4]) # Pa-s
-        self.saturation_pressure.data = np.array([     np.NAN,     np.NAN,     np.NAN,+2.27527E+3,+2.89580E+3,+4.75738E+3,+8.54950E+3,+1.48927E+4,+2.46143E+4,+3.87485E+4,+5.83986E+4,+8.48055E+4,+1.19969E+5,+1.65474E+5,+2.23390E+5,+2.97164E+5,+3.90243E+5,+5.05386E+5,+6.47418E+5,+8.20476E+5,+1.03146E+6,+1.28587E+6,+1.58993E+6,+1.87468E+6]) # Pa 
+        self.saturation_pressure.data = np.array([     np.NAN,     np.NAN,     np.NAN,+2.27527E+3,+2.89580E+3,+4.75738E+3,+8.54950E+3,+1.48927E+4,+2.46143E+4,+3.87485E+4,+5.83986E+4,+8.48055E+4,+1.19969E+5,+1.65474E+5,+2.23390E+5,+2.97164E+5,+3.90243E+5,+5.05386E+5,+6.47418E+5,+8.20476E+5,+1.03146E+6,+1.28587E+6,+1.58993E+6,+1.87468E+6]) # Pa
         self.Tmin        = np.min(self.temperature.data)
         self.Tmax        = np.max(self.temperature.data)
-        self.TminPsat    = 20+273.15  
+        self.TminPsat    = 20+273.15
         self.name        = "HC10"
         self.description = "Dynalene "+ self.name
-        self.reference   = "Dynalene data sheet"
+        self.reference   = "Dynalene2014"
+        self.reshapeAll()
+
+
+
+## Paratherm, see http://paracalc.paratherm.com
+class PCR(PureData):
+    """
+    The Paratherm CR (Patent Pending) heat transfer fluid provides predictable,
+    repeatable performance in cryogenically-driven processes. Consistent
+    properties improve productivity, and eliminate runaway coil freeze-ups.
+    10-cP viscosity @ -88 C (20-cP @ -96 C) brings higher efficiency at lower
+    temperatures. Ease of containment and handling allow greater latitude in
+    system design and component specification, and eliminate contamination and
+    costly clean-up.
+    """
+    def __init__(self):
+        PureData.__init__(self)
+        self.density.source           = self.density.SOURCE_DATA
+        self.viscosity.source         = self.viscosity.SOURCE_DATA
+        self.specific_heat.source     = self.specific_heat.SOURCE_DATA
+        self.conductivity.source      = self.conductivity.SOURCE_DATA
+        self.saturation_pressure.source = self.saturation_pressure.SOURCE_DATA
+        self.temperature.data         = np.array([1.731500E+2,1.741500E+2,1.751500E+2,1.761500E+2,1.771500E+2,1.781500E+2,1.791500E+2,1.801500E+2,1.811500E+2,1.821500E+2,1.831500E+2,1.841500E+2,1.851500E+2,1.861500E+2,1.871500E+2,1.881500E+2,1.891500E+2,1.901500E+2,1.911500E+2,1.921500E+2,1.931500E+2,1.941500E+2,1.951500E+2,1.961500E+2,1.971500E+2,1.981500E+2,1.991500E+2,2.001500E+2,2.011500E+2,2.021500E+2,2.031500E+2,2.041500E+2,2.051500E+2,2.061500E+2,2.071500E+2,2.081500E+2,2.091500E+2,2.101500E+2,2.111500E+2,2.121500E+2,2.131500E+2,2.141500E+2,2.151500E+2,2.161500E+2,2.171500E+2,2.181500E+2,2.191500E+2,2.201500E+2,2.211500E+2,2.221500E+2,2.231500E+2,2.241500E+2,2.251500E+2,2.261500E+2,2.271500E+2,2.281500E+2,2.291500E+2,2.301500E+2,2.311500E+2,2.321500E+2,2.331500E+2,2.341500E+2,2.351500E+2,2.361500E+2,2.371500E+2,2.381500E+2,2.391500E+2,2.401500E+2,2.411500E+2,2.421500E+2,2.431500E+2,2.441500E+2,2.451500E+2,2.461500E+2,2.471500E+2,2.481500E+2,2.491500E+2,2.501500E+2,2.511500E+2,2.521500E+2,2.531500E+2,2.541500E+2,2.551500E+2,2.561500E+2,2.571500E+2,2.581500E+2,2.591500E+2,2.601500E+2,2.611500E+2,2.621500E+2,2.631500E+2,2.641500E+2,2.651500E+2,2.661500E+2,2.671500E+2,2.681500E+2,2.691500E+2,2.701500E+2,2.711500E+2,2.721500E+2,2.731500E+2,2.741500E+2,2.751500E+2,2.761500E+2,2.771500E+2,2.781500E+2,2.791500E+2,2.801500E+2,2.811500E+2,2.821500E+2,2.831500E+2,2.841500E+2,2.851500E+2,2.861500E+2,2.871500E+2,2.881500E+2,2.891500E+2,2.901500E+2,2.911500E+2,2.921500E+2,2.931500E+2,2.941500E+2,2.951500E+2,2.961500E+2,2.971500E+2,2.981500E+2,2.991500E+2,3.001500E+2,3.011500E+2,3.021500E+2,3.031500E+2,3.041500E+2,3.051500E+2,3.061500E+2,3.071500E+2,3.081500E+2,3.091500E+2,3.101500E+2,3.111500E+2,3.121500E+2,3.131500E+2,3.141500E+2,3.151500E+2,3.161500E+2,3.171500E+2,3.181500E+2,3.191500E+2,3.201500E+2,3.211500E+2,3.221500E+2,3.231500E+2,3.241500E+2,3.251500E+2,3.261500E+2,3.271500E+2,3.281500E+2,3.291500E+2,3.301500E+2,3.311500E+2,3.321500E+2,3.331500E+2,3.341500E+2,3.351500E+2,3.361500E+2,3.371500E+2,3.381500E+2,3.391500E+2,3.401500E+2,3.411500E+2,3.421500E+2,3.431500E+2,3.441500E+2,3.451500E+2,3.461500E+2,3.471500E+2,3.481500E+2,3.491500E+2,3.501500E+2,3.511500E+2,3.521500E+2,3.531500E+2,3.541500E+2,3.551500E+2,3.561500E+2,3.571500E+2,3.581500E+2,3.591500E+2,3.601500E+2,3.611500E+2,3.621500E+2,3.631500E+2,3.641500E+2,3.651500E+2,3.661500E+2,3.671500E+2,3.681500E+2,3.691500E+2,3.701500E+2,3.711500E+2,3.721500E+2,3.731500E+2,3.741500E+2,3.751500E+2,3.761500E+2,3.771500E+2,3.781500E+2,3.791500E+2,3.801500E+2,3.811500E+2,3.821500E+2,3.831500E+2,3.841500E+2,3.851500E+2,3.861500E+2,3.871500E+2,3.881500E+2,3.891500E+2,3.901500E+2,3.911500E+2,3.921500E+2,3.931500E+2,3.941500E+2,3.951500E+2,3.961500E+2,3.971500E+2,3.981500E+2,3.991500E+2,4.001500E+2,4.011500E+2,4.021500E+2,4.031500E+2,4.041500E+2,4.051500E+2,4.061500E+2,4.071500E+2,4.081500E+2,4.091500E+2,4.101500E+2,4.111500E+2,4.121500E+2,4.131500E+2,4.141500E+2,4.151500E+2,4.161500E+2,4.171500E+2,4.181500E+2,4.191500E+2,4.201500E+2,4.211500E+2,4.221500E+2,4.231500E+2,4.241500E+2,4.251500E+2,4.261500E+2,4.271500E+2,4.281500E+2,4.291500E+2,4.301500E+2,4.311500E+2,4.321500E+2,4.331500E+2,4.341500E+2,4.351500E+2,4.361500E+2,4.371500E+2,4.381500E+2,4.391500E+2,4.401500E+2,4.411500E+2,4.421500E+2,4.431500E+2,4.441500E+2,4.451500E+2,4.461500E+2,4.471500E+2,4.481500E+2,4.491500E+2,4.501500E+2,4.511500E+2,4.521500E+2,4.531500E+2,4.541500E+2,4.551500E+2,4.561500E+2,4.571500E+2,4.581500E+2,4.591500E+2,4.601500E+2,4.611500E+2,4.621500E+2,4.631500E+2,4.641500E+2,4.651500E+2,4.661500E+2,4.671500E+2,4.681500E+2,4.691500E+2,4.701500E+2,4.711500E+2,4.721500E+2,4.731500E+2,4.741500E+2,4.751500E+2,4.761500E+2,4.771500E+2,4.781500E+2,4.791500E+2,4.801500E+2,4.811500E+2,4.821500E+2,4.831500E+2,4.841500E+2,4.851500E+2,4.861500E+2,4.871500E+2,4.881500E+2,4.891500E+2,4.901500E+2,4.911500E+2,4.921500E+2,4.931500E+2])
+        self.density.data             = np.array([9.490000E+2,9.480000E+2,9.470000E+2,9.460000E+2,9.450000E+2,9.440000E+2,9.430000E+2,9.420000E+2,9.410000E+2,9.400000E+2,9.390000E+2,9.380000E+2,9.370000E+2,9.360000E+2,9.350000E+2,9.340000E+2,9.330000E+2,9.320000E+2,9.310000E+2,9.300000E+2,9.290000E+2,9.280000E+2,9.270000E+2,9.260000E+2,9.250000E+2,9.240000E+2,9.230000E+2,9.220000E+2,9.210000E+2,9.200000E+2,9.190000E+2,9.180000E+2,9.170000E+2,9.160000E+2,9.150000E+2,9.140000E+2,9.130000E+2,9.120000E+2,9.110000E+2,9.100000E+2,9.090000E+2,9.080000E+2,9.070000E+2,9.060000E+2,9.050000E+2,9.040000E+2,9.030000E+2,9.020000E+2,9.010000E+2,9.000000E+2,8.990000E+2,8.980000E+2,8.970000E+2,8.960000E+2,8.950000E+2,8.940000E+2,8.930000E+2,8.920000E+2,8.910000E+2,8.900000E+2,8.890000E+2,8.880000E+2,8.870000E+2,8.860000E+2,8.850000E+2,8.830000E+2,8.820000E+2,8.810000E+2,8.800000E+2,8.790000E+2,8.780000E+2,8.770000E+2,8.760000E+2,8.750000E+2,8.740000E+2,8.730000E+2,8.720000E+2,8.710000E+2,8.700000E+2,8.690000E+2,8.680000E+2,8.670000E+2,8.660000E+2,8.650000E+2,8.640000E+2,8.630000E+2,8.620000E+2,8.610000E+2,8.600000E+2,8.590000E+2,8.580000E+2,8.570000E+2,8.560000E+2,8.550000E+2,8.540000E+2,8.530000E+2,8.520000E+2,8.510000E+2,8.500000E+2,8.490000E+2,8.480000E+2,8.470000E+2,8.460000E+2,8.450000E+2,8.440000E+2,8.430000E+2,8.420000E+2,8.410000E+2,8.400000E+2,8.390000E+2,8.380000E+2,8.370000E+2,8.360000E+2,8.350000E+2,8.340000E+2,8.330000E+2,8.320000E+2,8.310000E+2,8.300000E+2,8.290000E+2,8.280000E+2,8.270000E+2,8.260000E+2,8.250000E+2,8.240000E+2,8.230000E+2,8.220000E+2,8.210000E+2,8.200000E+2,8.190000E+2,8.180000E+2,8.170000E+2,8.160000E+2,8.150000E+2,8.140000E+2,8.130000E+2,8.120000E+2,8.110000E+2,8.100000E+2,8.090000E+2,8.080000E+2,8.070000E+2,8.060000E+2,8.050000E+2,8.040000E+2,8.030000E+2,8.020000E+2,8.010000E+2,8.000000E+2,7.990000E+2,7.980000E+2,7.970000E+2,7.960000E+2,7.950000E+2,7.940000E+2,7.930000E+2,7.920000E+2,7.910000E+2,7.900000E+2,7.890000E+2,7.880000E+2,7.870000E+2,7.860000E+2,7.850000E+2,7.840000E+2,7.830000E+2,7.820000E+2,7.810000E+2,7.800000E+2,7.790000E+2,7.780000E+2,7.770000E+2,7.760000E+2,7.750000E+2,7.740000E+2,7.730000E+2,7.720000E+2,7.710000E+2,7.700000E+2,7.690000E+2,7.680000E+2,7.670000E+2,7.660000E+2,7.650000E+2,7.640000E+2,7.630000E+2,7.620000E+2,7.610000E+2,7.600000E+2,7.590000E+2,7.580000E+2,7.570000E+2,7.560000E+2,7.550000E+2,7.540000E+2,7.520000E+2,7.510000E+2,7.500000E+2,7.490000E+2,7.480000E+2,7.470000E+2,7.460000E+2,7.450000E+2,7.440000E+2,7.430000E+2,7.420000E+2,7.410000E+2,7.400000E+2,7.390000E+2,7.380000E+2,7.370000E+2,7.360000E+2,7.350000E+2,7.340000E+2,7.330000E+2,7.320000E+2,7.310000E+2,7.300000E+2,7.290000E+2,7.280000E+2,7.270000E+2,7.260000E+2,7.250000E+2,7.240000E+2,7.230000E+2,7.220000E+2,7.210000E+2,7.200000E+2,7.190000E+2,7.180000E+2,7.170000E+2,7.160000E+2,7.150000E+2,7.140000E+2,7.130000E+2,7.120000E+2,7.110000E+2,7.100000E+2,7.090000E+2,7.080000E+2,7.070000E+2,7.060000E+2,7.050000E+2,7.040000E+2,7.030000E+2,7.020000E+2,7.010000E+2,7.000000E+2,6.990000E+2,6.980000E+2,6.970000E+2,6.960000E+2,6.950000E+2,6.940000E+2,6.930000E+2,6.920000E+2,6.910000E+2,6.900000E+2,6.890000E+2,6.880000E+2,6.870000E+2,6.860000E+2,6.850000E+2,6.840000E+2,6.830000E+2,6.820000E+2,6.810000E+2,6.800000E+2,6.790000E+2,6.780000E+2,6.770000E+2,6.760000E+2,6.750000E+2,6.740000E+2,6.730000E+2,6.720000E+2,6.710000E+2,6.700000E+2,6.690000E+2,6.680000E+2,6.670000E+2,6.660000E+2,6.650000E+2,6.640000E+2,6.630000E+2,6.620000E+2,6.610000E+2,6.600000E+2,6.590000E+2,6.580000E+2,6.570000E+2,6.560000E+2,6.550000E+2,6.540000E+2,6.530000E+2,6.520000E+2,6.510000E+2,6.500000E+2,6.490000E+2,6.480000E+2,6.470000E+2,6.460000E+2,6.450000E+2,6.440000E+2,6.430000E+2,6.420000E+2,6.410000E+2,6.400000E+2,6.390000E+2,6.380000E+2,6.370000E+2,6.360000E+2,6.350000E+2,6.340000E+2,6.330000E+2,6.320000E+2,6.310000E+2,6.300000E+2,6.290000E+2,6.280000E+2,6.270000E+2])
+        self.viscosity.data           = np.array([3.400000E-5,3.300000E-5,3.100000E-5,3.000000E-5,2.800000E-5,2.700000E-5,2.500000E-5,2.400000E-5,2.300000E-5,2.100000E-5,2.000000E-5,1.900000E-5,1.800000E-5,1.700000E-5,1.600000E-5,1.500000E-5,1.400000E-5,1.300000E-5,1.200000E-5,1.100000E-5,9.900000E-6,9.100000E-6,8.300000E-6,7.500000E-6,6.800000E-6,6.100000E-6,5.500000E-6,5.000000E-6,4.900000E-6,4.800000E-6,4.600000E-6,4.500000E-6,4.400000E-6,4.300000E-6,4.200000E-6,4.100000E-6,4.000000E-6,3.800000E-6,3.800000E-6,3.700000E-6,3.600000E-6,3.500000E-6,3.400000E-6,3.300000E-6,3.200000E-6,3.100000E-6,3.000000E-6,3.000000E-6,2.900000E-6,2.800000E-6,2.700000E-6,2.700000E-6,2.600000E-6,2.500000E-6,2.500000E-6,2.400000E-6,2.400000E-6,2.300000E-6,2.200000E-6,2.200000E-6,2.100000E-6,2.100000E-6,2.000000E-6,2.000000E-6,2.000000E-6,1.900000E-6,1.900000E-6,1.800000E-6,1.800000E-6,1.800000E-6,1.700000E-6,1.700000E-6,1.600000E-6,1.600000E-6,1.600000E-6,1.500000E-6,1.500000E-6,1.500000E-6,1.500000E-6,1.400000E-6,1.400000E-6,1.400000E-6,1.400000E-6,1.400000E-6,1.300000E-6,1.300000E-6,1.300000E-6,1.300000E-6,1.200000E-6,1.200000E-6,1.200000E-6,1.200000E-6,1.200000E-6,1.200000E-6,1.100000E-6,1.100000E-6,1.100000E-6,1.100000E-6,1.100000E-6,1.100000E-6,1.100000E-6,1.000000E-6,1.000000E-6,1.000000E-6,1.000000E-6,9.900000E-7,9.700000E-7,9.600000E-7,9.400000E-7,9.300000E-7,9.100000E-7,1.500000E-6,1.500000E-6,1.500000E-6,1.500000E-6,1.400000E-6,1.400000E-6,1.400000E-6,1.300000E-6,1.300000E-6,1.300000E-6,1.300000E-6,1.200000E-6,1.200000E-6,1.200000E-6,1.100000E-6,1.100000E-6,1.100000E-6,1.100000E-6,1.100000E-6,1.000000E-6,1.000000E-6,9.900000E-7,9.700000E-7,9.400000E-7,9.200000E-7,9.000000E-7,8.800000E-7,8.600000E-7,8.400000E-7,8.200000E-7,8.000000E-7,7.900000E-7,7.700000E-7,7.500000E-7,7.300000E-7,7.200000E-7,7.000000E-7,6.800000E-7,6.700000E-7,6.500000E-7,6.400000E-7,6.200000E-7,6.100000E-7,5.900000E-7,5.800000E-7,5.700000E-7,5.600000E-7,5.400000E-7,5.300000E-7,5.200000E-7,5.100000E-7,5.000000E-7,4.900000E-7,4.800000E-7,4.700000E-7,4.600000E-7,4.500000E-7,4.400000E-7,4.400000E-7,4.300000E-7,4.200000E-7,4.200000E-7,4.100000E-7,4.000000E-7,4.000000E-7,3.900000E-7,3.900000E-7,3.900000E-7,3.800000E-7,3.800000E-7,3.800000E-7,3.700000E-7,3.700000E-7,3.700000E-7,3.700000E-7,3.700000E-7,3.700000E-7,3.700000E-7,3.700000E-7,3.700000E-7,3.700000E-7,3.700000E-7,3.700000E-7,3.700000E-7,3.800000E-7,3.800000E-7,3.800000E-7,3.900000E-7,3.900000E-7,4.000000E-7,3.900000E-7,3.900000E-7,3.900000E-7,3.900000E-7,3.900000E-7,3.800000E-7,3.800000E-7,3.800000E-7,3.800000E-7,3.800000E-7,3.700000E-7,3.700000E-7,3.700000E-7,3.700000E-7,3.600000E-7,3.600000E-7,3.600000E-7,3.600000E-7,3.600000E-7,3.500000E-7,3.500000E-7,3.500000E-7,3.500000E-7,3.500000E-7,3.500000E-7,3.400000E-7,3.400000E-7,3.400000E-7,3.400000E-7,3.400000E-7,3.300000E-7,3.300000E-7,3.300000E-7,3.300000E-7,3.300000E-7,3.200000E-7,3.200000E-7,3.200000E-7,3.200000E-7,3.200000E-7,3.200000E-7,3.100000E-7,3.100000E-7,3.100000E-7,3.100000E-7,3.100000E-7,3.100000E-7,3.000000E-7,3.000000E-7,3.000000E-7,3.000000E-7,3.000000E-7,3.000000E-7,3.000000E-7,2.900000E-7,2.900000E-7,2.900000E-7,2.900000E-7,2.900000E-7,2.900000E-7,2.800000E-7,2.800000E-7,2.800000E-7,2.800000E-7,2.800000E-7,2.800000E-7,2.800000E-7,2.700000E-7,2.700000E-7,2.700000E-7,2.700000E-7,2.700000E-7,2.700000E-7,2.700000E-7,2.700000E-7,2.600000E-7,2.600000E-7,2.600000E-7,2.600000E-7,2.600000E-7,2.600000E-7,2.600000E-7,2.600000E-7,2.500000E-7,2.500000E-7,2.500000E-7,2.500000E-7,2.500000E-7,2.500000E-7,2.500000E-7,2.500000E-7,2.400000E-7,2.400000E-7,2.400000E-7,2.400000E-7,2.400000E-7,2.400000E-7,2.400000E-7,2.400000E-7,2.400000E-7,2.400000E-7,2.300000E-7,2.300000E-7,2.300000E-7,2.300000E-7,2.300000E-7,2.300000E-7,2.300000E-7,2.300000E-7,2.300000E-7,2.300000E-7,2.300000E-7,2.200000E-7,2.200000E-7,2.200000E-7,2.200000E-7,2.200000E-7,2.200000E-7,2.200000E-7,2.200000E-7])
+        self.specific_heat.data       = np.array([1.500000E+3,1.500000E+3,1.500000E+3,1.500000E+3,1.500000E+3,1.500000E+3,1.500000E+3,1.500000E+3,1.500000E+3,1.500000E+3,1.500000E+3,1.500000E+3,1.500000E+3,1.500000E+3,1.500000E+3,1.500000E+3,1.500000E+3,1.500000E+3,1.500000E+3,1.500000E+3,1.500000E+3,1.500000E+3,1.500000E+3,1.600000E+3,1.600000E+3,1.600000E+3,1.600000E+3,1.600000E+3,1.600000E+3,1.600000E+3,1.600000E+3,1.600000E+3,1.600000E+3,1.600000E+3,1.600000E+3,1.600000E+3,1.600000E+3,1.600000E+3,1.600000E+3,1.600000E+3,1.600000E+3,1.600000E+3,1.600000E+3,1.600000E+3,1.600000E+3,1.600000E+3,1.600000E+3,1.600000E+3,1.600000E+3,1.600000E+3,1.700000E+3,1.700000E+3,1.700000E+3,1.700000E+3,1.700000E+3,1.700000E+3,1.700000E+3,1.700000E+3,1.700000E+3,1.700000E+3,1.700000E+3,1.700000E+3,1.700000E+3,1.700000E+3,1.700000E+3,1.700000E+3,1.700000E+3,1.700000E+3,1.700000E+3,1.700000E+3,1.700000E+3,1.700000E+3,1.700000E+3,1.700000E+3,1.800000E+3,1.800000E+3,1.800000E+3,1.800000E+3,1.800000E+3,1.800000E+3,1.800000E+3,1.800000E+3,1.800000E+3,1.800000E+3,1.800000E+3,1.800000E+3,1.800000E+3,1.800000E+3,1.800000E+3,1.800000E+3,1.800000E+3,1.800000E+3,1.800000E+3,1.800000E+3,1.800000E+3,1.800000E+3,1.800000E+3,1.800000E+3,1.800000E+3,1.800000E+3,1.800000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3])
+        self.conductivity.data        = np.array([1.500000E-1,1.490000E-1,1.490000E-1,1.490000E-1,1.490000E-1,1.490000E-1,1.490000E-1,1.490000E-1,1.490000E-1,1.490000E-1,1.490000E-1,1.490000E-1,1.490000E-1,1.480000E-1,1.480000E-1,1.480000E-1,1.480000E-1,1.480000E-1,1.480000E-1,1.480000E-1,1.480000E-1,1.480000E-1,1.480000E-1,1.480000E-1,1.480000E-1,1.480000E-1,1.470000E-1,1.470000E-1,1.470000E-1,1.470000E-1,1.470000E-1,1.470000E-1,1.470000E-1,1.470000E-1,1.470000E-1,1.470000E-1,1.470000E-1,1.470000E-1,1.460000E-1,1.460000E-1,1.460000E-1,1.460000E-1,1.460000E-1,1.460000E-1,1.460000E-1,1.460000E-1,1.460000E-1,1.460000E-1,1.460000E-1,1.460000E-1,1.460000E-1,1.450000E-1,1.450000E-1,1.450000E-1,1.450000E-1,1.450000E-1,1.450000E-1,1.450000E-1,1.450000E-1,1.450000E-1,1.450000E-1,1.450000E-1,1.450000E-1,1.440000E-1,1.440000E-1,1.440000E-1,1.440000E-1,1.440000E-1,1.440000E-1,1.440000E-1,1.440000E-1,1.440000E-1,1.440000E-1,1.440000E-1,1.440000E-1,1.440000E-1,1.430000E-1,1.430000E-1,1.430000E-1,1.430000E-1,1.430000E-1,1.430000E-1,1.430000E-1,1.430000E-1,1.430000E-1,1.430000E-1,1.430000E-1,1.430000E-1,1.420000E-1,1.420000E-1,1.420000E-1,1.420000E-1,1.420000E-1,1.420000E-1,1.420000E-1,1.420000E-1,1.420000E-1,1.420000E-1,1.420000E-1,1.420000E-1,1.420000E-1,1.410000E-1,1.410000E-1,1.410000E-1,1.410000E-1,1.410000E-1,1.410000E-1,1.410000E-1,1.410000E-1,1.410000E-1,1.410000E-1,1.410000E-1,1.410000E-1,1.400000E-1,1.400000E-1,1.400000E-1,1.400000E-1,1.400000E-1,1.400000E-1,1.400000E-1,1.400000E-1,1.400000E-1,1.400000E-1,1.400000E-1,1.400000E-1,1.390000E-1,1.390000E-1,1.390000E-1,1.390000E-1,1.390000E-1,1.390000E-1,1.390000E-1,1.390000E-1,1.390000E-1,1.390000E-1,1.390000E-1,1.390000E-1,1.390000E-1,1.380000E-1,1.380000E-1,1.380000E-1,1.380000E-1,1.380000E-1,1.380000E-1,1.380000E-1,1.380000E-1,1.380000E-1,1.380000E-1,1.380000E-1,1.380000E-1,1.370000E-1,1.370000E-1,1.370000E-1,1.370000E-1,1.370000E-1,1.370000E-1,1.370000E-1,1.370000E-1,1.370000E-1,1.370000E-1,1.370000E-1,1.370000E-1,1.370000E-1,1.360000E-1,1.360000E-1,1.360000E-1,1.360000E-1,1.360000E-1,1.360000E-1,1.360000E-1,1.360000E-1,1.360000E-1,1.360000E-1,1.360000E-1,1.360000E-1,1.350000E-1,1.350000E-1,1.350000E-1,1.350000E-1,1.350000E-1,1.350000E-1,1.350000E-1,1.350000E-1,1.350000E-1,1.350000E-1,1.350000E-1,1.350000E-1,1.350000E-1,1.340000E-1,1.340000E-1,1.340000E-1,1.340000E-1,1.340000E-1,1.340000E-1,1.340000E-1,1.340000E-1,1.340000E-1,1.340000E-1,1.340000E-1,1.340000E-1,1.330000E-1,1.330000E-1,1.330000E-1,1.330000E-1,1.330000E-1,1.330000E-1,1.330000E-1,1.330000E-1,1.330000E-1,1.330000E-1,1.330000E-1,1.330000E-1,1.330000E-1,1.320000E-1,1.320000E-1,1.320000E-1,1.320000E-1,1.320000E-1,1.320000E-1,1.320000E-1,1.320000E-1,1.320000E-1,1.320000E-1,1.320000E-1,1.320000E-1,1.310000E-1,1.310000E-1,1.310000E-1,1.310000E-1,1.310000E-1,1.310000E-1,1.310000E-1,1.310000E-1,1.310000E-1,1.310000E-1,1.310000E-1,1.310000E-1,1.310000E-1,1.300000E-1,1.300000E-1,1.300000E-1,1.300000E-1,1.300000E-1,1.300000E-1,1.300000E-1,1.300000E-1,1.300000E-1,1.300000E-1,1.300000E-1,1.300000E-1,1.290000E-1,1.290000E-1,1.290000E-1,1.290000E-1,1.290000E-1,1.290000E-1,1.290000E-1,1.290000E-1,1.290000E-1,1.290000E-1,1.290000E-1,1.290000E-1,1.290000E-1,1.280000E-1,1.280000E-1,1.280000E-1,1.280000E-1,1.280000E-1,1.280000E-1,1.280000E-1,1.280000E-1,1.280000E-1,1.280000E-1,1.280000E-1,1.280000E-1,1.270000E-1,1.270000E-1,1.270000E-1,1.270000E-1,1.270000E-1,1.270000E-1,1.270000E-1,1.270000E-1,1.270000E-1,1.270000E-1,1.270000E-1,1.270000E-1,1.270000E-1,1.260000E-1,1.260000E-1,1.260000E-1,1.260000E-1,1.260000E-1,1.260000E-1,1.260000E-1,1.260000E-1,1.260000E-1,1.260000E-1,1.260000E-1,1.260000E-1,1.250000E-1,1.250000E-1,1.250000E-1,1.250000E-1,1.250000E-1,1.250000E-1,1.250000E-1,1.250000E-1,1.250000E-1,1.250000E-1,1.250000E-1,1.250000E-1,1.250000E-1,1.240000E-1,1.240000E-1,1.240000E-1,1.240000E-1,1.240000E-1,1.240000E-1,1.240000E-1,1.240000E-1])
+        self.saturation_pressure.data = np.array([     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,3.000000E+3,4.000000E+3,5.000000E+3,5.000000E+3,6.000000E+3,7.000000E+3,7.000000E+3,8.000000E+3,9.000000E+3,1.000000E+4,1.000000E+4,1.100000E+4,1.200000E+4,1.300000E+4,1.400000E+4,1.400000E+4,1.500000E+4,1.600000E+4,1.700000E+4,1.800000E+4,1.800000E+4,1.900000E+4,2.000000E+4,2.100000E+4,2.200000E+4,2.300000E+4,2.300000E+4,2.400000E+4,2.500000E+4,2.600000E+4,2.700000E+4,2.800000E+4,2.900000E+4,3.000000E+4,3.000000E+4,3.100000E+4,3.200000E+4,3.300000E+4,3.400000E+4,3.500000E+4,3.600000E+4,3.700000E+4,3.800000E+4,3.900000E+4,4.000000E+4,4.100000E+4,4.200000E+4,4.300000E+4,4.400000E+4,4.500000E+4,4.600000E+4,4.700000E+4,4.800000E+4,4.900000E+4,5.000000E+4,5.100000E+4,5.200000E+4,5.300000E+4,5.400000E+4,5.500000E+4,5.600000E+4,5.700000E+4,5.800000E+4,5.900000E+4,6.000000E+4,6.100000E+4,6.200000E+4,6.300000E+4,6.400000E+4,6.600000E+4,6.700000E+4,6.800000E+4,6.900000E+4,7.000000E+4,7.100000E+4,7.200000E+4,7.300000E+4,7.500000E+4,7.600000E+4,7.700000E+4,7.800000E+4,7.900000E+4,8.000000E+4,8.200000E+4,8.300000E+4,8.400000E+4,8.500000E+4,8.600000E+4,8.800000E+4,8.900000E+4,9.000000E+4,9.100000E+4,9.200000E+4,9.400000E+4,9.500000E+4,9.600000E+4,9.700000E+4,9.900000E+4,1.000000E+5,1.010000E+5,1.030000E+5,1.040000E+5,1.050000E+5,1.060000E+5,1.080000E+5,1.090000E+5,1.100000E+5,1.120000E+5,1.130000E+5,1.140000E+5,1.160000E+5,1.170000E+5,1.180000E+5,1.200000E+5,1.210000E+5,1.220000E+5,1.240000E+5,1.250000E+5,1.270000E+5,1.280000E+5,1.290000E+5,1.310000E+5,1.320000E+5,1.340000E+5,1.350000E+5,1.370000E+5,1.380000E+5,1.390000E+5,1.410000E+5,1.420000E+5,1.440000E+5,1.450000E+5,1.470000E+5,1.480000E+5,1.500000E+5,1.510000E+5])
+        self.Tmin        = np.min(self.temperature.data)
+        self.Tmax        = np.max(self.temperature.data)
+        self.TminPsat    = np.min(self.temperature.data[~np.isnan(self.saturation_pressure.data)])
+        self.name        = "PCR"
+        self.description = "Paratherm "+ self.name[1:]
+        self.reference   = "Paratherm2013"
+        self.reshapeAll()
+
+
+
+
+class PGLT(PureData):
+    """
+    Heat transfer fluid Paratherm GLT The Paratherm GLT heat transfer fluid is
+    an alkylated-aromatic based heat transfer fluid formulated for closed-loop
+    liquid-phase heating systems to 550 F using fired heaters and to 575 F in
+    waste-heat recovery systems.
+    """
+    def __init__(self):
+        PureData.__init__(self)
+        self.density.source           = self.density.SOURCE_DATA
+        self.viscosity.source         = self.viscosity.SOURCE_DATA
+        self.specific_heat.source     = self.specific_heat.SOURCE_DATA
+        self.conductivity.source      = self.conductivity.SOURCE_DATA
+        self.saturation_pressure.source = self.saturation_pressure.SOURCE_DATA
+        self.temperature.data         = np.array([2.581500E+2,2.591500E+2,2.601500E+2,2.611500E+2,2.621500E+2,2.631500E+2,2.641500E+2,2.651500E+2,2.661500E+2,2.671500E+2,2.681500E+2,2.691500E+2,2.701500E+2,2.711500E+2,2.721500E+2,2.731500E+2,2.741500E+2,2.751500E+2,2.761500E+2,2.771500E+2,2.781500E+2,2.791500E+2,2.801500E+2,2.811500E+2,2.821500E+2,2.831500E+2,2.841500E+2,2.851500E+2,2.861500E+2,2.871500E+2,2.881500E+2,2.891500E+2,2.901500E+2,2.911500E+2,2.921500E+2,2.931500E+2,2.941500E+2,2.951500E+2,2.961500E+2,2.971500E+2,2.981500E+2,2.991500E+2,3.001500E+2,3.011500E+2,3.021500E+2,3.031500E+2,3.041500E+2,3.051500E+2,3.061500E+2,3.071500E+2,3.081500E+2,3.091500E+2,3.101500E+2,3.111500E+2,3.121500E+2,3.131500E+2,3.141500E+2,3.151500E+2,3.161500E+2,3.171500E+2,3.181500E+2,3.191500E+2,3.201500E+2,3.211500E+2,3.221500E+2,3.231500E+2,3.241500E+2,3.251500E+2,3.261500E+2,3.271500E+2,3.281500E+2,3.291500E+2,3.301500E+2,3.311500E+2,3.321500E+2,3.331500E+2,3.341500E+2,3.351500E+2,3.361500E+2,3.371500E+2,3.381500E+2,3.391500E+2,3.401500E+2,3.411500E+2,3.421500E+2,3.431500E+2,3.441500E+2,3.451500E+2,3.461500E+2,3.471500E+2,3.481500E+2,3.491500E+2,3.501500E+2,3.511500E+2,3.521500E+2,3.531500E+2,3.541500E+2,3.551500E+2,3.561500E+2,3.571500E+2,3.581500E+2,3.591500E+2,3.601500E+2,3.611500E+2,3.621500E+2,3.631500E+2,3.641500E+2,3.651500E+2,3.661500E+2,3.671500E+2,3.681500E+2,3.691500E+2,3.701500E+2,3.711500E+2,3.721500E+2,3.731500E+2,3.741500E+2,3.751500E+2,3.761500E+2,3.771500E+2,3.781500E+2,3.791500E+2,3.801500E+2,3.811500E+2,3.821500E+2,3.831500E+2,3.841500E+2,3.851500E+2,3.861500E+2,3.871500E+2,3.881500E+2,3.891500E+2,3.901500E+2,3.911500E+2,3.921500E+2,3.931500E+2,3.941500E+2,3.951500E+2,3.961500E+2,3.971500E+2,3.981500E+2,3.991500E+2,4.001500E+2,4.011500E+2,4.021500E+2,4.031500E+2,4.041500E+2,4.051500E+2,4.061500E+2,4.071500E+2,4.081500E+2,4.091500E+2,4.101500E+2,4.111500E+2,4.121500E+2,4.131500E+2,4.141500E+2,4.151500E+2,4.161500E+2,4.171500E+2,4.181500E+2,4.191500E+2,4.201500E+2,4.211500E+2,4.221500E+2,4.231500E+2,4.241500E+2,4.251500E+2,4.261500E+2,4.271500E+2,4.281500E+2,4.291500E+2,4.301500E+2,4.311500E+2,4.321500E+2,4.331500E+2,4.341500E+2,4.351500E+2,4.361500E+2,4.371500E+2,4.381500E+2,4.391500E+2,4.401500E+2,4.411500E+2,4.421500E+2,4.431500E+2,4.441500E+2,4.451500E+2,4.461500E+2,4.471500E+2,4.481500E+2,4.491500E+2,4.501500E+2,4.511500E+2,4.521500E+2,4.531500E+2,4.541500E+2,4.551500E+2,4.561500E+2,4.571500E+2,4.581500E+2,4.591500E+2,4.601500E+2,4.611500E+2,4.621500E+2,4.631500E+2,4.641500E+2,4.651500E+2,4.661500E+2,4.671500E+2,4.681500E+2,4.691500E+2,4.701500E+2,4.711500E+2,4.721500E+2,4.731500E+2,4.741500E+2,4.751500E+2,4.761500E+2,4.771500E+2,4.781500E+2,4.791500E+2,4.801500E+2,4.811500E+2,4.821500E+2,4.831500E+2,4.841500E+2,4.851500E+2,4.861500E+2,4.871500E+2,4.881500E+2,4.891500E+2,4.901500E+2,4.911500E+2,4.921500E+2,4.931500E+2,4.941500E+2,4.951500E+2,4.961500E+2,4.971500E+2,4.981500E+2,4.991500E+2,5.001500E+2,5.011500E+2,5.021500E+2,5.031500E+2,5.041500E+2,5.051500E+2,5.061500E+2,5.071500E+2,5.081500E+2,5.091500E+2,5.101500E+2,5.111500E+2,5.121500E+2,5.131500E+2,5.141500E+2,5.151500E+2,5.161500E+2,5.171500E+2,5.181500E+2,5.191500E+2,5.201500E+2,5.211500E+2,5.221500E+2,5.231500E+2,5.241500E+2,5.251500E+2,5.261500E+2,5.271500E+2,5.281500E+2,5.291500E+2,5.301500E+2,5.311500E+2,5.321500E+2,5.331500E+2,5.341500E+2,5.351500E+2,5.361500E+2,5.371500E+2,5.381500E+2,5.391500E+2,5.401500E+2,5.411500E+2,5.421500E+2,5.431500E+2,5.441500E+2,5.451500E+2,5.461500E+2,5.471500E+2,5.481500E+2,5.491500E+2,5.501500E+2,5.511500E+2,5.521500E+2,5.531500E+2,5.541500E+2,5.551500E+2,5.561500E+2,5.571500E+2,5.581500E+2,5.591500E+2,5.601500E+2,5.611500E+2,5.621500E+2,5.631500E+2,5.641500E+2,5.651500E+2,5.661500E+2,5.671500E+2,5.681500E+2,5.691500E+2,5.701500E+2,5.711500E+2,5.721500E+2,5.731500E+2,5.741500E+2,5.751500E+2,5.761500E+2,5.771500E+2,5.781500E+2,5.791500E+2,5.801500E+2,5.811500E+2,5.821500E+2,5.831500E+2,5.841500E+2,5.851500E+2,5.861500E+2,5.871500E+2,5.881500E+2])
+        self.density.data             = np.array([9.020000E+2,9.010000E+2,9.000000E+2,9.000000E+2,8.990000E+2,8.980000E+2,8.970000E+2,8.970000E+2,8.960000E+2,8.950000E+2,8.950000E+2,8.940000E+2,8.930000E+2,8.930000E+2,8.920000E+2,8.910000E+2,8.910000E+2,8.900000E+2,8.890000E+2,8.880000E+2,8.880000E+2,8.870000E+2,8.860000E+2,8.860000E+2,8.850000E+2,8.840000E+2,8.840000E+2,8.830000E+2,8.820000E+2,8.810000E+2,8.810000E+2,8.800000E+2,8.790000E+2,8.790000E+2,8.780000E+2,8.770000E+2,8.770000E+2,8.760000E+2,8.750000E+2,8.740000E+2,8.740000E+2,8.730000E+2,8.720000E+2,8.720000E+2,8.710000E+2,8.700000E+2,8.700000E+2,8.690000E+2,8.680000E+2,8.680000E+2,8.670000E+2,8.660000E+2,8.650000E+2,8.650000E+2,8.640000E+2,8.630000E+2,8.630000E+2,8.620000E+2,8.610000E+2,8.610000E+2,8.600000E+2,8.590000E+2,8.580000E+2,8.580000E+2,8.570000E+2,8.560000E+2,8.560000E+2,8.550000E+2,8.540000E+2,8.540000E+2,8.530000E+2,8.520000E+2,8.510000E+2,8.510000E+2,8.500000E+2,8.490000E+2,8.490000E+2,8.480000E+2,8.470000E+2,8.470000E+2,8.460000E+2,8.450000E+2,8.440000E+2,8.440000E+2,8.430000E+2,8.420000E+2,8.420000E+2,8.410000E+2,8.400000E+2,8.400000E+2,8.390000E+2,8.380000E+2,8.380000E+2,8.370000E+2,8.360000E+2,8.350000E+2,8.350000E+2,8.340000E+2,8.330000E+2,8.330000E+2,8.320000E+2,8.310000E+2,8.310000E+2,8.300000E+2,8.290000E+2,8.280000E+2,8.280000E+2,8.270000E+2,8.260000E+2,8.260000E+2,8.250000E+2,8.240000E+2,8.240000E+2,8.230000E+2,8.220000E+2,8.210000E+2,8.210000E+2,8.200000E+2,8.190000E+2,8.190000E+2,8.180000E+2,8.170000E+2,8.170000E+2,8.160000E+2,8.150000E+2,8.150000E+2,8.140000E+2,8.130000E+2,8.120000E+2,8.120000E+2,8.110000E+2,8.100000E+2,8.100000E+2,8.090000E+2,8.080000E+2,8.080000E+2,8.070000E+2,8.060000E+2,8.050000E+2,8.050000E+2,8.040000E+2,8.030000E+2,8.030000E+2,8.020000E+2,8.010000E+2,8.010000E+2,8.000000E+2,7.990000E+2,7.980000E+2,7.980000E+2,7.970000E+2,7.960000E+2,7.960000E+2,7.950000E+2,7.940000E+2,7.940000E+2,7.930000E+2,7.920000E+2,7.910000E+2,7.910000E+2,7.900000E+2,7.890000E+2,7.890000E+2,7.880000E+2,7.870000E+2,7.870000E+2,7.860000E+2,7.850000E+2,7.850000E+2,7.840000E+2,7.830000E+2,7.820000E+2,7.820000E+2,7.810000E+2,7.800000E+2,7.800000E+2,7.790000E+2,7.780000E+2,7.780000E+2,7.770000E+2,7.760000E+2,7.750000E+2,7.750000E+2,7.740000E+2,7.730000E+2,7.730000E+2,7.720000E+2,7.710000E+2,7.710000E+2,7.700000E+2,7.690000E+2,7.680000E+2,7.680000E+2,7.670000E+2,7.660000E+2,7.660000E+2,7.650000E+2,7.640000E+2,7.640000E+2,7.630000E+2,7.620000E+2,7.620000E+2,7.610000E+2,7.600000E+2,7.590000E+2,7.590000E+2,7.580000E+2,7.570000E+2,7.570000E+2,7.560000E+2,7.550000E+2,7.550000E+2,7.540000E+2,7.530000E+2,7.520000E+2,7.520000E+2,7.510000E+2,7.500000E+2,7.500000E+2,7.490000E+2,7.480000E+2,7.480000E+2,7.470000E+2,7.460000E+2,7.450000E+2,7.450000E+2,7.440000E+2,7.430000E+2,7.430000E+2,7.420000E+2,7.410000E+2,7.410000E+2,7.400000E+2,7.390000E+2,7.380000E+2,7.380000E+2,7.370000E+2,7.360000E+2,7.360000E+2,7.350000E+2,7.340000E+2,7.340000E+2,7.330000E+2,7.320000E+2,7.320000E+2,7.310000E+2,7.300000E+2,7.290000E+2,7.290000E+2,7.280000E+2,7.270000E+2,7.270000E+2,7.260000E+2,7.250000E+2,7.250000E+2,7.240000E+2,7.230000E+2,7.220000E+2,7.220000E+2,7.210000E+2,7.200000E+2,7.200000E+2,7.190000E+2,7.180000E+2,7.180000E+2,7.170000E+2,7.160000E+2,7.150000E+2,7.150000E+2,7.140000E+2,7.130000E+2,7.130000E+2,7.120000E+2,7.110000E+2,7.110000E+2,7.100000E+2,7.090000E+2,7.090000E+2,7.080000E+2,7.070000E+2,7.060000E+2,7.060000E+2,7.050000E+2,7.040000E+2,7.040000E+2,7.030000E+2,7.020000E+2,7.020000E+2,7.010000E+2,7.000000E+2,6.990000E+2,6.990000E+2,6.980000E+2,6.970000E+2,6.970000E+2,6.960000E+2,6.950000E+2,6.950000E+2,6.940000E+2,6.930000E+2,6.920000E+2,6.920000E+2,6.910000E+2,6.900000E+2,6.900000E+2,6.890000E+2,6.880000E+2,6.880000E+2,6.870000E+2,6.860000E+2,6.850000E+2,6.850000E+2,6.840000E+2,6.830000E+2,6.830000E+2,6.820000E+2,6.810000E+2,6.810000E+2,6.800000E+2,6.790000E+2,6.790000E+2,6.780000E+2,6.770000E+2,6.760000E+2,6.760000E+2,6.750000E+2,6.740000E+2,6.740000E+2,6.730000E+2,6.720000E+2,6.720000E+2])
+        self.viscosity.data           = np.array([5.620000E-4,5.120000E-4,4.660000E-4,4.240000E-4,3.860000E-4,3.500000E-4,3.180000E-4,2.890000E-4,2.620000E-4,2.380000E-4,2.170000E-4,1.980000E-4,1.810000E-4,1.660000E-4,1.530000E-4,1.380000E-4,1.300000E-4,1.230000E-4,1.160000E-4,1.090000E-4,1.020000E-4,9.600000E-5,9.100000E-5,8.500000E-5,8.000000E-5,7.600000E-5,7.100000E-5,6.700000E-5,6.300000E-5,5.900000E-5,5.600000E-5,5.300000E-5,5.000000E-5,4.700000E-5,4.400000E-5,4.200000E-5,4.000000E-5,3.800000E-5,3.600000E-5,3.400000E-5,3.200000E-5,3.100000E-5,2.900000E-5,2.800000E-5,2.700000E-5,2.500000E-5,2.400000E-5,2.300000E-5,2.200000E-5,2.100000E-5,2.000000E-5,1.900000E-5,1.800000E-5,1.700000E-5,1.600000E-5,1.600000E-5,1.500000E-5,1.500000E-5,1.400000E-5,1.300000E-5,1.300000E-5,1.300000E-5,1.200000E-5,1.200000E-5,1.100000E-5,1.100000E-5,1.100000E-5,1.000000E-5,9.900000E-6,9.600000E-6,9.200000E-6,8.900000E-6,8.600000E-6,8.300000E-6,8.100000E-6,7.800000E-6,7.600000E-6,7.300000E-6,7.100000E-6,6.900000E-6,6.700000E-6,6.500000E-6,6.300000E-6,6.100000E-6,5.900000E-6,5.800000E-6,5.600000E-6,5.400000E-6,5.300000E-6,5.200000E-6,5.000000E-6,4.900000E-6,4.800000E-6,4.700000E-6,4.600000E-6,4.400000E-6,4.300000E-6,4.200000E-6,4.100000E-6,4.000000E-6,3.900000E-6,3.800000E-6,3.800000E-6,3.700000E-6,3.600000E-6,3.500000E-6,3.400000E-6,3.400000E-6,3.300000E-6,3.200000E-6,3.200000E-6,3.100000E-6,3.000000E-6,3.000000E-6,2.900000E-6,2.900000E-6,2.800000E-6,2.800000E-6,2.700000E-6,2.700000E-6,2.600000E-6,2.500000E-6,2.500000E-6,2.500000E-6,2.400000E-6,2.400000E-6,2.300000E-6,2.300000E-6,2.200000E-6,2.200000E-6,2.200000E-6,2.100000E-6,2.100000E-6,2.100000E-6,2.000000E-6,2.000000E-6,2.000000E-6,1.900000E-6,1.900000E-6,1.900000E-6,1.900000E-6,1.800000E-6,1.800000E-6,1.800000E-6,1.700000E-6,1.700000E-6,1.700000E-6,1.700000E-6,1.600000E-6,1.600000E-6,1.600000E-6,1.600000E-6,1.500000E-6,1.500000E-6,1.500000E-6,1.500000E-6,1.500000E-6,1.400000E-6,1.400000E-6,1.400000E-6,1.400000E-6,1.400000E-6,1.400000E-6,1.300000E-6,1.300000E-6,1.300000E-6,1.300000E-6,1.300000E-6,1.200000E-6,1.200000E-6,1.200000E-6,1.200000E-6,1.200000E-6,1.200000E-6,1.100000E-6,1.100000E-6,1.100000E-6,1.100000E-6,1.100000E-6,1.100000E-6,1.100000E-6,1.100000E-6,1.000000E-6,1.000000E-6,1.000000E-6,1.000000E-6,1.000000E-6,9.900000E-7,9.800000E-7,9.700000E-7,9.600000E-7,9.500000E-7,9.400000E-7,9.300000E-7,9.200000E-7,9.100000E-7,9.000000E-7,8.900000E-7,8.800000E-7,8.700000E-7,8.600000E-7,8.500000E-7,8.400000E-7,8.300000E-7,8.300000E-7,8.200000E-7,8.100000E-7,8.000000E-7,7.900000E-7,7.800000E-7,7.800000E-7,7.700000E-7,7.600000E-7,7.500000E-7,7.500000E-7,7.400000E-7,7.300000E-7,7.200000E-7,7.200000E-7,7.100000E-7,7.000000E-7,7.000000E-7,6.900000E-7,6.800000E-7,6.800000E-7,6.700000E-7,6.600000E-7,6.600000E-7,6.500000E-7,6.500000E-7,6.400000E-7,6.300000E-7,6.300000E-7,6.200000E-7,6.200000E-7,6.100000E-7,6.100000E-7,6.000000E-7,6.000000E-7,5.900000E-7,5.800000E-7,5.800000E-7,5.700000E-7,5.700000E-7,5.600000E-7,5.600000E-7,5.600000E-7,5.500000E-7,5.500000E-7,5.400000E-7,5.400000E-7,5.300000E-7,5.300000E-7,5.200000E-7,5.200000E-7,5.100000E-7,5.100000E-7,5.100000E-7,5.000000E-7,5.000000E-7,4.900000E-7,4.900000E-7,4.900000E-7,4.800000E-7,4.800000E-7,4.700000E-7,4.700000E-7,4.700000E-7,4.600000E-7,4.600000E-7,4.600000E-7,4.500000E-7,4.500000E-7,4.500000E-7,4.400000E-7,4.400000E-7,4.400000E-7,4.300000E-7,4.300000E-7,4.300000E-7,4.200000E-7,4.200000E-7,4.200000E-7,4.100000E-7,4.100000E-7,4.100000E-7,4.000000E-7,4.000000E-7,4.000000E-7,4.000000E-7,3.900000E-7,3.900000E-7,3.900000E-7,3.800000E-7,3.800000E-7,3.800000E-7,3.800000E-7,3.700000E-7,3.700000E-7,3.700000E-7,3.700000E-7,3.600000E-7,3.600000E-7,3.600000E-7,3.600000E-7,3.500000E-7,3.500000E-7,3.500000E-7,3.500000E-7,3.400000E-7,3.400000E-7,3.400000E-7,3.400000E-7,3.400000E-7,3.300000E-7,3.300000E-7,3.300000E-7,3.300000E-7,3.200000E-7,3.200000E-7,3.200000E-7,3.200000E-7,3.200000E-7,3.100000E-7,3.100000E-7,3.100000E-7,3.100000E-7,3.100000E-7,3.000000E-7,3.000000E-7,3.000000E-7])
+        self.specific_heat.data       = np.array([1.800000E+3,1.800000E+3,1.800000E+3,1.800000E+3,1.800000E+3,1.800000E+3,1.800000E+3,1.800000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.900000E+3,2.900000E+3,2.900000E+3,2.900000E+3,2.900000E+3,2.900000E+3,2.900000E+3])
+        self.conductivity.data        = np.array([1.360000E-1,1.360000E-1,1.360000E-1,1.360000E-1,1.360000E-1,1.360000E-1,1.360000E-1,1.360000E-1,1.350000E-1,1.350000E-1,1.350000E-1,1.350000E-1,1.350000E-1,1.350000E-1,1.350000E-1,1.350000E-1,1.350000E-1,1.350000E-1,1.350000E-1,1.350000E-1,1.340000E-1,1.340000E-1,1.340000E-1,1.340000E-1,1.340000E-1,1.340000E-1,1.340000E-1,1.340000E-1,1.340000E-1,1.340000E-1,1.340000E-1,1.340000E-1,1.340000E-1,1.330000E-1,1.330000E-1,1.330000E-1,1.330000E-1,1.330000E-1,1.330000E-1,1.330000E-1,1.330000E-1,1.330000E-1,1.330000E-1,1.330000E-1,1.330000E-1,1.320000E-1,1.320000E-1,1.320000E-1,1.320000E-1,1.320000E-1,1.320000E-1,1.320000E-1,1.320000E-1,1.320000E-1,1.320000E-1,1.320000E-1,1.320000E-1,1.320000E-1,1.310000E-1,1.310000E-1,1.310000E-1,1.310000E-1,1.310000E-1,1.310000E-1,1.310000E-1,1.310000E-1,1.310000E-1,1.310000E-1,1.310000E-1,1.310000E-1,1.300000E-1,1.300000E-1,1.300000E-1,1.300000E-1,1.300000E-1,1.300000E-1,1.300000E-1,1.300000E-1,1.300000E-1,1.300000E-1,1.300000E-1,1.300000E-1,1.300000E-1,1.290000E-1,1.290000E-1,1.290000E-1,1.290000E-1,1.290000E-1,1.290000E-1,1.290000E-1,1.290000E-1,1.290000E-1,1.290000E-1,1.290000E-1,1.290000E-1,1.280000E-1,1.280000E-1,1.280000E-1,1.280000E-1,1.280000E-1,1.280000E-1,1.280000E-1,1.280000E-1,1.280000E-1,1.280000E-1,1.280000E-1,1.280000E-1,1.280000E-1,1.270000E-1,1.270000E-1,1.270000E-1,1.270000E-1,1.270000E-1,1.270000E-1,1.270000E-1,1.270000E-1,1.270000E-1,1.270000E-1,1.270000E-1,1.270000E-1,1.260000E-1,1.260000E-1,1.260000E-1,1.260000E-1,1.260000E-1,1.260000E-1,1.260000E-1,1.260000E-1,1.260000E-1,1.260000E-1,1.260000E-1,1.260000E-1,1.260000E-1,1.250000E-1,1.250000E-1,1.250000E-1,1.250000E-1,1.250000E-1,1.250000E-1,1.250000E-1,1.250000E-1,1.250000E-1,1.250000E-1,1.250000E-1,1.250000E-1,1.240000E-1,1.240000E-1,1.240000E-1,1.240000E-1,1.240000E-1,1.240000E-1,1.240000E-1,1.240000E-1,1.240000E-1,1.240000E-1,1.240000E-1,1.240000E-1,1.240000E-1,1.230000E-1,1.230000E-1,1.230000E-1,1.230000E-1,1.230000E-1,1.230000E-1,1.230000E-1,1.230000E-1,1.230000E-1,1.230000E-1,1.230000E-1,1.230000E-1,1.220000E-1,1.220000E-1,1.220000E-1,1.220000E-1,1.220000E-1,1.220000E-1,1.220000E-1,1.220000E-1,1.220000E-1,1.220000E-1,1.220000E-1,1.220000E-1,1.220000E-1,1.210000E-1,1.210000E-1,1.210000E-1,1.210000E-1,1.210000E-1,1.210000E-1,1.210000E-1,1.210000E-1,1.210000E-1,1.210000E-1,1.210000E-1,1.210000E-1,1.200000E-1,1.200000E-1,1.200000E-1,1.200000E-1,1.200000E-1,1.200000E-1,1.200000E-1,1.200000E-1,1.200000E-1,1.200000E-1,1.200000E-1,1.200000E-1,1.200000E-1,1.190000E-1,1.190000E-1,1.190000E-1,1.190000E-1,1.190000E-1,1.190000E-1,1.190000E-1,1.190000E-1,1.190000E-1,1.190000E-1,1.190000E-1,1.190000E-1,1.180000E-1,1.180000E-1,1.180000E-1,1.180000E-1,1.180000E-1,1.180000E-1,1.180000E-1,1.180000E-1,1.180000E-1,1.180000E-1,1.180000E-1,1.180000E-1,1.180000E-1,1.170000E-1,1.170000E-1,1.170000E-1,1.170000E-1,1.170000E-1,1.170000E-1,1.170000E-1,1.170000E-1,1.170000E-1,1.170000E-1,1.170000E-1,1.170000E-1,1.160000E-1,1.160000E-1,1.160000E-1,1.160000E-1,1.160000E-1,1.160000E-1,1.160000E-1,1.160000E-1,1.160000E-1,1.160000E-1,1.160000E-1,1.160000E-1,1.160000E-1,1.150000E-1,1.150000E-1,1.150000E-1,1.150000E-1,1.150000E-1,1.150000E-1,1.150000E-1,1.150000E-1,1.150000E-1,1.150000E-1,1.150000E-1,1.150000E-1,1.140000E-1,1.140000E-1,1.140000E-1,1.140000E-1,1.140000E-1,1.140000E-1,1.140000E-1,1.140000E-1,1.140000E-1,1.140000E-1,1.140000E-1,1.140000E-1,1.140000E-1,1.130000E-1,1.130000E-1,1.130000E-1,1.130000E-1,1.130000E-1,1.130000E-1,1.130000E-1,1.130000E-1,1.130000E-1,1.130000E-1,1.130000E-1,1.130000E-1,1.120000E-1,1.120000E-1,1.120000E-1,1.120000E-1,1.120000E-1,1.120000E-1,1.120000E-1,1.120000E-1,1.120000E-1,1.120000E-1,1.120000E-1,1.120000E-1,1.120000E-1,1.110000E-1,1.110000E-1,1.110000E-1,1.110000E-1,1.110000E-1,1.110000E-1,1.110000E-1,1.110000E-1,1.110000E-1,1.110000E-1,1.110000E-1,1.110000E-1,1.100000E-1,1.100000E-1,1.100000E-1,1.100000E-1,1.100000E-1,1.100000E-1,1.100000E-1,1.100000E-1,1.100000E-1,1.100000E-1,1.100000E-1])
+        self.saturation_pressure.data = np.array([     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,3.000000E+3,3.000000E+3,3.000000E+3,3.000000E+3,3.000000E+3,3.000000E+3,3.000000E+3,3.000000E+3,3.000000E+3,3.000000E+3,3.000000E+3,4.000000E+3,4.000000E+3,4.000000E+3,4.000000E+3,4.000000E+3,4.000000E+3,4.000000E+3,4.000000E+3,4.000000E+3,5.000000E+3,5.000000E+3,5.000000E+3,5.000000E+3,5.000000E+3,5.000000E+3,5.000000E+3,6.000000E+3,6.000000E+3,6.000000E+3,6.000000E+3,6.000000E+3,6.000000E+3,7.000000E+3,7.000000E+3,7.000000E+3,7.000000E+3,7.000000E+3,8.000000E+3,8.000000E+3,8.000000E+3,8.000000E+3,8.000000E+3,9.000000E+3,9.000000E+3,9.000000E+3,9.000000E+3,9.000000E+3,1.000000E+4,1.000000E+4,1.000000E+4,1.000000E+4,1.100000E+4,1.100000E+4,1.100000E+4,1.200000E+4,1.200000E+4,1.200000E+4,1.200000E+4,1.300000E+4,1.300000E+4,1.300000E+4,1.400000E+4,1.400000E+4,1.400000E+4,1.500000E+4,1.500000E+4,1.500000E+4,1.600000E+4,1.600000E+4,1.700000E+4,1.700000E+4,1.700000E+4,1.800000E+4,1.800000E+4,1.900000E+4,1.900000E+4,1.900000E+4,2.000000E+4,2.000000E+4,2.100000E+4,2.100000E+4,2.200000E+4,2.200000E+4,2.300000E+4,2.300000E+4,2.400000E+4,2.400000E+4,2.500000E+4,2.500000E+4,2.600000E+4,2.700000E+4,2.700000E+4,2.800000E+4,2.800000E+4,2.900000E+4,3.000000E+4,3.000000E+4,3.100000E+4,3.100000E+4,3.200000E+4,3.300000E+4,3.300000E+4,3.400000E+4,3.500000E+4,3.600000E+4,3.600000E+4,3.700000E+4,3.800000E+4,3.900000E+4,3.900000E+4,4.000000E+4,4.100000E+4,4.200000E+4,4.300000E+4,4.400000E+4])
+        self.Tmin        = np.min(self.temperature.data)
+        self.Tmax        = np.max(self.temperature.data)
+        self.TminPsat    = np.min(self.temperature.data[~np.isnan(self.saturation_pressure.data)])
+        self.name        = "PGLT"
+        self.description = "Paratherm "+ self.name[1:]
+        self.reference   = "Paratherm2013"
+        self.reshapeAll()
+
+
+
+class PHE(PureData):
+    """
+    The Paratherm HE high flash and fire point heat transfer fluid is rated for
+    an optimal service range of 150 F to 600 F (66 C to 316 C). Engineered for
+    higher thermal and oxidative stability, it is efficient and cost effective.
+    Its greater purity allows it to strongly resist degradation while holding
+    thermal properties and maintaining efficiency. This provides for low
+    maintenance and solid performance over an extended operating life.
+    Non-toxic, the HE fluid is safe to use and easy to dispose. It can be
+    safely combined with spent lubricating oils and recycled locally.
+    """
+    def __init__(self):
+        PureData.__init__(self)
+        self.density.source           = self.density.SOURCE_DATA
+        self.viscosity.source         = self.viscosity.SOURCE_DATA
+        self.specific_heat.source     = self.specific_heat.SOURCE_DATA
+        self.conductivity.source      = self.conductivity.SOURCE_DATA
+        self.saturation_pressure.source = self.saturation_pressure.SOURCE_DATA
+        self.temperature.data         = np.array([2.731500E+2,2.741500E+2,2.751500E+2,2.761500E+2,2.771500E+2,2.781500E+2,2.791500E+2,2.801500E+2,2.811500E+2,2.821500E+2,2.831500E+2,2.841500E+2,2.851500E+2,2.861500E+2,2.871500E+2,2.881500E+2,2.891500E+2,2.901500E+2,2.911500E+2,2.921500E+2,2.931500E+2,2.941500E+2,2.951500E+2,2.961500E+2,2.971500E+2,2.981500E+2,2.991500E+2,3.001500E+2,3.011500E+2,3.021500E+2,3.031500E+2,3.041500E+2,3.051500E+2,3.061500E+2,3.071500E+2,3.081500E+2,3.091500E+2,3.101500E+2,3.111500E+2,3.121500E+2,3.131500E+2,3.141500E+2,3.151500E+2,3.161500E+2,3.171500E+2,3.181500E+2,3.191500E+2,3.201500E+2,3.211500E+2,3.221500E+2,3.231500E+2,3.241500E+2,3.251500E+2,3.261500E+2,3.271500E+2,3.281500E+2,3.291500E+2,3.301500E+2,3.311500E+2,3.321500E+2,3.331500E+2,3.341500E+2,3.351500E+2,3.361500E+2,3.371500E+2,3.381500E+2,3.391500E+2,3.401500E+2,3.411500E+2,3.421500E+2,3.431500E+2,3.441500E+2,3.451500E+2,3.461500E+2,3.471500E+2,3.481500E+2,3.491500E+2,3.501500E+2,3.511500E+2,3.521500E+2,3.531500E+2,3.541500E+2,3.551500E+2,3.561500E+2,3.571500E+2,3.581500E+2,3.591500E+2,3.601500E+2,3.611500E+2,3.621500E+2,3.631500E+2,3.641500E+2,3.651500E+2,3.661500E+2,3.671500E+2,3.681500E+2,3.691500E+2,3.701500E+2,3.711500E+2,3.721500E+2,3.731500E+2,3.741500E+2,3.751500E+2,3.761500E+2,3.771500E+2,3.781500E+2,3.791500E+2,3.801500E+2,3.811500E+2,3.821500E+2,3.831500E+2,3.841500E+2,3.851500E+2,3.861500E+2,3.871500E+2,3.881500E+2,3.891500E+2,3.901500E+2,3.911500E+2,3.921500E+2,3.931500E+2,3.941500E+2,3.951500E+2,3.961500E+2,3.971500E+2,3.981500E+2,3.991500E+2,4.001500E+2,4.011500E+2,4.021500E+2,4.031500E+2,4.041500E+2,4.051500E+2,4.061500E+2,4.071500E+2,4.081500E+2,4.091500E+2,4.101500E+2,4.111500E+2,4.121500E+2,4.131500E+2,4.141500E+2,4.151500E+2,4.161500E+2,4.171500E+2,4.181500E+2,4.191500E+2,4.201500E+2,4.211500E+2,4.221500E+2,4.231500E+2,4.241500E+2,4.251500E+2,4.261500E+2,4.271500E+2,4.281500E+2,4.291500E+2,4.301500E+2,4.311500E+2,4.321500E+2,4.331500E+2,4.341500E+2,4.351500E+2,4.361500E+2,4.371500E+2,4.381500E+2,4.391500E+2,4.401500E+2,4.411500E+2,4.421500E+2,4.431500E+2,4.441500E+2,4.451500E+2,4.461500E+2,4.471500E+2,4.481500E+2,4.491500E+2,4.501500E+2,4.511500E+2,4.521500E+2,4.531500E+2,4.541500E+2,4.551500E+2,4.561500E+2,4.571500E+2,4.581500E+2,4.591500E+2,4.601500E+2,4.611500E+2,4.621500E+2,4.631500E+2,4.641500E+2,4.651500E+2,4.661500E+2,4.671500E+2,4.681500E+2,4.691500E+2,4.701500E+2,4.711500E+2,4.721500E+2,4.731500E+2,4.741500E+2,4.751500E+2,4.761500E+2,4.771500E+2,4.781500E+2,4.791500E+2,4.801500E+2,4.811500E+2,4.821500E+2,4.831500E+2,4.841500E+2,4.851500E+2,4.861500E+2,4.871500E+2,4.881500E+2,4.891500E+2,4.901500E+2,4.911500E+2,4.921500E+2,4.931500E+2,4.941500E+2,4.951500E+2,4.961500E+2,4.971500E+2,4.981500E+2,4.991500E+2,5.001500E+2,5.011500E+2,5.021500E+2,5.031500E+2,5.041500E+2,5.051500E+2,5.061500E+2,5.071500E+2,5.081500E+2,5.091500E+2,5.101500E+2,5.111500E+2,5.121500E+2,5.131500E+2,5.141500E+2,5.151500E+2,5.161500E+2,5.171500E+2,5.181500E+2,5.191500E+2,5.201500E+2,5.211500E+2,5.221500E+2,5.231500E+2,5.241500E+2,5.251500E+2,5.261500E+2,5.271500E+2,5.281500E+2,5.291500E+2,5.301500E+2,5.311500E+2,5.321500E+2,5.331500E+2,5.341500E+2,5.351500E+2,5.361500E+2,5.371500E+2,5.381500E+2,5.391500E+2,5.401500E+2,5.411500E+2,5.421500E+2,5.431500E+2,5.441500E+2,5.451500E+2,5.461500E+2,5.471500E+2,5.481500E+2,5.491500E+2,5.501500E+2,5.511500E+2,5.521500E+2,5.531500E+2,5.541500E+2,5.551500E+2,5.561500E+2,5.571500E+2,5.581500E+2,5.591500E+2,5.601500E+2,5.611500E+2,5.621500E+2,5.631500E+2,5.641500E+2,5.651500E+2,5.661500E+2,5.671500E+2,5.681500E+2,5.691500E+2,5.701500E+2,5.711500E+2,5.721500E+2,5.731500E+2,5.741500E+2,5.751500E+2,5.761500E+2,5.771500E+2,5.781500E+2,5.791500E+2,5.801500E+2,5.811500E+2,5.821500E+2,5.831500E+2,5.841500E+2,5.851500E+2,5.861500E+2,5.871500E+2,5.881500E+2,5.891500E+2,5.901500E+2,5.911500E+2,5.921500E+2,5.931500E+2,5.941500E+2,5.951500E+2,5.961500E+2,5.971500E+2,5.981500E+2,5.991500E+2,6.001500E+2,6.011500E+2,6.021500E+2,6.031500E+2])
+        self.density.data             = np.array([8.750000E+2,8.750000E+2,8.740000E+2,8.740000E+2,8.730000E+2,8.720000E+2,8.720000E+2,8.710000E+2,8.700000E+2,8.700000E+2,8.690000E+2,8.680000E+2,8.680000E+2,8.670000E+2,8.660000E+2,8.660000E+2,8.650000E+2,8.650000E+2,8.640000E+2,8.630000E+2,8.630000E+2,8.620000E+2,8.610000E+2,8.610000E+2,8.600000E+2,8.590000E+2,8.590000E+2,8.580000E+2,8.580000E+2,8.570000E+2,8.560000E+2,8.560000E+2,8.550000E+2,8.540000E+2,8.540000E+2,8.530000E+2,8.520000E+2,8.520000E+2,8.510000E+2,8.500000E+2,8.500000E+2,8.490000E+2,8.490000E+2,8.480000E+2,8.470000E+2,8.470000E+2,8.460000E+2,8.450000E+2,8.450000E+2,8.440000E+2,8.430000E+2,8.430000E+2,8.420000E+2,8.420000E+2,8.410000E+2,8.400000E+2,8.400000E+2,8.390000E+2,8.380000E+2,8.380000E+2,8.370000E+2,8.360000E+2,8.360000E+2,8.350000E+2,8.340000E+2,8.340000E+2,8.330000E+2,8.330000E+2,8.320000E+2,8.310000E+2,8.310000E+2,8.300000E+2,8.290000E+2,8.290000E+2,8.280000E+2,8.270000E+2,8.270000E+2,8.260000E+2,8.250000E+2,8.250000E+2,8.240000E+2,8.240000E+2,8.230000E+2,8.220000E+2,8.220000E+2,8.210000E+2,8.200000E+2,8.200000E+2,8.190000E+2,8.180000E+2,8.180000E+2,8.170000E+2,8.170000E+2,8.160000E+2,8.150000E+2,8.150000E+2,8.140000E+2,8.130000E+2,8.130000E+2,8.120000E+2,8.110000E+2,8.110000E+2,8.100000E+2,8.090000E+2,8.090000E+2,8.080000E+2,8.080000E+2,8.070000E+2,8.060000E+2,8.060000E+2,8.050000E+2,8.040000E+2,8.040000E+2,8.030000E+2,8.020000E+2,8.020000E+2,8.010000E+2,8.000000E+2,8.000000E+2,7.990000E+2,7.990000E+2,7.980000E+2,7.970000E+2,7.970000E+2,7.960000E+2,7.950000E+2,7.950000E+2,7.940000E+2,7.930000E+2,7.930000E+2,7.920000E+2,7.920000E+2,7.910000E+2,7.900000E+2,7.900000E+2,7.890000E+2,7.880000E+2,7.880000E+2,7.870000E+2,7.860000E+2,7.860000E+2,7.850000E+2,7.840000E+2,7.840000E+2,7.830000E+2,7.830000E+2,7.820000E+2,7.810000E+2,7.810000E+2,7.800000E+2,7.790000E+2,7.790000E+2,7.780000E+2,7.770000E+2,7.770000E+2,7.760000E+2,7.750000E+2,7.750000E+2,7.740000E+2,7.740000E+2,7.730000E+2,7.720000E+2,7.720000E+2,7.710000E+2,7.700000E+2,7.700000E+2,7.690000E+2,7.680000E+2,7.680000E+2,7.670000E+2,7.670000E+2,7.660000E+2,7.650000E+2,7.650000E+2,7.640000E+2,7.630000E+2,7.630000E+2,7.620000E+2,7.610000E+2,7.610000E+2,7.600000E+2,7.590000E+2,7.590000E+2,7.580000E+2,7.580000E+2,7.570000E+2,7.560000E+2,7.560000E+2,7.550000E+2,7.540000E+2,7.540000E+2,7.530000E+2,7.520000E+2,7.520000E+2,7.510000E+2,7.500000E+2,7.500000E+2,7.490000E+2,7.490000E+2,7.480000E+2,7.470000E+2,7.470000E+2,7.460000E+2,7.450000E+2,7.450000E+2,7.440000E+2,7.430000E+2,7.430000E+2,7.420000E+2,7.420000E+2,7.410000E+2,7.400000E+2,7.400000E+2,7.390000E+2,7.380000E+2,7.380000E+2,7.370000E+2,7.360000E+2,7.360000E+2,7.350000E+2,7.340000E+2,7.340000E+2,7.330000E+2,7.330000E+2,7.320000E+2,7.310000E+2,7.310000E+2,7.300000E+2,7.290000E+2,7.290000E+2,7.280000E+2,7.270000E+2,7.270000E+2,7.260000E+2,7.250000E+2,7.250000E+2,7.240000E+2,7.240000E+2,7.230000E+2,7.220000E+2,7.220000E+2,7.210000E+2,7.200000E+2,7.200000E+2,7.190000E+2,7.180000E+2,7.180000E+2,7.170000E+2,7.170000E+2,7.160000E+2,7.150000E+2,7.150000E+2,7.140000E+2,7.130000E+2,7.130000E+2,7.120000E+2,7.110000E+2,7.110000E+2,7.100000E+2,7.090000E+2,7.090000E+2,7.080000E+2,7.080000E+2,7.070000E+2,7.060000E+2,7.060000E+2,7.050000E+2,7.040000E+2,7.040000E+2,7.030000E+2,7.020000E+2,7.020000E+2,7.010000E+2,7.010000E+2,7.000000E+2,6.990000E+2,6.990000E+2,6.980000E+2,6.970000E+2,6.970000E+2,6.960000E+2,6.950000E+2,6.950000E+2,6.940000E+2,6.930000E+2,6.930000E+2,6.920000E+2,6.920000E+2,6.910000E+2,6.900000E+2,6.900000E+2,6.890000E+2,6.880000E+2,6.880000E+2,6.870000E+2,6.860000E+2,6.860000E+2,6.850000E+2,6.840000E+2,6.840000E+2,6.830000E+2,6.830000E+2,6.820000E+2,6.810000E+2,6.810000E+2,6.800000E+2,6.790000E+2,6.790000E+2,6.780000E+2,6.770000E+2,6.770000E+2,6.760000E+2,6.760000E+2,6.750000E+2,6.740000E+2,6.740000E+2,6.730000E+2,6.720000E+2,6.720000E+2,6.710000E+2,6.700000E+2,6.700000E+2,6.690000E+2,6.680000E+2,6.680000E+2,6.670000E+2,6.670000E+2,6.660000E+2,6.650000E+2,6.650000E+2,6.640000E+2])
+        self.viscosity.data           = np.array([4.070000E-4,3.770000E-4,3.490000E-4,3.230000E-4,2.990000E-4,2.760000E-4,2.560000E-4,2.370000E-4,2.190000E-4,2.030000E-4,1.880000E-4,1.740000E-4,1.610000E-4,1.490000E-4,1.380000E-4,1.270000E-4,1.180000E-4,1.140000E-4,1.050000E-4,9.700000E-5,9.000000E-5,8.400000E-5,7.800000E-5,7.400000E-5,6.900000E-5,6.500000E-5,6.200000E-5,5.800000E-5,5.500000E-5,5.300000E-5,5.000000E-5,4.800000E-5,4.600000E-5,4.400000E-5,4.200000E-5,4.000000E-5,3.800000E-5,3.700000E-5,3.500000E-5,3.400000E-5,3.300000E-5,3.200000E-5,3.100000E-5,3.000000E-5,2.900000E-5,2.800000E-5,2.700000E-5,2.600000E-5,2.500000E-5,2.400000E-5,2.300000E-5,2.200000E-5,2.100000E-5,2.000000E-5,1.900000E-5,1.800000E-5,1.800000E-5,1.700000E-5,1.600000E-5,1.600000E-5,1.500000E-5,1.500000E-5,1.400000E-5,1.400000E-5,1.300000E-5,1.300000E-5,1.200000E-5,1.200000E-5,1.200000E-5,1.100000E-5,1.100000E-5,1.100000E-5,1.000000E-5,1.000000E-5,9.800000E-6,9.500000E-6,9.300000E-6,9.000000E-6,8.800000E-6,8.500000E-6,8.300000E-6,8.100000E-6,7.900000E-6,7.700000E-6,7.500000E-6,7.300000E-6,7.100000E-6,6.900000E-6,6.800000E-6,6.600000E-6,6.500000E-6,6.300000E-6,6.200000E-6,6.000000E-6,5.900000E-6,5.800000E-6,5.600000E-6,5.500000E-6,5.400000E-6,5.300000E-6,5.200000E-6,5.100000E-6,5.000000E-6,4.900000E-6,4.800000E-6,4.700000E-6,4.600000E-6,4.500000E-6,4.400000E-6,4.300000E-6,4.200000E-6,4.100000E-6,4.100000E-6,4.000000E-6,3.900000E-6,3.800000E-6,3.800000E-6,3.600000E-6,3.500000E-6,3.500000E-6,3.400000E-6,3.300000E-6,3.300000E-6,3.200000E-6,3.200000E-6,3.100000E-6,3.100000E-6,3.000000E-6,3.000000E-6,2.900000E-6,2.900000E-6,2.800000E-6,2.800000E-6,2.800000E-6,2.700000E-6,2.700000E-6,2.600000E-6,2.600000E-6,2.600000E-6,2.500000E-6,2.500000E-6,2.500000E-6,2.400000E-6,2.400000E-6,2.300000E-6,2.300000E-6,2.300000E-6,2.200000E-6,2.200000E-6,2.200000E-6,2.200000E-6,2.100000E-6,2.100000E-6,2.100000E-6,2.000000E-6,2.000000E-6,2.000000E-6,2.000000E-6,1.900000E-6,1.900000E-6,1.900000E-6,1.900000E-6,1.900000E-6,1.800000E-6,1.800000E-6,1.800000E-6,1.800000E-6,1.700000E-6,1.700000E-6,1.700000E-6,1.700000E-6,1.600000E-6,1.600000E-6,1.600000E-6,1.600000E-6,1.600000E-6,1.600000E-6,1.500000E-6,1.500000E-6,1.500000E-6,1.500000E-6,1.500000E-6,1.500000E-6,1.400000E-6,1.400000E-6,1.400000E-6,1.400000E-6,1.400000E-6,1.400000E-6,1.400000E-6,1.300000E-6,1.300000E-6,1.300000E-6,1.300000E-6,1.300000E-6,1.300000E-6,1.200000E-6,1.200000E-6,1.200000E-6,1.200000E-6,1.200000E-6,1.200000E-6,1.200000E-6,1.200000E-6,1.200000E-6,1.100000E-6,1.100000E-6,1.100000E-6,1.100000E-6,1.100000E-6,1.100000E-6,1.100000E-6,1.100000E-6,1.100000E-6,1.000000E-6,1.000000E-6,1.000000E-6,1.000000E-6,1.000000E-6,1.000000E-6,9.900000E-7,9.800000E-7,9.700000E-7,9.600000E-7,9.600000E-7,9.500000E-7,9.400000E-7,9.300000E-7,9.200000E-7,9.100000E-7,9.100000E-7,9.000000E-7,8.900000E-7,8.800000E-7,8.700000E-7,8.700000E-7,8.600000E-7,8.500000E-7,8.400000E-7,8.400000E-7,8.300000E-7,8.200000E-7,8.200000E-7,8.100000E-7,8.000000E-7,8.000000E-7,7.900000E-7,7.800000E-7,7.800000E-7,7.700000E-7,7.600000E-7,7.600000E-7,7.500000E-7,7.500000E-7,7.400000E-7,7.300000E-7,7.300000E-7,7.200000E-7,7.200000E-7,7.100000E-7,7.100000E-7,7.000000E-7,6.900000E-7,6.900000E-7,6.800000E-7,6.800000E-7,6.700000E-7,6.700000E-7,6.600000E-7,6.600000E-7,6.500000E-7,6.500000E-7,6.400000E-7,6.400000E-7,6.300000E-7,6.300000E-7,6.200000E-7,6.200000E-7,6.200000E-7,6.100000E-7,6.100000E-7,6.000000E-7,6.000000E-7,5.900000E-7,5.900000E-7,5.900000E-7,5.800000E-7,5.800000E-7,5.700000E-7,5.700000E-7,5.700000E-7,5.600000E-7,5.600000E-7,5.500000E-7,5.500000E-7,5.500000E-7,5.400000E-7,5.400000E-7,5.300000E-7,5.300000E-7,5.300000E-7,5.200000E-7,5.200000E-7,5.200000E-7,5.100000E-7,5.100000E-7,5.100000E-7,5.000000E-7,5.000000E-7,5.000000E-7,4.900000E-7,4.900000E-7,4.900000E-7,4.800000E-7,4.800000E-7,4.800000E-7,4.700000E-7,4.700000E-7,4.700000E-7,4.700000E-7,4.600000E-7,4.600000E-7,4.600000E-7,4.500000E-7,4.500000E-7,4.500000E-7,4.500000E-7,4.400000E-7,4.400000E-7,4.400000E-7,4.300000E-7])
+        self.specific_heat.data       = np.array([1.800000E+3,1.800000E+3,1.800000E+3,1.800000E+3,1.800000E+3,1.800000E+3,1.800000E+3,1.800000E+3,1.800000E+3,1.800000E+3,1.800000E+3,1.800000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.900000E+3,2.900000E+3,2.900000E+3,2.900000E+3,2.900000E+3,2.900000E+3,2.900000E+3,2.900000E+3,2.900000E+3,2.900000E+3,2.900000E+3,2.900000E+3,2.900000E+3,2.900000E+3,2.900000E+3,2.900000E+3,2.900000E+3,2.900000E+3,2.900000E+3,2.900000E+3,2.900000E+3,2.900000E+3,2.900000E+3,2.900000E+3,2.900000E+3,2.900000E+3,2.900000E+3,3.000000E+3,3.000000E+3,3.000000E+3,3.000000E+3,3.000000E+3,3.000000E+3,3.000000E+3,3.000000E+3,3.000000E+3,3.000000E+3,3.000000E+3,3.000000E+3,3.000000E+3,3.000000E+3])
+        self.conductivity.data        = np.array([1.340000E-1,1.340000E-1,1.340000E-1,1.340000E-1,1.340000E-1,1.340000E-1,1.340000E-1,1.340000E-1,1.330000E-1,1.330000E-1,1.330000E-1,1.330000E-1,1.330000E-1,1.330000E-1,1.330000E-1,1.330000E-1,1.330000E-1,1.330000E-1,1.330000E-1,1.330000E-1,1.320000E-1,1.320000E-1,1.320000E-1,1.320000E-1,1.320000E-1,1.320000E-1,1.320000E-1,1.320000E-1,1.320000E-1,1.320000E-1,1.320000E-1,1.320000E-1,1.320000E-1,1.310000E-1,1.310000E-1,1.310000E-1,1.310000E-1,1.310000E-1,1.310000E-1,1.310000E-1,1.310000E-1,1.310000E-1,1.310000E-1,1.310000E-1,1.310000E-1,1.300000E-1,1.300000E-1,1.300000E-1,1.300000E-1,1.300000E-1,1.300000E-1,1.300000E-1,1.300000E-1,1.300000E-1,1.300000E-1,1.300000E-1,1.300000E-1,1.300000E-1,1.290000E-1,1.290000E-1,1.290000E-1,1.290000E-1,1.290000E-1,1.290000E-1,1.290000E-1,1.290000E-1,1.290000E-1,1.290000E-1,1.290000E-1,1.290000E-1,1.280000E-1,1.280000E-1,1.280000E-1,1.280000E-1,1.280000E-1,1.280000E-1,1.280000E-1,1.280000E-1,1.280000E-1,1.280000E-1,1.280000E-1,1.280000E-1,1.280000E-1,1.270000E-1,1.270000E-1,1.270000E-1,1.270000E-1,1.270000E-1,1.270000E-1,1.270000E-1,1.270000E-1,1.270000E-1,1.270000E-1,1.270000E-1,1.270000E-1,1.260000E-1,1.260000E-1,1.260000E-1,1.260000E-1,1.260000E-1,1.260000E-1,1.260000E-1,1.260000E-1,1.260000E-1,1.260000E-1,1.260000E-1,1.260000E-1,1.260000E-1,1.250000E-1,1.250000E-1,1.250000E-1,1.250000E-1,1.250000E-1,1.250000E-1,1.250000E-1,1.250000E-1,1.250000E-1,1.250000E-1,1.250000E-1,1.250000E-1,1.240000E-1,1.240000E-1,1.240000E-1,1.240000E-1,1.240000E-1,1.240000E-1,1.240000E-1,1.240000E-1,1.240000E-1,1.240000E-1,1.240000E-1,1.240000E-1,1.240000E-1,1.230000E-1,1.230000E-1,1.230000E-1,1.230000E-1,1.230000E-1,1.230000E-1,1.230000E-1,1.230000E-1,1.230000E-1,1.230000E-1,1.230000E-1,1.230000E-1,1.220000E-1,1.220000E-1,1.220000E-1,1.220000E-1,1.220000E-1,1.220000E-1,1.220000E-1,1.220000E-1,1.220000E-1,1.220000E-1,1.220000E-1,1.220000E-1,1.220000E-1,1.210000E-1,1.210000E-1,1.210000E-1,1.210000E-1,1.210000E-1,1.210000E-1,1.210000E-1,1.210000E-1,1.210000E-1,1.210000E-1,1.210000E-1,1.210000E-1,1.200000E-1,1.200000E-1,1.200000E-1,1.200000E-1,1.200000E-1,1.200000E-1,1.200000E-1,1.200000E-1,1.200000E-1,1.200000E-1,1.200000E-1,1.200000E-1,1.200000E-1,1.190000E-1,1.190000E-1,1.190000E-1,1.190000E-1,1.190000E-1,1.190000E-1,1.190000E-1,1.190000E-1,1.190000E-1,1.190000E-1,1.190000E-1,1.190000E-1,1.180000E-1,1.180000E-1,1.180000E-1,1.180000E-1,1.180000E-1,1.180000E-1,1.180000E-1,1.180000E-1,1.180000E-1,1.180000E-1,1.180000E-1,1.180000E-1,1.180000E-1,1.170000E-1,1.170000E-1,1.170000E-1,1.170000E-1,1.170000E-1,1.170000E-1,1.170000E-1,1.170000E-1,1.170000E-1,1.170000E-1,1.170000E-1,1.170000E-1,1.160000E-1,1.160000E-1,1.160000E-1,1.160000E-1,1.160000E-1,1.160000E-1,1.160000E-1,1.160000E-1,1.160000E-1,1.160000E-1,1.160000E-1,1.160000E-1,1.160000E-1,1.150000E-1,1.150000E-1,1.150000E-1,1.150000E-1,1.150000E-1,1.150000E-1,1.150000E-1,1.150000E-1,1.150000E-1,1.150000E-1,1.150000E-1,1.150000E-1,1.140000E-1,1.140000E-1,1.140000E-1,1.140000E-1,1.140000E-1,1.140000E-1,1.140000E-1,1.140000E-1,1.140000E-1,1.140000E-1,1.140000E-1,1.140000E-1,1.140000E-1,1.130000E-1,1.130000E-1,1.130000E-1,1.130000E-1,1.130000E-1,1.130000E-1,1.130000E-1,1.130000E-1,1.130000E-1,1.130000E-1,1.130000E-1,1.130000E-1,1.120000E-1,1.120000E-1,1.120000E-1,1.120000E-1,1.120000E-1,1.120000E-1,1.120000E-1,1.120000E-1,1.120000E-1,1.120000E-1,1.120000E-1,1.120000E-1,1.120000E-1,1.110000E-1,1.110000E-1,1.110000E-1,1.110000E-1,1.110000E-1,1.110000E-1,1.110000E-1,1.110000E-1,1.110000E-1,1.110000E-1,1.110000E-1,1.110000E-1,1.100000E-1,1.100000E-1,1.100000E-1,1.100000E-1,1.100000E-1,1.100000E-1,1.100000E-1,1.100000E-1,1.100000E-1,1.100000E-1,1.100000E-1,1.100000E-1,1.100000E-1,1.090000E-1,1.090000E-1,1.090000E-1,1.090000E-1,1.090000E-1,1.090000E-1,1.090000E-1,1.090000E-1,1.090000E-1,1.090000E-1,1.090000E-1,1.090000E-1,1.080000E-1,1.080000E-1,1.080000E-1,1.080000E-1,1.080000E-1,1.080000E-1,1.080000E-1,1.080000E-1,1.080000E-1,1.080000E-1,1.080000E-1])
+        self.saturation_pressure.data = np.array([     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,3.000000E+3,3.000000E+3,3.000000E+3,3.000000E+3,3.000000E+3,3.000000E+3,3.000000E+3,3.000000E+3,3.000000E+3,4.000000E+3,4.000000E+3,4.000000E+3,4.000000E+3,4.000000E+3,4.000000E+3,4.000000E+3,5.000000E+3,5.000000E+3,5.000000E+3,5.000000E+3,5.000000E+3,5.000000E+3,6.000000E+3,6.000000E+3,6.000000E+3,6.000000E+3,6.000000E+3,7.000000E+3,7.000000E+3,7.000000E+3,7.000000E+3,8.000000E+3,8.000000E+3,8.000000E+3,8.000000E+3,9.000000E+3,9.000000E+3,9.000000E+3,9.000000E+3,1.000000E+4,1.000000E+4,1.000000E+4,1.100000E+4,1.100000E+4,1.100000E+4])
+        self.Tmin        = np.min(self.temperature.data)
+        self.Tmax        = np.max(self.temperature.data)
+        self.TminPsat    = np.min(self.temperature.data[~np.isnan(self.saturation_pressure.data)])
+        self.name        = "PHE"
+        self.description = "Paratherm "+ self.name[1:]
+        self.reference   = "Paratherm2013"
+        self.reshapeAll()
+
+
+
+class PHR(PureData):
+    """
+    The Paratherm HR Heat Transfer Fluid is an alkylated-aromatic based heat
+    transfer fluid formulated for closed loop liquid phase heating to 650 F in
+    fired heaters and 675 F in waste heat recovery and full convection heaters.
+    """
+    def __init__(self):
+        PureData.__init__(self)
+        self.density.source           = self.density.SOURCE_DATA
+        self.viscosity.source         = self.viscosity.SOURCE_DATA
+        self.specific_heat.source     = self.specific_heat.SOURCE_DATA
+        self.conductivity.source      = self.conductivity.SOURCE_DATA
+        self.saturation_pressure.source = self.saturation_pressure.SOURCE_DATA
+        self.temperature.data         = np.array([2.581500E+2,2.591500E+2,2.601500E+2,2.611500E+2,2.621500E+2,2.631500E+2,2.641500E+2,2.651500E+2,2.661500E+2,2.671500E+2,2.681500E+2,2.691500E+2,2.701500E+2,2.711500E+2,2.721500E+2,2.731500E+2,2.741500E+2,2.751500E+2,2.761500E+2,2.771500E+2,2.781500E+2,2.791500E+2,2.801500E+2,2.811500E+2,2.821500E+2,2.831500E+2,2.841500E+2,2.851500E+2,2.861500E+2,2.871500E+2,2.881500E+2,2.891500E+2,2.901500E+2,2.911500E+2,2.921500E+2,2.931500E+2,2.941500E+2,2.951500E+2,2.961500E+2,2.971500E+2,2.981500E+2,2.991500E+2,3.001500E+2,3.011500E+2,3.021500E+2,3.031500E+2,3.041500E+2,3.051500E+2,3.061500E+2,3.071500E+2,3.081500E+2,3.091500E+2,3.101500E+2,3.111500E+2,3.121500E+2,3.131500E+2,3.141500E+2,3.151500E+2,3.161500E+2,3.171500E+2,3.181500E+2,3.191500E+2,3.201500E+2,3.211500E+2,3.221500E+2,3.231500E+2,3.241500E+2,3.251500E+2,3.261500E+2,3.271500E+2,3.281500E+2,3.291500E+2,3.301500E+2,3.311500E+2,3.321500E+2,3.331500E+2,3.341500E+2,3.351500E+2,3.361500E+2,3.371500E+2,3.381500E+2,3.391500E+2,3.401500E+2,3.411500E+2,3.421500E+2,3.431500E+2,3.441500E+2,3.451500E+2,3.461500E+2,3.471500E+2,3.481500E+2,3.491500E+2,3.501500E+2,3.511500E+2,3.521500E+2,3.531500E+2,3.541500E+2,3.551500E+2,3.561500E+2,3.571500E+2,3.581500E+2,3.591500E+2,3.601500E+2,3.611500E+2,3.621500E+2,3.631500E+2,3.641500E+2,3.651500E+2,3.661500E+2,3.671500E+2,3.681500E+2,3.691500E+2,3.701500E+2,3.711500E+2,3.721500E+2,3.731500E+2,3.741500E+2,3.751500E+2,3.761500E+2,3.771500E+2,3.781500E+2,3.791500E+2,3.801500E+2,3.811500E+2,3.821500E+2,3.831500E+2,3.841500E+2,3.851500E+2,3.861500E+2,3.871500E+2,3.881500E+2,3.891500E+2,3.901500E+2,3.911500E+2,3.921500E+2,3.931500E+2,3.941500E+2,3.951500E+2,3.961500E+2,3.971500E+2,3.981500E+2,3.991500E+2,4.001500E+2,4.011500E+2,4.021500E+2,4.031500E+2,4.041500E+2,4.051500E+2,4.061500E+2,4.071500E+2,4.081500E+2,4.091500E+2,4.101500E+2,4.111500E+2,4.121500E+2,4.131500E+2,4.141500E+2,4.151500E+2,4.161500E+2,4.171500E+2,4.181500E+2,4.191500E+2,4.201500E+2,4.211500E+2,4.221500E+2,4.231500E+2,4.241500E+2,4.251500E+2,4.261500E+2,4.271500E+2,4.281500E+2,4.291500E+2,4.301500E+2,4.311500E+2,4.321500E+2,4.331500E+2,4.341500E+2,4.351500E+2,4.361500E+2,4.371500E+2,4.381500E+2,4.391500E+2,4.401500E+2,4.411500E+2,4.421500E+2,4.431500E+2,4.441500E+2,4.451500E+2,4.461500E+2,4.471500E+2,4.481500E+2,4.491500E+2,4.501500E+2,4.511500E+2,4.521500E+2,4.531500E+2,4.541500E+2,4.551500E+2,4.561500E+2,4.571500E+2,4.581500E+2,4.591500E+2,4.601500E+2,4.611500E+2,4.621500E+2,4.631500E+2,4.641500E+2,4.651500E+2,4.661500E+2,4.671500E+2,4.681500E+2,4.691500E+2,4.701500E+2,4.711500E+2,4.721500E+2,4.731500E+2,4.741500E+2,4.751500E+2,4.761500E+2,4.771500E+2,4.781500E+2,4.791500E+2,4.801500E+2,4.811500E+2,4.821500E+2,4.831500E+2,4.841500E+2,4.851500E+2,4.861500E+2,4.871500E+2,4.881500E+2,4.891500E+2,4.901500E+2,4.911500E+2,4.921500E+2,4.931500E+2,4.941500E+2,4.951500E+2,4.961500E+2,4.971500E+2,4.981500E+2,4.991500E+2,5.001500E+2,5.011500E+2,5.021500E+2,5.031500E+2,5.041500E+2,5.051500E+2,5.061500E+2,5.071500E+2,5.081500E+2,5.091500E+2,5.101500E+2,5.111500E+2,5.121500E+2,5.131500E+2,5.141500E+2,5.151500E+2,5.161500E+2,5.171500E+2,5.181500E+2,5.191500E+2,5.201500E+2,5.211500E+2,5.221500E+2,5.231500E+2,5.241500E+2,5.251500E+2,5.261500E+2,5.271500E+2,5.281500E+2,5.291500E+2,5.301500E+2,5.311500E+2,5.321500E+2,5.331500E+2,5.341500E+2,5.351500E+2,5.361500E+2,5.371500E+2,5.381500E+2,5.391500E+2,5.401500E+2,5.411500E+2,5.421500E+2,5.431500E+2,5.441500E+2,5.451500E+2,5.461500E+2,5.471500E+2,5.481500E+2,5.491500E+2,5.501500E+2,5.511500E+2,5.521500E+2,5.531500E+2,5.541500E+2,5.551500E+2,5.561500E+2,5.571500E+2,5.581500E+2,5.591500E+2,5.601500E+2,5.611500E+2,5.621500E+2,5.631500E+2,5.641500E+2,5.651500E+2,5.661500E+2,5.671500E+2,5.681500E+2,5.691500E+2,5.701500E+2,5.711500E+2,5.721500E+2,5.731500E+2,5.741500E+2,5.751500E+2,5.761500E+2,5.771500E+2,5.781500E+2,5.791500E+2,5.801500E+2,5.811500E+2,5.821500E+2,5.831500E+2,5.841500E+2,5.851500E+2,5.861500E+2,5.871500E+2,5.881500E+2,5.891500E+2,5.901500E+2,5.911500E+2,5.921500E+2,5.931500E+2,5.941500E+2,5.951500E+2,5.961500E+2,5.971500E+2,5.981500E+2,5.991500E+2,6.001500E+2,6.011500E+2,6.021500E+2,6.031500E+2,6.041500E+2,6.051500E+2,6.061500E+2,6.071500E+2,6.081500E+2,6.091500E+2,6.101500E+2,6.111500E+2,6.121500E+2,6.131500E+2,6.141500E+2,6.151500E+2,6.161500E+2,6.171500E+2,6.181500E+2,6.191500E+2,6.201500E+2,6.211500E+2,6.221500E+2,6.231500E+2,6.241500E+2,6.251500E+2,6.261500E+2,6.271500E+2,6.281500E+2,6.291500E+2,6.301500E+2,6.311500E+2,6.321500E+2,6.331500E+2,6.341500E+2,6.351500E+2,6.361500E+2,6.371500E+2,6.381500E+2,6.391500E+2,6.401500E+2,6.411500E+2,6.421500E+2,6.431500E+2])
+        self.density.data             = np.array([9.870000E+2,9.860000E+2,9.850000E+2,9.850000E+2,9.840000E+2,9.830000E+2,9.820000E+2,9.810000E+2,9.810000E+2,9.800000E+2,9.790000E+2,9.780000E+2,9.780000E+2,9.770000E+2,9.760000E+2,9.750000E+2,9.750000E+2,9.740000E+2,9.730000E+2,9.720000E+2,9.710000E+2,9.710000E+2,9.700000E+2,9.690000E+2,9.680000E+2,9.680000E+2,9.670000E+2,9.660000E+2,9.650000E+2,9.650000E+2,9.640000E+2,9.630000E+2,9.620000E+2,9.610000E+2,9.610000E+2,9.600000E+2,9.590000E+2,9.580000E+2,9.580000E+2,9.570000E+2,9.560000E+2,9.550000E+2,9.550000E+2,9.540000E+2,9.530000E+2,9.520000E+2,9.510000E+2,9.510000E+2,9.500000E+2,9.490000E+2,9.480000E+2,9.480000E+2,9.470000E+2,9.460000E+2,9.450000E+2,9.450000E+2,9.440000E+2,9.430000E+2,9.420000E+2,9.410000E+2,9.410000E+2,9.400000E+2,9.390000E+2,9.380000E+2,9.380000E+2,9.370000E+2,9.360000E+2,9.350000E+2,9.350000E+2,9.340000E+2,9.330000E+2,9.320000E+2,9.310000E+2,9.310000E+2,9.300000E+2,9.290000E+2,9.280000E+2,9.280000E+2,9.270000E+2,9.260000E+2,9.250000E+2,9.250000E+2,9.240000E+2,9.230000E+2,9.220000E+2,9.210000E+2,9.210000E+2,9.200000E+2,9.190000E+2,9.180000E+2,9.180000E+2,9.170000E+2,9.160000E+2,9.150000E+2,9.150000E+2,9.140000E+2,9.130000E+2,9.120000E+2,9.110000E+2,9.110000E+2,9.100000E+2,9.090000E+2,9.080000E+2,9.080000E+2,9.070000E+2,9.060000E+2,9.050000E+2,9.050000E+2,9.040000E+2,9.030000E+2,9.020000E+2,9.010000E+2,9.010000E+2,9.000000E+2,8.990000E+2,8.980000E+2,8.980000E+2,8.970000E+2,8.960000E+2,8.950000E+2,8.950000E+2,8.940000E+2,8.930000E+2,8.920000E+2,8.910000E+2,8.910000E+2,8.900000E+2,8.890000E+2,8.880000E+2,8.880000E+2,8.870000E+2,8.860000E+2,8.850000E+2,8.850000E+2,8.840000E+2,8.830000E+2,8.820000E+2,8.810000E+2,8.810000E+2,8.800000E+2,8.790000E+2,8.780000E+2,8.780000E+2,8.770000E+2,8.760000E+2,8.750000E+2,8.750000E+2,8.740000E+2,8.730000E+2,8.720000E+2,8.710000E+2,8.710000E+2,8.700000E+2,8.690000E+2,8.680000E+2,8.680000E+2,8.670000E+2,8.660000E+2,8.650000E+2,8.650000E+2,8.640000E+2,8.630000E+2,8.620000E+2,8.610000E+2,8.610000E+2,8.600000E+2,8.590000E+2,8.580000E+2,8.580000E+2,8.570000E+2,8.560000E+2,8.550000E+2,8.540000E+2,8.540000E+2,8.530000E+2,8.520000E+2,8.510000E+2,8.510000E+2,8.500000E+2,8.490000E+2,8.480000E+2,8.480000E+2,8.470000E+2,8.460000E+2,8.450000E+2,8.440000E+2,8.440000E+2,8.430000E+2,8.420000E+2,8.410000E+2,8.410000E+2,8.400000E+2,8.390000E+2,8.380000E+2,8.380000E+2,8.370000E+2,8.360000E+2,8.350000E+2,8.340000E+2,8.340000E+2,8.330000E+2,8.320000E+2,8.310000E+2,8.310000E+2,8.300000E+2,8.290000E+2,8.280000E+2,8.280000E+2,8.270000E+2,8.260000E+2,8.250000E+2,8.240000E+2,8.240000E+2,8.230000E+2,8.220000E+2,8.210000E+2,8.210000E+2,8.200000E+2,8.190000E+2,8.180000E+2,8.180000E+2,8.170000E+2,8.160000E+2,8.150000E+2,8.140000E+2,8.140000E+2,8.130000E+2,8.120000E+2,8.110000E+2,8.110000E+2,8.100000E+2,8.090000E+2,8.080000E+2,8.080000E+2,8.070000E+2,8.060000E+2,8.050000E+2,8.040000E+2,8.040000E+2,8.030000E+2,8.020000E+2,8.010000E+2,8.010000E+2,8.000000E+2,7.990000E+2,7.980000E+2,7.980000E+2,7.970000E+2,7.960000E+2,7.950000E+2,7.940000E+2,7.940000E+2,7.930000E+2,7.920000E+2,7.910000E+2,7.910000E+2,7.900000E+2,7.890000E+2,7.880000E+2,7.880000E+2,7.870000E+2,7.860000E+2,7.850000E+2,7.840000E+2,7.840000E+2,7.830000E+2,7.820000E+2,7.810000E+2,7.810000E+2,7.800000E+2,7.790000E+2,7.780000E+2,7.780000E+2,7.770000E+2,7.760000E+2,7.750000E+2,7.740000E+2,7.740000E+2,7.730000E+2,7.720000E+2,7.710000E+2,7.710000E+2,7.700000E+2,7.690000E+2,7.680000E+2,7.680000E+2,7.670000E+2,7.660000E+2,7.650000E+2,7.640000E+2,7.640000E+2,7.630000E+2,7.620000E+2,7.610000E+2,7.610000E+2,7.600000E+2,7.590000E+2,7.580000E+2,7.580000E+2,7.570000E+2,7.560000E+2,7.550000E+2,7.540000E+2,7.540000E+2,7.530000E+2,7.520000E+2,7.510000E+2,7.510000E+2,7.500000E+2,7.490000E+2,7.480000E+2,7.480000E+2,7.470000E+2,7.460000E+2,7.450000E+2,7.440000E+2,7.440000E+2,7.430000E+2,7.420000E+2,7.410000E+2,7.410000E+2,7.400000E+2,7.390000E+2,7.380000E+2,7.380000E+2,7.370000E+2,7.360000E+2,7.350000E+2,7.340000E+2,7.340000E+2,7.330000E+2,7.320000E+2,7.310000E+2,7.310000E+2,7.300000E+2,7.290000E+2,7.280000E+2,7.280000E+2,7.270000E+2,7.260000E+2,7.250000E+2,7.240000E+2,7.240000E+2,7.230000E+2,7.220000E+2,7.210000E+2,7.210000E+2,7.200000E+2,7.190000E+2,7.180000E+2,7.180000E+2,7.170000E+2,7.160000E+2,7.150000E+2,7.140000E+2,7.140000E+2,7.130000E+2,7.120000E+2,7.110000E+2,7.110000E+2,7.100000E+2,7.090000E+2,7.080000E+2,7.080000E+2,7.070000E+2,7.060000E+2,7.050000E+2,7.040000E+2,7.040000E+2,7.030000E+2,7.020000E+2,7.010000E+2,7.010000E+2,7.000000E+2,6.990000E+2,6.980000E+2,6.980000E+2,6.970000E+2,6.960000E+2,6.950000E+2,6.940000E+2,6.940000E+2,6.930000E+2,6.920000E+2,6.910000E+2,6.910000E+2])
+        self.viscosity.data           = np.array([4.180000E-4,3.800000E-4,3.440000E-4,3.120000E-4,2.810000E-4,2.530000E-4,2.280000E-4,2.040000E-4,1.830000E-4,1.640000E-4,1.470000E-4,1.320000E-4,1.180000E-4,1.060000E-4,9.600000E-5,8.600000E-5,7.900000E-5,7.500000E-5,7.100000E-5,6.800000E-5,6.500000E-5,6.200000E-5,5.900000E-5,5.600000E-5,5.300000E-5,5.000000E-5,4.700000E-5,4.500000E-5,4.200000E-5,4.000000E-5,3.800000E-5,3.500000E-5,3.300000E-5,3.100000E-5,2.900000E-5,2.800000E-5,2.600000E-5,2.400000E-5,2.300000E-5,2.100000E-5,2.000000E-5,2.000000E-5,1.900000E-5,1.800000E-5,1.700000E-5,1.600000E-5,1.600000E-5,1.500000E-5,1.400000E-5,1.400000E-5,1.300000E-5,1.200000E-5,1.200000E-5,1.100000E-5,1.100000E-5,1.000000E-5,9.900000E-6,9.500000E-6,9.200000E-6,8.800000E-6,8.500000E-6,8.300000E-6,8.000000E-6,7.800000E-6,7.600000E-6,7.300000E-6,7.100000E-6,6.800000E-6,6.600000E-6,6.400000E-6,6.200000E-6,6.000000E-6,5.800000E-6,5.700000E-6,5.500000E-6,5.300000E-6,5.200000E-6,5.000000E-6,4.900000E-6,4.800000E-6,4.600000E-6,4.500000E-6,4.400000E-6,4.300000E-6,4.200000E-6,4.100000E-6,4.000000E-6,3.900000E-6,3.800000E-6,3.700000E-6,3.600000E-6,3.500000E-6,3.500000E-6,3.400000E-6,3.300000E-6,3.200000E-6,3.200000E-6,3.100000E-6,3.000000E-6,3.000000E-6,2.900000E-6,2.800000E-6,2.800000E-6,2.700000E-6,2.700000E-6,2.600000E-6,2.600000E-6,2.500000E-6,2.500000E-6,2.500000E-6,2.400000E-6,2.400000E-6,2.300000E-6,2.300000E-6,2.200000E-6,2.200000E-6,2.200000E-6,2.100000E-6,2.100000E-6,2.000000E-6,2.000000E-6,2.000000E-6,2.000000E-6,1.900000E-6,1.900000E-6,1.900000E-6,1.800000E-6,1.800000E-6,1.800000E-6,1.800000E-6,1.700000E-6,1.700000E-6,1.700000E-6,1.600000E-6,1.600000E-6,1.600000E-6,1.600000E-6,1.600000E-6,1.500000E-6,1.500000E-6,1.500000E-6,1.500000E-6,1.500000E-6,1.400000E-6,1.400000E-6,1.400000E-6,1.400000E-6,1.400000E-6,1.300000E-6,1.300000E-6,1.300000E-6,1.300000E-6,1.300000E-6,1.300000E-6,1.200000E-6,1.200000E-6,1.200000E-6,1.200000E-6,1.200000E-6,1.200000E-6,1.100000E-6,1.100000E-6,1.100000E-6,1.100000E-6,1.100000E-6,1.100000E-6,1.100000E-6,1.100000E-6,1.000000E-6,1.000000E-6,1.000000E-6,1.000000E-6,1.000000E-6,9.900000E-7,9.800000E-7,9.700000E-7,9.600000E-7,9.500000E-7,9.400000E-7,9.300000E-7,9.200000E-7,9.100000E-7,9.000000E-7,8.900000E-7,8.800000E-7,8.800000E-7,8.700000E-7,8.600000E-7,8.500000E-7,8.400000E-7,8.300000E-7,8.200000E-7,8.200000E-7,8.100000E-7,8.000000E-7,7.900000E-7,7.900000E-7,7.800000E-7,7.700000E-7,7.600000E-7,7.600000E-7,7.500000E-7,7.400000E-7,7.400000E-7,7.300000E-7,7.200000E-7,7.200000E-7,7.100000E-7,7.000000E-7,7.000000E-7,6.900000E-7,6.800000E-7,6.800000E-7,6.700000E-7,6.700000E-7,6.600000E-7,6.600000E-7,6.500000E-7,6.400000E-7,6.400000E-7,6.300000E-7,6.300000E-7,6.200000E-7,6.200000E-7,6.100000E-7,6.100000E-7,6.000000E-7,6.000000E-7,5.900000E-7,5.900000E-7,5.800000E-7,5.800000E-7,5.700000E-7,5.700000E-7,5.600000E-7,5.600000E-7,5.600000E-7,5.500000E-7,5.500000E-7,5.400000E-7,5.400000E-7,5.300000E-7,5.300000E-7,5.300000E-7,5.200000E-7,5.200000E-7,5.100000E-7,5.100000E-7,5.100000E-7,5.000000E-7,5.000000E-7,5.000000E-7,4.900000E-7,4.900000E-7,4.900000E-7,4.800000E-7,4.800000E-7,4.700000E-7,4.700000E-7,4.700000E-7,4.600000E-7,4.600000E-7,4.600000E-7,4.600000E-7,4.500000E-7,4.500000E-7,4.500000E-7,4.400000E-7,4.400000E-7,4.400000E-7,4.300000E-7,4.300000E-7,4.300000E-7,4.200000E-7,4.200000E-7,4.200000E-7,4.200000E-7,4.100000E-7,4.100000E-7,4.100000E-7,4.100000E-7,4.000000E-7,4.000000E-7,4.000000E-7,4.000000E-7,3.900000E-7,3.900000E-7,3.900000E-7,3.900000E-7,3.800000E-7,3.800000E-7,3.800000E-7,3.800000E-7,3.700000E-7,3.700000E-7,3.700000E-7,3.700000E-7,3.600000E-7,3.600000E-7,3.600000E-7,3.600000E-7,3.600000E-7,3.500000E-7,3.500000E-7,3.500000E-7,3.500000E-7,3.400000E-7,3.400000E-7,3.400000E-7,3.400000E-7,3.400000E-7,3.300000E-7,3.300000E-7,3.300000E-7,3.300000E-7,3.300000E-7,3.300000E-7,3.200000E-7,3.200000E-7,3.200000E-7,3.200000E-7,3.200000E-7,3.100000E-7,3.100000E-7,3.100000E-7,3.100000E-7,3.100000E-7,3.100000E-7,3.000000E-7,3.000000E-7,3.000000E-7,3.000000E-7,3.000000E-7,3.000000E-7,2.900000E-7,2.900000E-7,2.900000E-7,2.900000E-7,2.900000E-7,2.900000E-7,2.800000E-7,2.800000E-7,2.800000E-7,2.800000E-7,2.800000E-7,2.800000E-7,2.800000E-7,2.700000E-7,2.700000E-7,2.700000E-7,2.700000E-7,2.700000E-7,2.700000E-7,2.700000E-7,2.600000E-7,2.600000E-7,2.600000E-7,2.600000E-7,2.600000E-7,2.600000E-7,2.600000E-7,2.600000E-7,2.500000E-7,2.500000E-7,2.500000E-7,2.500000E-7,2.500000E-7,2.500000E-7,2.500000E-7,2.500000E-7,2.400000E-7,2.400000E-7,2.400000E-7,2.400000E-7,2.400000E-7,2.400000E-7,2.400000E-7,2.400000E-7,2.400000E-7,2.300000E-7,2.300000E-7,2.300000E-7,2.300000E-7,2.300000E-7,2.300000E-7,2.300000E-7])
+        self.specific_heat.data       = np.array([1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3])
+        self.conductivity.data        = np.array([1.180000E-1,1.180000E-1,1.180000E-1,1.180000E-1,1.180000E-1,1.180000E-1,1.180000E-1,1.180000E-1,1.180000E-1,1.180000E-1,1.180000E-1,1.180000E-1,1.180000E-1,1.180000E-1,1.180000E-1,1.180000E-1,1.180000E-1,1.180000E-1,1.180000E-1,1.180000E-1,1.180000E-1,1.180000E-1,1.180000E-1,1.180000E-1,1.180000E-1,1.180000E-1,1.180000E-1,1.180000E-1,1.180000E-1,1.180000E-1,1.180000E-1,1.180000E-1,1.180000E-1,1.180000E-1,1.180000E-1,1.180000E-1,1.180000E-1,1.180000E-1,1.180000E-1,1.180000E-1,1.180000E-1,1.180000E-1,1.180000E-1,1.170000E-1,1.170000E-1,1.170000E-1,1.170000E-1,1.170000E-1,1.170000E-1,1.170000E-1,1.170000E-1,1.170000E-1,1.170000E-1,1.170000E-1,1.170000E-1,1.170000E-1,1.170000E-1,1.170000E-1,1.170000E-1,1.170000E-1,1.170000E-1,1.170000E-1,1.170000E-1,1.170000E-1,1.170000E-1,1.170000E-1,1.170000E-1,1.170000E-1,1.170000E-1,1.170000E-1,1.160000E-1,1.160000E-1,1.160000E-1,1.160000E-1,1.160000E-1,1.160000E-1,1.160000E-1,1.160000E-1,1.160000E-1,1.160000E-1,1.160000E-1,1.160000E-1,1.160000E-1,1.160000E-1,1.160000E-1,1.160000E-1,1.160000E-1,1.160000E-1,1.160000E-1,1.160000E-1,1.160000E-1,1.160000E-1,1.150000E-1,1.150000E-1,1.150000E-1,1.150000E-1,1.150000E-1,1.150000E-1,1.150000E-1,1.150000E-1,1.150000E-1,1.150000E-1,1.150000E-1,1.150000E-1,1.150000E-1,1.150000E-1,1.150000E-1,1.150000E-1,1.150000E-1,1.150000E-1,1.140000E-1,1.140000E-1,1.140000E-1,1.140000E-1,1.140000E-1,1.140000E-1,1.140000E-1,1.140000E-1,1.140000E-1,1.140000E-1,1.140000E-1,1.140000E-1,1.140000E-1,1.140000E-1,1.140000E-1,1.140000E-1,1.140000E-1,1.130000E-1,1.130000E-1,1.130000E-1,1.130000E-1,1.130000E-1,1.130000E-1,1.130000E-1,1.130000E-1,1.130000E-1,1.130000E-1,1.130000E-1,1.130000E-1,1.130000E-1,1.130000E-1,1.130000E-1,1.120000E-1,1.120000E-1,1.120000E-1,1.120000E-1,1.120000E-1,1.120000E-1,1.120000E-1,1.120000E-1,1.120000E-1,1.120000E-1,1.120000E-1,1.120000E-1,1.120000E-1,1.110000E-1,1.110000E-1,1.110000E-1,1.110000E-1,1.110000E-1,1.110000E-1,1.110000E-1,1.110000E-1,1.110000E-1,1.110000E-1,1.110000E-1,1.110000E-1,1.110000E-1,1.100000E-1,1.100000E-1,1.100000E-1,1.100000E-1,1.100000E-1,1.100000E-1,1.100000E-1,1.100000E-1,1.100000E-1,1.100000E-1,1.100000E-1,1.100000E-1,1.090000E-1,1.090000E-1,1.090000E-1,1.090000E-1,1.090000E-1,1.090000E-1,1.090000E-1,1.090000E-1,1.090000E-1,1.090000E-1,1.090000E-1,1.080000E-1,1.080000E-1,1.080000E-1,1.080000E-1,1.080000E-1,1.080000E-1,1.080000E-1,1.080000E-1,1.080000E-1,1.080000E-1,1.080000E-1,1.070000E-1,1.070000E-1,1.070000E-1,1.070000E-1,1.070000E-1,1.070000E-1,1.070000E-1,1.070000E-1,1.070000E-1,1.070000E-1,1.060000E-1,1.060000E-1,1.060000E-1,1.060000E-1,1.060000E-1,1.060000E-1,1.060000E-1,1.060000E-1,1.060000E-1,1.060000E-1,1.050000E-1,1.050000E-1,1.050000E-1,1.050000E-1,1.050000E-1,1.050000E-1,1.050000E-1,1.050000E-1,1.050000E-1,1.050000E-1,1.040000E-1,1.040000E-1,1.040000E-1,1.040000E-1,1.040000E-1,1.040000E-1,1.040000E-1,1.040000E-1,1.040000E-1,1.030000E-1,1.030000E-1,1.030000E-1,1.030000E-1,1.030000E-1,1.030000E-1,1.030000E-1,1.030000E-1,1.030000E-1,1.020000E-1,1.020000E-1,1.020000E-1,1.020000E-1,1.020000E-1,1.020000E-1,1.020000E-1,1.020000E-1,1.020000E-1,1.010000E-1,1.010000E-1,1.010000E-1,1.010000E-1,1.010000E-1,1.010000E-1,1.010000E-1,1.010000E-1,1.000000E-1,1.000000E-1,1.000000E-1,1.000000E-1,1.000000E-1,1.000000E-1,1.000000E-1,1.000000E-1,9.900000E-2,9.900000E-2,9.900000E-2,9.900000E-2,9.900000E-2,9.900000E-2,9.900000E-2,9.900000E-2,9.800000E-2,9.800000E-2,9.800000E-2,9.800000E-2,9.800000E-2,9.800000E-2,9.800000E-2,9.800000E-2,9.700000E-2,9.700000E-2,9.700000E-2,9.700000E-2,9.700000E-2,9.700000E-2,9.700000E-2,9.700000E-2,9.600000E-2,9.600000E-2,9.600000E-2,9.600000E-2,9.600000E-2,9.600000E-2,9.600000E-2,9.500000E-2,9.500000E-2,9.500000E-2,9.500000E-2,9.500000E-2,9.500000E-2,9.500000E-2,9.400000E-2,9.400000E-2,9.400000E-2,9.400000E-2,9.400000E-2,9.400000E-2,9.400000E-2,9.300000E-2,9.300000E-2,9.300000E-2,9.300000E-2,9.300000E-2,9.300000E-2,9.300000E-2,9.200000E-2,9.200000E-2,9.200000E-2,9.200000E-2,9.200000E-2,9.200000E-2,9.200000E-2,9.100000E-2,9.100000E-2,9.100000E-2,9.100000E-2,9.100000E-2,9.100000E-2,9.100000E-2,9.000000E-2,9.000000E-2,9.000000E-2,9.000000E-2,9.000000E-2,9.000000E-2,9.000000E-2,8.900000E-2,8.900000E-2,8.900000E-2,8.900000E-2,8.900000E-2,8.900000E-2,8.800000E-2,8.800000E-2,8.800000E-2,8.800000E-2,8.800000E-2,8.800000E-2,8.700000E-2,8.700000E-2,8.700000E-2,8.700000E-2,8.700000E-2,8.700000E-2,8.700000E-2,8.600000E-2,8.600000E-2,8.600000E-2,8.600000E-2,8.600000E-2,8.600000E-2,8.500000E-2,8.500000E-2,8.500000E-2,8.500000E-2,8.500000E-2,8.500000E-2,8.400000E-2,8.400000E-2,8.400000E-2,8.400000E-2,8.400000E-2,8.400000E-2,8.300000E-2])
+        self.saturation_pressure.data = np.array([     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,3.000000E+3,3.000000E+3,3.000000E+3,3.000000E+3,3.000000E+3,3.000000E+3,3.000000E+3,3.000000E+3,3.000000E+3,3.000000E+3,4.000000E+3,4.000000E+3,4.000000E+3,4.000000E+3,4.000000E+3,4.000000E+3,4.000000E+3,4.000000E+3,5.000000E+3,5.000000E+3,5.000000E+3,5.000000E+3,5.000000E+3,5.000000E+3,6.000000E+3,6.000000E+3,6.000000E+3,6.000000E+3,6.000000E+3,6.000000E+3,7.000000E+3,7.000000E+3,7.000000E+3,7.000000E+3,8.000000E+3,8.000000E+3,8.000000E+3,8.000000E+3,9.000000E+3,9.000000E+3,9.000000E+3,9.000000E+3,1.000000E+4,1.000000E+4,1.000000E+4,1.000000E+4,1.100000E+4,1.100000E+4,1.100000E+4,1.200000E+4,1.200000E+4,1.200000E+4,1.300000E+4,1.300000E+4,1.300000E+4,1.400000E+4,1.400000E+4,1.500000E+4,1.500000E+4,1.500000E+4,1.600000E+4,1.600000E+4,1.700000E+4,1.700000E+4,1.800000E+4,1.800000E+4,1.900000E+4,1.900000E+4,1.900000E+4,2.000000E+4,2.100000E+4,2.100000E+4,2.200000E+4,2.200000E+4,2.300000E+4,2.300000E+4,2.400000E+4,2.500000E+4,2.500000E+4,2.600000E+4,2.600000E+4,2.700000E+4,2.800000E+4,2.900000E+4,2.900000E+4,3.000000E+4,3.100000E+4,3.100000E+4,3.200000E+4,3.300000E+4,3.400000E+4,3.500000E+4,3.500000E+4,3.600000E+4,3.700000E+4,3.800000E+4,3.900000E+4,4.000000E+4,4.100000E+4,4.200000E+4,4.300000E+4,4.400000E+4,4.500000E+4,4.600000E+4,4.700000E+4,4.800000E+4,4.900000E+4,5.000000E+4,5.100000E+4,5.300000E+4,5.400000E+4,5.500000E+4,5.600000E+4,5.800000E+4,5.900000E+4,6.000000E+4,6.100000E+4,6.300000E+4,6.400000E+4,6.600000E+4,6.700000E+4,6.900000E+4,7.000000E+4,7.200000E+4,7.300000E+4,7.500000E+4,7.600000E+4,7.800000E+4,8.000000E+4,8.100000E+4,8.300000E+4,8.500000E+4,8.700000E+4,8.900000E+4,9.000000E+4,9.200000E+4,9.400000E+4,9.600000E+4,9.800000E+4,1.000000E+5,1.020000E+5,1.050000E+5,1.070000E+5,1.090000E+5,1.110000E+5,1.130000E+5,1.160000E+5,1.180000E+5,1.200000E+5,1.230000E+5,1.250000E+5,1.280000E+5,1.300000E+5,1.330000E+5,1.360000E+5,1.380000E+5,1.410000E+5,1.440000E+5,1.470000E+5,1.500000E+5,1.520000E+5,1.550000E+5,1.580000E+5,1.610000E+5,1.650000E+5,1.680000E+5,1.710000E+5,1.740000E+5,1.780000E+5,1.810000E+5,1.840000E+5,1.880000E+5,1.920000E+5,1.950000E+5])
+        self.Tmin        = np.min(self.temperature.data)
+        self.Tmax        = np.max(self.temperature.data)
+        self.TminPsat    = np.min(self.temperature.data[~np.isnan(self.saturation_pressure.data)])
+        self.name        = "PHR"
+        self.description = "Paratherm "+ self.name[1:]
+        self.reference   = "Paratherm2013"
+        self.reshapeAll()
+
+
+
+
+class PLR(PureData):
+    """
+    The Paratherm LR low-range heat transfer fluid is rated for service from
+    -40 F to 400 F (-40 C to 204 C). Non-aromatic, this non-toxic liquid is safe
+    to use and is easy to dispose. Tough and durable, the Paratherm LR fluid is
+    designed for a broad variety of cooling and heating applications. It is
+    engineered to provide extended performance under rugged operating
+    conditions, yet is easy and safe to handle.
+    """
+    def __init__(self):
+        PureData.__init__(self)
+        self.density.source           = self.density.SOURCE_DATA
+        self.viscosity.source         = self.viscosity.SOURCE_DATA
+        self.specific_heat.source     = self.specific_heat.SOURCE_DATA
+        self.conductivity.source      = self.conductivity.SOURCE_DATA
+        self.saturation_pressure.source = self.saturation_pressure.SOURCE_DATA
+        self.temperature.data         = np.array([1.881500E+2,1.891500E+2,1.901500E+2,1.911500E+2,1.921500E+2,1.931500E+2,1.941500E+2,1.951500E+2,1.961500E+2,1.971500E+2,1.981500E+2,1.991500E+2,2.001500E+2,2.011500E+2,2.021500E+2,2.031500E+2,2.041500E+2,2.051500E+2,2.061500E+2,2.071500E+2,2.081500E+2,2.091500E+2,2.101500E+2,2.111500E+2,2.121500E+2,2.131500E+2,2.141500E+2,2.151500E+2,2.161500E+2,2.171500E+2,2.181500E+2,2.191500E+2,2.201500E+2,2.211500E+2,2.221500E+2,2.231500E+2,2.241500E+2,2.251500E+2,2.261500E+2,2.271500E+2,2.281500E+2,2.291500E+2,2.301500E+2,2.311500E+2,2.321500E+2,2.331500E+2,2.341500E+2,2.351500E+2,2.361500E+2,2.371500E+2,2.381500E+2,2.391500E+2,2.401500E+2,2.411500E+2,2.421500E+2,2.431500E+2,2.441500E+2,2.451500E+2,2.461500E+2,2.471500E+2,2.481500E+2,2.491500E+2,2.501500E+2,2.511500E+2,2.521500E+2,2.531500E+2,2.541500E+2,2.551500E+2,2.561500E+2,2.571500E+2,2.581500E+2,2.591500E+2,2.601500E+2,2.611500E+2,2.621500E+2,2.631500E+2,2.641500E+2,2.651500E+2,2.661500E+2,2.671500E+2,2.681500E+2,2.691500E+2,2.701500E+2,2.711500E+2,2.721500E+2,2.731500E+2,2.741500E+2,2.751500E+2,2.761500E+2,2.771500E+2,2.781500E+2,2.791500E+2,2.801500E+2,2.811500E+2,2.821500E+2,2.831500E+2,2.841500E+2,2.851500E+2,2.861500E+2,2.871500E+2,2.881500E+2,2.891500E+2,2.901500E+2,2.911500E+2,2.921500E+2,2.931500E+2,2.941500E+2,2.951500E+2,2.961500E+2,2.971500E+2,2.981500E+2,2.991500E+2,3.001500E+2,3.011500E+2,3.021500E+2,3.031500E+2,3.041500E+2,3.051500E+2,3.061500E+2,3.071500E+2,3.081500E+2,3.091500E+2,3.101500E+2,3.111500E+2,3.121500E+2,3.131500E+2,3.141500E+2,3.151500E+2,3.161500E+2,3.171500E+2,3.181500E+2,3.191500E+2,3.201500E+2,3.211500E+2,3.221500E+2,3.231500E+2,3.241500E+2,3.251500E+2,3.261500E+2,3.271500E+2,3.281500E+2,3.291500E+2,3.301500E+2,3.311500E+2,3.321500E+2,3.331500E+2,3.341500E+2,3.351500E+2,3.361500E+2,3.371500E+2,3.381500E+2,3.391500E+2,3.401500E+2,3.411500E+2,3.421500E+2,3.431500E+2,3.441500E+2,3.451500E+2,3.461500E+2,3.471500E+2,3.481500E+2,3.491500E+2,3.501500E+2,3.511500E+2,3.521500E+2,3.531500E+2,3.541500E+2,3.551500E+2,3.561500E+2,3.571500E+2,3.581500E+2,3.591500E+2,3.601500E+2,3.611500E+2,3.621500E+2,3.631500E+2,3.641500E+2,3.651500E+2,3.661500E+2,3.671500E+2,3.681500E+2,3.691500E+2,3.701500E+2,3.711500E+2,3.721500E+2,3.731500E+2,3.741500E+2,3.751500E+2,3.761500E+2,3.771500E+2,3.781500E+2,3.791500E+2,3.801500E+2,3.811500E+2,3.821500E+2,3.831500E+2,3.841500E+2,3.851500E+2,3.861500E+2,3.871500E+2,3.881500E+2,3.891500E+2,3.901500E+2,3.911500E+2,3.921500E+2,3.931500E+2,3.941500E+2,3.951500E+2,3.961500E+2,3.971500E+2,3.981500E+2,3.991500E+2,4.001500E+2,4.011500E+2,4.021500E+2,4.031500E+2,4.041500E+2,4.051500E+2,4.061500E+2,4.071500E+2,4.081500E+2,4.091500E+2,4.101500E+2,4.111500E+2,4.121500E+2,4.131500E+2,4.141500E+2,4.151500E+2,4.161500E+2,4.171500E+2,4.181500E+2,4.191500E+2,4.201500E+2,4.211500E+2,4.221500E+2,4.231500E+2,4.241500E+2,4.251500E+2,4.261500E+2,4.271500E+2,4.281500E+2,4.291500E+2,4.301500E+2,4.311500E+2,4.321500E+2,4.331500E+2,4.341500E+2,4.351500E+2,4.361500E+2,4.371500E+2,4.381500E+2,4.391500E+2,4.401500E+2,4.411500E+2,4.421500E+2,4.431500E+2,4.441500E+2,4.451500E+2,4.461500E+2,4.471500E+2,4.481500E+2,4.491500E+2,4.501500E+2,4.511500E+2,4.521500E+2,4.531500E+2,4.541500E+2,4.551500E+2,4.561500E+2,4.571500E+2,4.581500E+2,4.591500E+2,4.601500E+2,4.611500E+2,4.621500E+2,4.631500E+2,4.641500E+2,4.651500E+2,4.661500E+2,4.671500E+2,4.681500E+2,4.691500E+2,4.701500E+2,4.711500E+2,4.721500E+2,4.731500E+2,4.741500E+2,4.751500E+2,4.761500E+2,4.771500E+2,4.781500E+2,4.791500E+2,4.801500E+2,4.811500E+2,4.821500E+2,4.831500E+2,4.841500E+2,4.851500E+2,4.861500E+2,4.871500E+2,4.881500E+2,4.891500E+2,4.901500E+2,4.911500E+2,4.921500E+2,4.931500E+2,4.941500E+2,4.951500E+2,4.961500E+2,4.971500E+2,4.981500E+2,4.991500E+2,5.001500E+2,5.011500E+2,5.021500E+2,5.031500E+2])
+        self.density.data             = np.array([8.390000E+2,8.380000E+2,8.380000E+2,8.370000E+2,8.360000E+2,8.350000E+2,8.350000E+2,8.340000E+2,8.330000E+2,8.330000E+2,8.320000E+2,8.310000E+2,8.300000E+2,8.300000E+2,8.290000E+2,8.280000E+2,8.270000E+2,8.270000E+2,8.260000E+2,8.250000E+2,8.240000E+2,8.240000E+2,8.230000E+2,8.220000E+2,8.210000E+2,8.210000E+2,8.200000E+2,8.190000E+2,8.190000E+2,8.180000E+2,8.170000E+2,8.160000E+2,8.160000E+2,8.150000E+2,8.140000E+2,8.130000E+2,8.130000E+2,8.120000E+2,8.110000E+2,8.100000E+2,8.100000E+2,8.090000E+2,8.080000E+2,8.070000E+2,8.070000E+2,8.060000E+2,8.050000E+2,8.050000E+2,8.040000E+2,8.030000E+2,8.020000E+2,8.020000E+2,8.010000E+2,8.000000E+2,7.990000E+2,7.990000E+2,7.980000E+2,7.970000E+2,7.960000E+2,7.960000E+2,7.950000E+2,7.940000E+2,7.930000E+2,7.930000E+2,7.920000E+2,7.910000E+2,7.910000E+2,7.900000E+2,7.890000E+2,7.880000E+2,7.880000E+2,7.870000E+2,7.860000E+2,7.850000E+2,7.850000E+2,7.840000E+2,7.830000E+2,7.820000E+2,7.820000E+2,7.810000E+2,7.800000E+2,7.800000E+2,7.790000E+2,7.780000E+2,7.770000E+2,7.770000E+2,7.760000E+2,7.750000E+2,7.740000E+2,7.740000E+2,7.730000E+2,7.720000E+2,7.710000E+2,7.710000E+2,7.700000E+2,7.690000E+2,7.680000E+2,7.680000E+2,7.670000E+2,7.660000E+2,7.660000E+2,7.650000E+2,7.640000E+2,7.630000E+2,7.630000E+2,7.620000E+2,7.610000E+2,7.600000E+2,7.600000E+2,7.590000E+2,7.580000E+2,7.570000E+2,7.570000E+2,7.560000E+2,7.550000E+2,7.540000E+2,7.540000E+2,7.530000E+2,7.520000E+2,7.520000E+2,7.510000E+2,7.500000E+2,7.490000E+2,7.490000E+2,7.480000E+2,7.470000E+2,7.460000E+2,7.460000E+2,7.450000E+2,7.440000E+2,7.430000E+2,7.430000E+2,7.420000E+2,7.410000E+2,7.410000E+2,7.400000E+2,7.390000E+2,7.380000E+2,7.380000E+2,7.370000E+2,7.360000E+2,7.350000E+2,7.350000E+2,7.340000E+2,7.330000E+2,7.320000E+2,7.320000E+2,7.310000E+2,7.300000E+2,7.290000E+2,7.290000E+2,7.280000E+2,7.270000E+2,7.270000E+2,7.260000E+2,7.250000E+2,7.240000E+2,7.240000E+2,7.230000E+2,7.220000E+2,7.210000E+2,7.210000E+2,7.200000E+2,7.190000E+2,7.180000E+2,7.180000E+2,7.170000E+2,7.160000E+2,7.150000E+2,7.150000E+2,7.140000E+2,7.130000E+2,7.130000E+2,7.120000E+2,7.110000E+2,7.100000E+2,7.100000E+2,7.090000E+2,7.080000E+2,7.070000E+2,7.070000E+2,7.060000E+2,7.050000E+2,7.040000E+2,7.040000E+2,7.030000E+2,7.020000E+2,7.010000E+2,7.010000E+2,7.000000E+2,6.990000E+2,6.990000E+2,6.980000E+2,6.970000E+2,6.960000E+2,6.960000E+2,6.950000E+2,6.940000E+2,6.930000E+2,6.930000E+2,6.920000E+2,6.910000E+2,6.900000E+2,6.900000E+2,6.890000E+2,6.880000E+2,6.880000E+2,6.870000E+2,6.860000E+2,6.850000E+2,6.850000E+2,6.840000E+2,6.830000E+2,6.820000E+2,6.820000E+2,6.810000E+2,6.800000E+2,6.790000E+2,6.790000E+2,6.780000E+2,6.770000E+2,6.760000E+2,6.760000E+2,6.750000E+2,6.740000E+2,6.740000E+2,6.730000E+2,6.720000E+2,6.710000E+2,6.710000E+2,6.700000E+2,6.690000E+2,6.680000E+2,6.680000E+2,6.670000E+2,6.660000E+2,6.650000E+2,6.650000E+2,6.640000E+2,6.630000E+2,6.620000E+2,6.620000E+2,6.610000E+2,6.600000E+2,6.600000E+2,6.590000E+2,6.580000E+2,6.570000E+2,6.570000E+2,6.560000E+2,6.550000E+2,6.540000E+2,6.540000E+2,6.530000E+2,6.520000E+2,6.510000E+2,6.510000E+2,6.500000E+2,6.490000E+2,6.490000E+2,6.480000E+2,6.470000E+2,6.460000E+2,6.460000E+2,6.450000E+2,6.440000E+2,6.430000E+2,6.430000E+2,6.420000E+2,6.410000E+2,6.400000E+2,6.400000E+2,6.390000E+2,6.380000E+2,6.370000E+2,6.370000E+2,6.360000E+2,6.350000E+2,6.350000E+2,6.340000E+2,6.330000E+2,6.320000E+2,6.320000E+2,6.310000E+2,6.300000E+2,6.290000E+2,6.290000E+2,6.280000E+2,6.270000E+2,6.260000E+2,6.260000E+2,6.250000E+2,6.240000E+2,6.230000E+2,6.230000E+2,6.220000E+2,6.210000E+2,6.210000E+2,6.200000E+2,6.190000E+2,6.180000E+2,6.180000E+2,6.170000E+2,6.160000E+2,6.150000E+2,6.150000E+2,6.140000E+2,6.130000E+2,6.120000E+2,6.120000E+2,6.110000E+2,6.100000E+2,6.090000E+2,6.090000E+2,6.080000E+2,6.070000E+2])
+        self.viscosity.data           = np.array([5.020000E-4,4.540000E-4,4.100000E-4,3.700000E-4,3.350000E-4,3.020000E-4,2.730000E-4,2.470000E-4,2.230000E-4,2.010000E-4,1.820000E-4,1.640000E-4,1.490000E-4,1.340000E-4,1.210000E-4,1.100000E-4,9.900000E-5,8.900000E-5,8.100000E-5,7.300000E-5,6.600000E-5,6.000000E-5,5.400000E-5,4.900000E-5,4.400000E-5,4.000000E-5,3.600000E-5,3.200000E-5,2.900000E-5,2.600000E-5,2.400000E-5,2.200000E-5,2.000000E-5,1.800000E-5,2.200000E-5,2.000000E-5,1.900000E-5,1.800000E-5,1.700000E-5,1.600000E-5,1.500000E-5,1.400000E-5,1.400000E-5,1.300000E-5,1.200000E-5,1.200000E-5,1.100000E-5,1.100000E-5,1.000000E-5,9.600000E-6,9.200000E-6,8.800000E-6,8.400000E-6,8.000000E-6,7.700000E-6,7.400000E-6,7.100000E-6,6.800000E-6,6.500000E-6,6.300000E-6,6.000000E-6,5.800000E-6,5.600000E-6,5.400000E-6,5.200000E-6,5.000000E-6,4.800000E-6,4.700000E-6,4.500000E-6,4.400000E-6,4.200000E-6,4.100000E-6,4.000000E-6,3.800000E-6,3.700000E-6,3.600000E-6,3.500000E-6,3.400000E-6,3.300000E-6,3.200000E-6,3.100000E-6,3.000000E-6,2.900000E-6,2.800000E-6,2.800000E-6,2.700000E-6,2.600000E-6,2.500000E-6,2.500000E-6,2.400000E-6,2.300000E-6,2.300000E-6,2.200000E-6,2.200000E-6,2.100000E-6,2.100000E-6,2.000000E-6,2.000000E-6,1.900000E-6,1.900000E-6,1.800000E-6,1.800000E-6,1.700000E-6,1.700000E-6,1.700000E-6,1.600000E-6,1.600000E-6,1.600000E-6,1.500000E-6,1.500000E-6,1.500000E-6,1.400000E-6,1.400000E-6,1.400000E-6,1.400000E-6,1.300000E-6,1.300000E-6,1.300000E-6,1.300000E-6,1.200000E-6,1.200000E-6,1.200000E-6,1.200000E-6,1.200000E-6,1.100000E-6,1.100000E-6,1.100000E-6,1.100000E-6,1.100000E-6,1.100000E-6,1.000000E-6,1.000000E-6,1.000000E-6,1.000000E-6,9.800000E-7,9.700000E-7,9.600000E-7,9.400000E-7,9.300000E-7,9.200000E-7,9.000000E-7,8.900000E-7,8.800000E-7,8.600000E-7,8.500000E-7,8.400000E-7,8.300000E-7,8.200000E-7,8.100000E-7,7.900000E-7,7.800000E-7,7.700000E-7,7.600000E-7,7.500000E-7,7.400000E-7,7.300000E-7,7.200000E-7,7.100000E-7,7.000000E-7,6.900000E-7,6.800000E-7,6.700000E-7,6.600000E-7,6.500000E-7,6.400000E-7,6.400000E-7,6.300000E-7,6.200000E-7,6.100000E-7,6.000000E-7,5.900000E-7,5.800000E-7,5.800000E-7,5.700000E-7,5.600000E-7,5.500000E-7,5.400000E-7,5.400000E-7,5.300000E-7,5.200000E-7,5.100000E-7,5.100000E-7,5.000000E-7,4.900000E-7,5.400000E-7,5.300000E-7,5.200000E-7,5.200000E-7,5.100000E-7,5.000000E-7,5.000000E-7,4.900000E-7,4.900000E-7,4.800000E-7,4.800000E-7,4.700000E-7,4.600000E-7,4.600000E-7,4.500000E-7,4.500000E-7,4.400000E-7,4.400000E-7,4.300000E-7,4.300000E-7,4.300000E-7,4.200000E-7,4.200000E-7,4.100000E-7,4.100000E-7,4.000000E-7,4.000000E-7,4.000000E-7,3.900000E-7,3.900000E-7,3.800000E-7,3.800000E-7,3.800000E-7,3.700000E-7,3.700000E-7,3.700000E-7,3.600000E-7,3.600000E-7,3.600000E-7,3.500000E-7,3.500000E-7,3.500000E-7,3.400000E-7,3.400000E-7,3.400000E-7,3.300000E-7,3.300000E-7,3.300000E-7,3.300000E-7,3.200000E-7,3.200000E-7,3.200000E-7,3.100000E-7,3.100000E-7,3.100000E-7,3.100000E-7,3.000000E-7,3.000000E-7,3.000000E-7,3.000000E-7,2.900000E-7,2.900000E-7,2.900000E-7,2.900000E-7,2.900000E-7,2.800000E-7,2.800000E-7,2.800000E-7,2.800000E-7,2.700000E-7,2.700000E-7,2.700000E-7,2.700000E-7,2.700000E-7,2.600000E-7,2.600000E-7,2.600000E-7,2.600000E-7,2.600000E-7,2.600000E-7,2.500000E-7,2.500000E-7,2.500000E-7,2.500000E-7,2.500000E-7,2.400000E-7,2.400000E-7,2.400000E-7,2.400000E-7,2.400000E-7,2.400000E-7,2.300000E-7,2.300000E-7,2.300000E-7,2.300000E-7,2.300000E-7,2.300000E-7,2.300000E-7,2.200000E-7,2.200000E-7,2.200000E-7,2.200000E-7,2.200000E-7,2.200000E-7,2.200000E-7,2.100000E-7,2.100000E-7,2.100000E-7,2.100000E-7,2.100000E-7,2.100000E-7,2.100000E-7,2.100000E-7,2.000000E-7,2.000000E-7,2.000000E-7,2.000000E-7,2.000000E-7,2.000000E-7,2.000000E-7,2.000000E-7,1.900000E-7,1.900000E-7,1.900000E-7,1.900000E-7,1.900000E-7,1.900000E-7,1.900000E-7,1.900000E-7,1.900000E-7,1.900000E-7,1.800000E-7])
+        self.specific_heat.data       = np.array([1.600000E+3,1.600000E+3,1.600000E+3,1.600000E+3,1.600000E+3,1.600000E+3,1.600000E+3,1.600000E+3,1.600000E+3,1.600000E+3,1.600000E+3,1.600000E+3,1.600000E+3,1.600000E+3,1.600000E+3,1.600000E+3,1.600000E+3,1.700000E+3,1.700000E+3,1.700000E+3,1.700000E+3,1.700000E+3,1.700000E+3,1.700000E+3,1.700000E+3,1.700000E+3,1.700000E+3,1.700000E+3,1.700000E+3,1.700000E+3,1.700000E+3,1.700000E+3,1.700000E+3,1.700000E+3,1.700000E+3,1.700000E+3,1.700000E+3,1.700000E+3,1.700000E+3,1.700000E+3,1.800000E+3,1.800000E+3,1.800000E+3,1.800000E+3,1.800000E+3,1.800000E+3,1.800000E+3,1.800000E+3,1.800000E+3,1.800000E+3,1.800000E+3,1.800000E+3,1.800000E+3,1.800000E+3,1.800000E+3,1.800000E+3,1.800000E+3,1.800000E+3,1.800000E+3,1.800000E+3,1.800000E+3,1.800000E+3,1.800000E+3,1.800000E+3,1.800000E+3,1.800000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3])
+        self.conductivity.data        = np.array([1.600000E-1,1.600000E-1,1.600000E-1,1.600000E-1,1.600000E-1,1.600000E-1,1.590000E-1,1.590000E-1,1.590000E-1,1.590000E-1,1.590000E-1,1.590000E-1,1.590000E-1,1.590000E-1,1.590000E-1,1.590000E-1,1.590000E-1,1.590000E-1,1.580000E-1,1.580000E-1,1.580000E-1,1.580000E-1,1.580000E-1,1.580000E-1,1.580000E-1,1.580000E-1,1.580000E-1,1.580000E-1,1.580000E-1,1.580000E-1,1.580000E-1,1.570000E-1,1.570000E-1,1.570000E-1,1.570000E-1,1.570000E-1,1.570000E-1,1.570000E-1,1.570000E-1,1.570000E-1,1.570000E-1,1.570000E-1,1.570000E-1,1.560000E-1,1.560000E-1,1.560000E-1,1.560000E-1,1.560000E-1,1.560000E-1,1.560000E-1,1.560000E-1,1.560000E-1,1.560000E-1,1.560000E-1,1.560000E-1,1.560000E-1,1.550000E-1,1.550000E-1,1.550000E-1,1.550000E-1,1.550000E-1,1.550000E-1,1.550000E-1,1.550000E-1,1.550000E-1,1.550000E-1,1.550000E-1,1.550000E-1,1.540000E-1,1.540000E-1,1.540000E-1,1.540000E-1,1.540000E-1,1.540000E-1,1.540000E-1,1.540000E-1,1.540000E-1,1.540000E-1,1.540000E-1,1.540000E-1,1.540000E-1,1.530000E-1,1.530000E-1,1.530000E-1,1.530000E-1,1.530000E-1,1.530000E-1,1.530000E-1,1.530000E-1,1.530000E-1,1.530000E-1,1.530000E-1,1.530000E-1,1.520000E-1,1.520000E-1,1.520000E-1,1.520000E-1,1.520000E-1,1.520000E-1,1.520000E-1,1.520000E-1,1.520000E-1,1.520000E-1,1.520000E-1,1.520000E-1,1.520000E-1,1.510000E-1,1.510000E-1,1.510000E-1,1.510000E-1,1.510000E-1,1.510000E-1,1.510000E-1,1.510000E-1,1.510000E-1,1.510000E-1,1.510000E-1,1.510000E-1,1.500000E-1,1.500000E-1,1.500000E-1,1.500000E-1,1.500000E-1,1.500000E-1,1.500000E-1,1.500000E-1,1.500000E-1,1.500000E-1,1.500000E-1,1.500000E-1,1.500000E-1,1.490000E-1,1.490000E-1,1.490000E-1,1.490000E-1,1.490000E-1,1.490000E-1,1.490000E-1,1.490000E-1,1.490000E-1,1.490000E-1,1.490000E-1,1.490000E-1,1.480000E-1,1.480000E-1,1.480000E-1,1.480000E-1,1.480000E-1,1.480000E-1,1.480000E-1,1.480000E-1,1.480000E-1,1.480000E-1,1.480000E-1,1.480000E-1,1.480000E-1,1.470000E-1,1.470000E-1,1.470000E-1,1.470000E-1,1.470000E-1,1.470000E-1,1.470000E-1,1.470000E-1,1.470000E-1,1.470000E-1,1.470000E-1,1.470000E-1,1.460000E-1,1.460000E-1,1.460000E-1,1.460000E-1,1.460000E-1,1.460000E-1,1.460000E-1,1.460000E-1,1.460000E-1,1.460000E-1,1.460000E-1,1.460000E-1,1.460000E-1,1.450000E-1,1.450000E-1,1.450000E-1,1.450000E-1,1.450000E-1,1.450000E-1,1.450000E-1,1.450000E-1,1.450000E-1,1.450000E-1,1.450000E-1,1.450000E-1,1.440000E-1,1.440000E-1,1.440000E-1,1.440000E-1,1.440000E-1,1.440000E-1,1.440000E-1,1.440000E-1,1.440000E-1,1.440000E-1,1.440000E-1,1.440000E-1,1.440000E-1,1.430000E-1,1.430000E-1,1.430000E-1,1.430000E-1,1.430000E-1,1.430000E-1,1.430000E-1,1.430000E-1,1.430000E-1,1.430000E-1,1.430000E-1,1.430000E-1,1.420000E-1,1.420000E-1,1.420000E-1,1.420000E-1,1.420000E-1,1.420000E-1,1.420000E-1,1.420000E-1,1.420000E-1,1.420000E-1,1.420000E-1,1.420000E-1,1.420000E-1,1.410000E-1,1.410000E-1,1.410000E-1,1.410000E-1,1.410000E-1,1.410000E-1,1.410000E-1,1.410000E-1,1.410000E-1,1.410000E-1,1.410000E-1,1.410000E-1,1.400000E-1,1.400000E-1,1.400000E-1,1.400000E-1,1.400000E-1,1.400000E-1,1.400000E-1,1.400000E-1,1.400000E-1,1.400000E-1,1.400000E-1,1.400000E-1,1.400000E-1,1.390000E-1,1.390000E-1,1.390000E-1,1.390000E-1,1.390000E-1,1.390000E-1,1.390000E-1,1.390000E-1,1.390000E-1,1.390000E-1,1.390000E-1,1.390000E-1,1.380000E-1,1.380000E-1,1.380000E-1,1.380000E-1,1.380000E-1,1.380000E-1,1.380000E-1,1.380000E-1,1.380000E-1,1.380000E-1,1.380000E-1,1.380000E-1,1.380000E-1,1.370000E-1,1.370000E-1,1.370000E-1,1.370000E-1,1.370000E-1,1.370000E-1,1.370000E-1,1.370000E-1,1.370000E-1,1.370000E-1,1.370000E-1,1.370000E-1,1.360000E-1,1.360000E-1,1.360000E-1,1.360000E-1,1.360000E-1,1.360000E-1,1.360000E-1,1.360000E-1,1.360000E-1,1.360000E-1,1.360000E-1,1.360000E-1,1.360000E-1,1.350000E-1,1.350000E-1,1.350000E-1,1.350000E-1,1.350000E-1,1.350000E-1,1.350000E-1,1.350000E-1,1.350000E-1,1.350000E-1])
+        self.saturation_pressure.data = np.array([     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,3.000000E+3,3.000000E+3,3.000000E+3,3.000000E+3,3.000000E+3,3.000000E+3,3.000000E+3,3.000000E+3,3.000000E+3,4.000000E+3,4.000000E+3,4.000000E+3,4.000000E+3,4.000000E+3,4.000000E+3,5.000000E+3,5.000000E+3,5.000000E+3,5.000000E+3,5.000000E+3,5.000000E+3,6.000000E+3,6.000000E+3,6.000000E+3,6.000000E+3,6.000000E+3,7.000000E+3,7.000000E+3,7.000000E+3,7.000000E+3,8.000000E+3,8.000000E+3,8.000000E+3,8.000000E+3,9.000000E+3,9.000000E+3,9.000000E+3,1.000000E+4,1.000000E+4,1.100000E+4,1.100000E+4,1.200000E+4,1.200000E+4,1.300000E+4,1.300000E+4,1.400000E+4,1.400000E+4,1.500000E+4,1.600000E+4,1.600000E+4,1.700000E+4,1.800000E+4,1.800000E+4,1.900000E+4,2.000000E+4,2.100000E+4,2.100000E+4,2.200000E+4,2.300000E+4,2.400000E+4,2.500000E+4,2.600000E+4,2.700000E+4,2.800000E+4,2.900000E+4,3.000000E+4,3.100000E+4,3.200000E+4,3.300000E+4,3.400000E+4,3.500000E+4,3.700000E+4,3.800000E+4,3.900000E+4,4.000000E+4,4.200000E+4,4.300000E+4,4.400000E+4,4.500000E+4,4.700000E+4,4.800000E+4,5.000000E+4,5.100000E+4,5.300000E+4,5.500000E+4,5.700000E+4,6.000000E+4,6.200000E+4,6.400000E+4,6.600000E+4,6.800000E+4,7.000000E+4,7.200000E+4,7.400000E+4,7.600000E+4,7.800000E+4,8.000000E+4,8.200000E+4,8.400000E+4,8.600000E+4,8.800000E+4,8.900000E+4,9.100000E+4,9.300000E+4,9.500000E+4,9.700000E+4,9.800000E+4,1.000000E+5,1.020000E+5,1.030000E+5,1.050000E+5,1.070000E+5,1.080000E+5,1.100000E+5,1.110000E+5,1.130000E+5,1.150000E+5,1.160000E+5,1.180000E+5,1.190000E+5,1.210000E+5,1.220000E+5,1.230000E+5,1.250000E+5,1.260000E+5,1.270000E+5,1.290000E+5,1.300000E+5,1.310000E+5,1.330000E+5,1.340000E+5,1.350000E+5,1.360000E+5,1.380000E+5,1.390000E+5,1.400000E+5])
+        self.Tmin        = np.min(self.temperature.data)
+        self.Tmax        = np.max(self.temperature.data)
+        self.TminPsat    = np.min(self.temperature.data[~np.isnan(self.saturation_pressure.data)])
+        self.name        = "PLR"
+        self.description = "Paratherm "+ self.name[1:]
+        self.reference   = "Paratherm2013"
+        self.reshapeAll()
+
+
+
+
+class PMR(PureData):
+    """
+    Paratherm MR is a food grade (NSF Certified) single fluid heating and
+    cooling from 36 F to 550 F. Eliminates design and maintenance problems
+    caused by steam/chilled water temperature control systems. Quick low-
+    temperature start-ups.
+    """
+    def __init__(self):
+        PureData.__init__(self)
+        self.density.source           = self.density.SOURCE_DATA
+        self.viscosity.source         = self.viscosity.SOURCE_DATA
+        self.specific_heat.source     = self.specific_heat.SOURCE_DATA
+        self.conductivity.source      = self.conductivity.SOURCE_DATA
+        self.saturation_pressure.source = self.saturation_pressure.SOURCE_DATA
+        self.temperature.data         = np.array([2.331500E+2,2.341500E+2,2.351500E+2,2.361500E+2,2.371500E+2,2.381500E+2,2.391500E+2,2.401500E+2,2.411500E+2,2.421500E+2,2.431500E+2,2.441500E+2,2.451500E+2,2.461500E+2,2.471500E+2,2.481500E+2,2.491500E+2,2.501500E+2,2.511500E+2,2.521500E+2,2.531500E+2,2.541500E+2,2.551500E+2,2.561500E+2,2.571500E+2,2.581500E+2,2.591500E+2,2.601500E+2,2.611500E+2,2.621500E+2,2.631500E+2,2.641500E+2,2.651500E+2,2.661500E+2,2.671500E+2,2.681500E+2,2.691500E+2,2.701500E+2,2.711500E+2,2.721500E+2,2.731500E+2,2.741500E+2,2.751500E+2,2.761500E+2,2.771500E+2,2.781500E+2,2.791500E+2,2.801500E+2,2.811500E+2,2.821500E+2,2.831500E+2,2.841500E+2,2.851500E+2,2.861500E+2,2.871500E+2,2.881500E+2,2.891500E+2,2.901500E+2,2.911500E+2,2.921500E+2,2.931500E+2,2.941500E+2,2.951500E+2,2.961500E+2,2.971500E+2,2.981500E+2,2.991500E+2,3.001500E+2,3.011500E+2,3.021500E+2,3.031500E+2,3.041500E+2,3.051500E+2,3.061500E+2,3.071500E+2,3.081500E+2,3.091500E+2,3.101500E+2,3.111500E+2,3.121500E+2,3.131500E+2,3.141500E+2,3.151500E+2,3.161500E+2,3.171500E+2,3.181500E+2,3.191500E+2,3.201500E+2,3.211500E+2,3.221500E+2,3.231500E+2,3.241500E+2,3.251500E+2,3.261500E+2,3.271500E+2,3.281500E+2,3.291500E+2,3.301500E+2,3.311500E+2,3.321500E+2,3.331500E+2,3.341500E+2,3.351500E+2,3.361500E+2,3.371500E+2,3.381500E+2,3.391500E+2,3.401500E+2,3.411500E+2,3.421500E+2,3.431500E+2,3.441500E+2,3.451500E+2,3.461500E+2,3.471500E+2,3.481500E+2,3.491500E+2,3.501500E+2,3.511500E+2,3.521500E+2,3.531500E+2,3.541500E+2,3.551500E+2,3.561500E+2,3.571500E+2,3.581500E+2,3.591500E+2,3.601500E+2,3.611500E+2,3.621500E+2,3.631500E+2,3.641500E+2,3.651500E+2,3.661500E+2,3.671500E+2,3.681500E+2,3.691500E+2,3.701500E+2,3.711500E+2,3.721500E+2,3.731500E+2,3.741500E+2,3.751500E+2,3.761500E+2,3.771500E+2,3.781500E+2,3.791500E+2,3.801500E+2,3.811500E+2,3.821500E+2,3.831500E+2,3.841500E+2,3.851500E+2,3.861500E+2,3.871500E+2,3.881500E+2,3.891500E+2,3.901500E+2,3.911500E+2,3.921500E+2,3.931500E+2,3.941500E+2,3.951500E+2,3.961500E+2,3.971500E+2,3.981500E+2,3.991500E+2,4.001500E+2,4.011500E+2,4.021500E+2,4.031500E+2,4.041500E+2,4.051500E+2,4.061500E+2,4.071500E+2,4.081500E+2,4.091500E+2,4.101500E+2,4.111500E+2,4.121500E+2,4.131500E+2,4.141500E+2,4.151500E+2,4.161500E+2,4.171500E+2,4.181500E+2,4.191500E+2,4.201500E+2,4.211500E+2,4.221500E+2,4.231500E+2,4.241500E+2,4.251500E+2,4.261500E+2,4.271500E+2,4.281500E+2,4.291500E+2,4.301500E+2,4.311500E+2,4.321500E+2,4.331500E+2,4.341500E+2,4.351500E+2,4.361500E+2,4.371500E+2,4.381500E+2,4.391500E+2,4.401500E+2,4.411500E+2,4.421500E+2,4.431500E+2,4.441500E+2,4.451500E+2,4.461500E+2,4.471500E+2,4.481500E+2,4.491500E+2,4.501500E+2,4.511500E+2,4.521500E+2,4.531500E+2,4.541500E+2,4.551500E+2,4.561500E+2,4.571500E+2,4.581500E+2,4.591500E+2,4.601500E+2,4.611500E+2,4.621500E+2,4.631500E+2,4.641500E+2,4.651500E+2,4.661500E+2,4.671500E+2,4.681500E+2,4.691500E+2,4.701500E+2,4.711500E+2,4.721500E+2,4.731500E+2,4.741500E+2,4.751500E+2,4.761500E+2,4.771500E+2,4.781500E+2,4.791500E+2,4.801500E+2,4.811500E+2,4.821500E+2,4.831500E+2,4.841500E+2,4.851500E+2,4.861500E+2,4.871500E+2,4.881500E+2,4.891500E+2,4.901500E+2,4.911500E+2,4.921500E+2,4.931500E+2,4.941500E+2,4.951500E+2,4.961500E+2,4.971500E+2,4.981500E+2,4.991500E+2,5.001500E+2,5.011500E+2,5.021500E+2,5.031500E+2,5.041500E+2,5.051500E+2,5.061500E+2,5.071500E+2,5.081500E+2,5.091500E+2,5.101500E+2,5.111500E+2,5.121500E+2,5.131500E+2,5.141500E+2,5.151500E+2,5.161500E+2,5.171500E+2,5.181500E+2,5.191500E+2,5.201500E+2,5.211500E+2,5.221500E+2,5.231500E+2,5.241500E+2,5.251500E+2,5.261500E+2,5.271500E+2,5.281500E+2,5.291500E+2,5.301500E+2,5.311500E+2,5.321500E+2,5.331500E+2,5.341500E+2,5.351500E+2,5.361500E+2,5.371500E+2,5.381500E+2,5.391500E+2,5.401500E+2,5.411500E+2,5.421500E+2,5.431500E+2,5.441500E+2,5.451500E+2,5.461500E+2,5.471500E+2,5.481500E+2,5.491500E+2,5.501500E+2,5.511500E+2,5.521500E+2,5.531500E+2,5.541500E+2,5.551500E+2,5.561500E+2,5.571500E+2,5.581500E+2,5.591500E+2,5.601500E+2,5.611500E+2,5.621500E+2,5.631500E+2,5.641500E+2,5.651500E+2,5.661500E+2,5.671500E+2,5.681500E+2,5.691500E+2,5.701500E+2,5.711500E+2,5.721500E+2,5.731500E+2,5.741500E+2,5.751500E+2,5.761500E+2,5.771500E+2,5.781500E+2,5.791500E+2,5.801500E+2,5.811500E+2,5.821500E+2,5.831500E+2,5.841500E+2,5.851500E+2,5.861500E+2,5.871500E+2,5.881500E+2])
+        self.density.data             = np.array([8.680000E+2,8.670000E+2,8.650000E+2,8.640000E+2,8.630000E+2,8.620000E+2,8.600000E+2,8.590000E+2,8.580000E+2,8.570000E+2,8.550000E+2,8.540000E+2,8.530000E+2,8.520000E+2,8.510000E+2,8.490000E+2,8.480000E+2,8.470000E+2,8.460000E+2,8.450000E+2,8.430000E+2,8.420000E+2,8.410000E+2,8.400000E+2,8.390000E+2,8.380000E+2,8.360000E+2,8.350000E+2,8.340000E+2,8.330000E+2,8.320000E+2,8.310000E+2,8.300000E+2,8.280000E+2,8.270000E+2,8.260000E+2,8.250000E+2,8.240000E+2,8.230000E+2,8.220000E+2,8.210000E+2,8.200000E+2,8.180000E+2,8.170000E+2,8.160000E+2,8.150000E+2,8.140000E+2,8.130000E+2,8.120000E+2,8.110000E+2,8.100000E+2,8.090000E+2,8.080000E+2,8.070000E+2,8.060000E+2,8.050000E+2,8.040000E+2,8.020000E+2,8.010000E+2,8.000000E+2,7.990000E+2,7.980000E+2,7.970000E+2,7.960000E+2,7.950000E+2,7.940000E+2,7.930000E+2,7.920000E+2,7.910000E+2,7.900000E+2,7.890000E+2,7.880000E+2,7.870000E+2,7.860000E+2,7.860000E+2,7.850000E+2,7.840000E+2,7.830000E+2,7.820000E+2,7.810000E+2,7.800000E+2,7.790000E+2,7.780000E+2,7.770000E+2,7.760000E+2,7.750000E+2,7.740000E+2,7.730000E+2,7.720000E+2,7.720000E+2,7.710000E+2,7.700000E+2,7.690000E+2,7.680000E+2,7.670000E+2,7.660000E+2,7.650000E+2,7.640000E+2,7.640000E+2,7.630000E+2,7.620000E+2,7.610000E+2,7.600000E+2,7.590000E+2,7.580000E+2,7.580000E+2,7.570000E+2,7.560000E+2,7.550000E+2,7.540000E+2,7.530000E+2,7.530000E+2,7.520000E+2,7.510000E+2,7.500000E+2,7.490000E+2,7.490000E+2,7.480000E+2,7.470000E+2,7.460000E+2,7.450000E+2,7.450000E+2,7.440000E+2,7.430000E+2,7.420000E+2,7.420000E+2,7.410000E+2,7.400000E+2,7.390000E+2,7.390000E+2,7.380000E+2,7.370000E+2,7.360000E+2,7.360000E+2,7.350000E+2,7.340000E+2,7.330000E+2,7.330000E+2,7.320000E+2,7.310000E+2,7.310000E+2,7.300000E+2,7.290000E+2,7.290000E+2,7.280000E+2,7.270000E+2,7.260000E+2,7.260000E+2,7.250000E+2,7.240000E+2,7.240000E+2,7.230000E+2,7.220000E+2,7.220000E+2,7.210000E+2,7.210000E+2,7.200000E+2,7.190000E+2,7.190000E+2,7.180000E+2,7.170000E+2,7.170000E+2,7.160000E+2,7.160000E+2,7.150000E+2,7.140000E+2,7.140000E+2,7.130000E+2,7.130000E+2,7.120000E+2,7.110000E+2,7.110000E+2,7.100000E+2,7.100000E+2,7.090000E+2,7.090000E+2,7.080000E+2,7.070000E+2,7.070000E+2,7.060000E+2,7.060000E+2,7.050000E+2,7.050000E+2,7.040000E+2,7.040000E+2,7.030000E+2,7.030000E+2,7.020000E+2,7.020000E+2,7.010000E+2,7.010000E+2,7.000000E+2,7.000000E+2,6.990000E+2,6.990000E+2,6.980000E+2,6.980000E+2,6.970000E+2,6.970000E+2,6.960000E+2,6.960000E+2,6.950000E+2,6.950000E+2,6.940000E+2,6.940000E+2,6.940000E+2,6.930000E+2,6.930000E+2,6.920000E+2,6.920000E+2,6.910000E+2,6.910000E+2,6.910000E+2,6.900000E+2,6.900000E+2,6.890000E+2,6.890000E+2,6.890000E+2,6.880000E+2,6.880000E+2,6.870000E+2,6.870000E+2,6.870000E+2,6.860000E+2,6.860000E+2,6.860000E+2,6.850000E+2,6.850000E+2,6.840000E+2,6.840000E+2,6.840000E+2,6.830000E+2,6.830000E+2,6.830000E+2,6.820000E+2,6.820000E+2,6.820000E+2,6.820000E+2,6.810000E+2,6.810000E+2,6.810000E+2,6.800000E+2,6.800000E+2,6.800000E+2,6.790000E+2,6.790000E+2,6.790000E+2,6.790000E+2,6.780000E+2,6.780000E+2,6.780000E+2,6.780000E+2,6.770000E+2,6.770000E+2,6.770000E+2,6.770000E+2,6.760000E+2,6.760000E+2,6.760000E+2,6.760000E+2,6.750000E+2,6.750000E+2,6.750000E+2,6.750000E+2,6.750000E+2,6.740000E+2,6.740000E+2,6.740000E+2,6.740000E+2,6.740000E+2,6.730000E+2,6.730000E+2,6.730000E+2,6.730000E+2,6.730000E+2,6.730000E+2,6.720000E+2,6.720000E+2,6.720000E+2,6.720000E+2,6.720000E+2,6.720000E+2,6.720000E+2,6.710000E+2,6.710000E+2,6.710000E+2,6.710000E+2,6.710000E+2,6.710000E+2,6.710000E+2,6.710000E+2,6.700000E+2,6.700000E+2,6.700000E+2,6.700000E+2,6.700000E+2,6.700000E+2,6.700000E+2,6.700000E+2,6.700000E+2,6.700000E+2,6.700000E+2,6.700000E+2,6.700000E+2,6.700000E+2,6.700000E+2,6.700000E+2,6.690000E+2,6.690000E+2,6.690000E+2,6.690000E+2,6.690000E+2,6.690000E+2,6.690000E+2,6.690000E+2,6.690000E+2,6.690000E+2,6.690000E+2,6.690000E+2,6.690000E+2,6.690000E+2,6.690000E+2,6.690000E+2,6.690000E+2,6.690000E+2,6.690000E+2,6.690000E+2,6.690000E+2,6.680000E+2,6.680000E+2,6.680000E+2,6.680000E+2,6.680000E+2,6.680000E+2,6.680000E+2,6.680000E+2,6.680000E+2,6.680000E+2,6.680000E+2,6.680000E+2,6.680000E+2,6.680000E+2,6.680000E+2,6.680000E+2,6.680000E+2,6.680000E+2,6.680000E+2,6.680000E+2,6.670000E+2,6.670000E+2,6.670000E+2,6.670000E+2,6.670000E+2,6.670000E+2,6.670000E+2,6.670000E+2])
+        self.viscosity.data           = np.array([3.860000E-4,3.150000E-4,2.730000E-4,2.440000E-4,2.210000E-4,2.030000E-4,1.870000E-4,1.730000E-4,1.610000E-4,1.500000E-4,1.410000E-4,1.320000E-4,1.240000E-4,1.160000E-4,1.090000E-4,1.020000E-4,9.600000E-5,9.000000E-5,8.500000E-5,8.000000E-5,7.500000E-5,7.000000E-5,6.500000E-5,6.100000E-5,5.700000E-5,5.300000E-5,4.900000E-5,4.500000E-5,4.200000E-5,3.800000E-5,3.500000E-5,3.200000E-5,2.800000E-5,2.500000E-5,2.800000E-5,2.700000E-5,2.600000E-5,2.500000E-5,2.400000E-5,2.300000E-5,2.200000E-5,2.100000E-5,2.000000E-5,1.900000E-5,1.900000E-5,1.800000E-5,1.700000E-5,1.600000E-5,1.600000E-5,1.500000E-5,1.400000E-5,1.400000E-5,1.300000E-5,1.200000E-5,1.200000E-5,1.100000E-5,1.100000E-5,1.000000E-5,1.000000E-5,9.600000E-6,9.200000E-6,8.900000E-6,8.500000E-6,8.200000E-6,8.000000E-6,7.700000E-6,7.500000E-6,7.300000E-6,7.100000E-6,7.000000E-6,6.800000E-6,6.600000E-6,6.400000E-6,6.200000E-6,6.000000E-6,5.800000E-6,5.700000E-6,5.500000E-6,5.300000E-6,5.200000E-6,5.100000E-6,4.900000E-6,4.800000E-6,4.700000E-6,4.600000E-6,4.500000E-6,4.400000E-6,4.300000E-6,4.200000E-6,4.100000E-6,4.000000E-6,3.900000E-6,3.800000E-6,3.800000E-6,3.700000E-6,3.600000E-6,3.600000E-6,3.500000E-6,3.400000E-6,3.400000E-6,3.300000E-6,3.100000E-6,3.000000E-6,3.000000E-6,2.900000E-6,2.800000E-6,2.800000E-6,2.700000E-6,2.600000E-6,2.600000E-6,2.500000E-6,2.500000E-6,2.400000E-6,2.400000E-6,2.300000E-6,2.300000E-6,2.200000E-6,2.200000E-6,2.100000E-6,2.100000E-6,2.100000E-6,2.000000E-6,2.000000E-6,2.000000E-6,1.900000E-6,1.900000E-6,1.800000E-6,1.800000E-6,1.800000E-6,1.800000E-6,1.700000E-6,1.700000E-6,1.700000E-6,1.600000E-6,1.600000E-6,1.600000E-6,1.600000E-6,1.500000E-6,1.500000E-6,1.500000E-6,1.500000E-6,1.400000E-6,1.400000E-6,1.400000E-6,1.400000E-6,1.400000E-6,1.300000E-6,1.300000E-6,1.300000E-6,1.300000E-6,1.300000E-6,1.200000E-6,1.200000E-6,1.200000E-6,1.200000E-6,1.200000E-6,1.200000E-6,1.200000E-6,1.100000E-6,1.100000E-6,1.100000E-6,1.100000E-6,1.100000E-6,1.100000E-6,1.100000E-6,1.000000E-6,1.000000E-6,1.000000E-6,1.000000E-6,1.000000E-6,9.800000E-7,9.700000E-7,9.600000E-7,9.500000E-7,9.400000E-7,9.300000E-7,9.200000E-7,9.100000E-7,9.000000E-7,8.900000E-7,8.800000E-7,8.700000E-7,8.600000E-7,8.500000E-7,8.400000E-7,8.300000E-7,8.200000E-7,8.200000E-7,8.100000E-7,8.000000E-7,7.900000E-7,7.800000E-7,7.800000E-7,7.700000E-7,7.600000E-7,7.500000E-7,7.500000E-7,7.400000E-7,7.300000E-7,7.200000E-7,7.200000E-7,7.100000E-7,7.000000E-7,7.000000E-7,6.900000E-7,6.800000E-7,6.800000E-7,6.700000E-7,6.700000E-7,6.600000E-7,6.500000E-7,6.500000E-7,6.400000E-7,6.400000E-7,6.300000E-7,6.300000E-7,6.200000E-7,6.200000E-7,6.100000E-7,6.100000E-7,6.000000E-7,5.900000E-7,5.900000E-7,5.900000E-7,5.800000E-7,5.800000E-7,5.700000E-7,5.700000E-7,5.600000E-7,5.600000E-7,5.500000E-7,5.500000E-7,5.400000E-7,5.400000E-7,5.400000E-7,5.300000E-7,5.300000E-7,5.200000E-7,5.200000E-7,5.200000E-7,5.100000E-7,5.100000E-7,5.000000E-7,5.000000E-7,5.000000E-7,4.900000E-7,4.900000E-7,4.900000E-7,4.800000E-7,4.800000E-7,4.700000E-7,4.700000E-7,4.700000E-7,4.600000E-7,4.600000E-7,4.600000E-7,4.500000E-7,4.500000E-7,4.500000E-7,4.500000E-7,4.400000E-7,4.400000E-7,4.400000E-7,4.300000E-7,4.300000E-7,4.300000E-7,4.200000E-7,4.200000E-7,4.200000E-7,4.200000E-7,4.100000E-7,4.100000E-7,4.100000E-7,4.100000E-7,4.000000E-7,4.000000E-7,4.000000E-7,3.900000E-7,3.900000E-7,3.900000E-7,3.900000E-7,3.800000E-7,3.800000E-7,3.800000E-7,3.800000E-7,3.800000E-7,3.700000E-7,3.700000E-7,3.700000E-7,3.700000E-7,3.600000E-7,3.600000E-7,3.600000E-7,3.600000E-7,3.600000E-7,3.500000E-7,3.500000E-7,3.500000E-7,3.500000E-7,3.400000E-7,3.400000E-7,3.400000E-7,3.400000E-7,3.400000E-7,3.400000E-7,3.300000E-7,3.300000E-7,3.300000E-7,3.300000E-7,3.300000E-7,3.200000E-7,3.200000E-7,3.200000E-7,3.200000E-7,3.200000E-7,3.100000E-7,3.100000E-7,3.100000E-7,3.100000E-7,3.100000E-7,3.100000E-7,3.000000E-7,3.000000E-7,3.000000E-7,3.000000E-7,3.000000E-7,3.000000E-7,3.000000E-7,2.900000E-7,2.900000E-7,2.900000E-7,2.900000E-7,2.900000E-7,2.900000E-7,2.800000E-7,2.800000E-7,2.800000E-7,2.800000E-7,2.800000E-7,2.800000E-7,2.800000E-7,2.700000E-7,2.700000E-7,2.700000E-7,2.700000E-7,2.700000E-7,2.700000E-7,2.700000E-7,2.700000E-7,2.600000E-7,2.600000E-7,2.600000E-7,2.600000E-7,2.600000E-7,2.600000E-7,2.600000E-7])
+        self.specific_heat.data       = np.array([2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.900000E+3,2.900000E+3,2.900000E+3,2.900000E+3,2.900000E+3,2.900000E+3,2.900000E+3,2.900000E+3,2.900000E+3,2.900000E+3,2.900000E+3,2.900000E+3,2.900000E+3,2.900000E+3,2.900000E+3,2.900000E+3,2.900000E+3,2.900000E+3,2.900000E+3,2.900000E+3,2.900000E+3,2.900000E+3,2.900000E+3,2.900000E+3,2.900000E+3,2.900000E+3,2.900000E+3,2.900000E+3,2.900000E+3,2.900000E+3,2.900000E+3,2.900000E+3,2.900000E+3,2.900000E+3,2.900000E+3,2.900000E+3,2.900000E+3,2.900000E+3,2.900000E+3,2.900000E+3,2.900000E+3,2.900000E+3,2.900000E+3,3.000000E+3,3.000000E+3,3.000000E+3,3.000000E+3,3.000000E+3,3.000000E+3,3.000000E+3,3.000000E+3,3.000000E+3,3.000000E+3,3.000000E+3,3.000000E+3,3.000000E+3,3.000000E+3,3.000000E+3,3.000000E+3,3.000000E+3,3.000000E+3,3.000000E+3,3.000000E+3])
+        self.conductivity.data        = np.array([1.330000E-1,1.330000E-1,1.330000E-1,1.330000E-1,1.330000E-1,1.330000E-1,1.330000E-1,1.330000E-1,1.330000E-1,1.340000E-1,1.340000E-1,1.340000E-1,1.340000E-1,1.340000E-1,1.340000E-1,1.340000E-1,1.340000E-1,1.340000E-1,1.340000E-1,1.340000E-1,1.340000E-1,1.340000E-1,1.340000E-1,1.340000E-1,1.340000E-1,1.340000E-1,1.340000E-1,1.340000E-1,1.340000E-1,1.340000E-1,1.340000E-1,1.340000E-1,1.340000E-1,1.340000E-1,1.340000E-1,1.340000E-1,1.340000E-1,1.340000E-1,1.340000E-1,1.340000E-1,1.340000E-1,1.340000E-1,1.340000E-1,1.340000E-1,1.340000E-1,1.340000E-1,1.340000E-1,1.340000E-1,1.340000E-1,1.340000E-1,1.340000E-1,1.340000E-1,1.340000E-1,1.340000E-1,1.340000E-1,1.340000E-1,1.340000E-1,1.340000E-1,1.340000E-1,1.340000E-1,1.340000E-1,1.340000E-1,1.330000E-1,1.330000E-1,1.330000E-1,1.330000E-1,1.330000E-1,1.330000E-1,1.330000E-1,1.330000E-1,1.330000E-1,1.330000E-1,1.330000E-1,1.330000E-1,1.330000E-1,1.330000E-1,1.330000E-1,1.330000E-1,1.330000E-1,1.330000E-1,1.330000E-1,1.330000E-1,1.330000E-1,1.330000E-1,1.330000E-1,1.330000E-1,1.330000E-1,1.330000E-1,1.330000E-1,1.330000E-1,1.330000E-1,1.330000E-1,1.330000E-1,1.330000E-1,1.330000E-1,1.330000E-1,1.330000E-1,1.330000E-1,1.330000E-1,1.320000E-1,1.320000E-1,1.320000E-1,1.320000E-1,1.320000E-1,1.320000E-1,1.320000E-1,1.320000E-1,1.320000E-1,1.320000E-1,1.320000E-1,1.320000E-1,1.320000E-1,1.320000E-1,1.320000E-1,1.320000E-1,1.320000E-1,1.320000E-1,1.320000E-1,1.320000E-1,1.320000E-1,1.320000E-1,1.310000E-1,1.310000E-1,1.310000E-1,1.310000E-1,1.310000E-1,1.310000E-1,1.310000E-1,1.310000E-1,1.310000E-1,1.310000E-1,1.310000E-1,1.310000E-1,1.310000E-1,1.310000E-1,1.310000E-1,1.310000E-1,1.310000E-1,1.310000E-1,1.300000E-1,1.300000E-1,1.300000E-1,1.300000E-1,1.300000E-1,1.300000E-1,1.300000E-1,1.300000E-1,1.300000E-1,1.300000E-1,1.300000E-1,1.300000E-1,1.300000E-1,1.300000E-1,1.300000E-1,1.290000E-1,1.290000E-1,1.290000E-1,1.290000E-1,1.290000E-1,1.290000E-1,1.290000E-1,1.290000E-1,1.290000E-1,1.290000E-1,1.290000E-1,1.290000E-1,1.290000E-1,1.280000E-1,1.280000E-1,1.280000E-1,1.280000E-1,1.280000E-1,1.280000E-1,1.280000E-1,1.280000E-1,1.280000E-1,1.280000E-1,1.280000E-1,1.280000E-1,1.270000E-1,1.270000E-1,1.270000E-1,1.270000E-1,1.270000E-1,1.270000E-1,1.270000E-1,1.270000E-1,1.270000E-1,1.270000E-1,1.270000E-1,1.260000E-1,1.260000E-1,1.260000E-1,1.260000E-1,1.260000E-1,1.260000E-1,1.260000E-1,1.260000E-1,1.260000E-1,1.260000E-1,1.260000E-1,1.250000E-1,1.250000E-1,1.250000E-1,1.250000E-1,1.250000E-1,1.250000E-1,1.250000E-1,1.250000E-1,1.250000E-1,1.250000E-1,1.240000E-1,1.240000E-1,1.240000E-1,1.240000E-1,1.240000E-1,1.240000E-1,1.240000E-1,1.240000E-1,1.240000E-1,1.230000E-1,1.230000E-1,1.230000E-1,1.230000E-1,1.230000E-1,1.230000E-1,1.230000E-1,1.230000E-1,1.230000E-1,1.220000E-1,1.220000E-1,1.220000E-1,1.220000E-1,1.220000E-1,1.220000E-1,1.220000E-1,1.220000E-1,1.210000E-1,1.210000E-1,1.210000E-1,1.210000E-1,1.210000E-1,1.210000E-1,1.210000E-1,1.210000E-1,1.200000E-1,1.200000E-1,1.200000E-1,1.200000E-1,1.200000E-1,1.200000E-1,1.200000E-1,1.200000E-1,1.190000E-1,1.190000E-1,1.190000E-1,1.190000E-1,1.190000E-1,1.190000E-1,1.190000E-1,1.190000E-1,1.180000E-1,1.180000E-1,1.180000E-1,1.180000E-1,1.180000E-1,1.180000E-1,1.180000E-1,1.170000E-1,1.170000E-1,1.170000E-1,1.170000E-1,1.170000E-1,1.170000E-1,1.170000E-1,1.160000E-1,1.160000E-1,1.160000E-1,1.160000E-1,1.160000E-1,1.160000E-1,1.160000E-1,1.150000E-1,1.150000E-1,1.150000E-1,1.150000E-1,1.150000E-1,1.150000E-1,1.150000E-1,1.140000E-1,1.140000E-1,1.140000E-1,1.140000E-1,1.140000E-1,1.140000E-1,1.130000E-1,1.130000E-1,1.130000E-1,1.130000E-1,1.130000E-1,1.130000E-1,1.120000E-1,1.120000E-1,1.120000E-1,1.120000E-1,1.120000E-1,1.120000E-1,1.120000E-1,1.110000E-1,1.110000E-1,1.110000E-1,1.110000E-1,1.110000E-1,1.110000E-1,1.100000E-1,1.100000E-1,1.100000E-1,1.100000E-1,1.100000E-1,1.100000E-1,1.090000E-1,1.090000E-1,1.090000E-1,1.090000E-1,1.090000E-1,1.080000E-1,1.080000E-1,1.080000E-1,1.080000E-1,1.080000E-1,1.080000E-1,1.070000E-1,1.070000E-1,1.070000E-1,1.070000E-1,1.070000E-1,1.070000E-1,1.060000E-1,1.060000E-1,1.060000E-1,1.060000E-1,1.060000E-1,1.050000E-1,1.050000E-1,1.050000E-1,1.050000E-1,1.050000E-1,1.050000E-1,1.040000E-1,1.040000E-1,1.040000E-1,1.040000E-1,1.040000E-1,1.030000E-1,1.030000E-1,1.030000E-1])
+        self.saturation_pressure.data = np.array([     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,3.000000E+3,3.000000E+3,3.000000E+3,3.000000E+3,3.000000E+3,3.000000E+3,3.000000E+3,3.000000E+3,3.000000E+3,4.000000E+3,4.000000E+3,4.000000E+3,4.000000E+3,4.000000E+3,4.000000E+3,4.000000E+3,4.000000E+3,5.000000E+3,5.000000E+3,5.000000E+3,5.000000E+3,5.000000E+3,5.000000E+3,6.000000E+3,6.000000E+3,6.000000E+3,6.000000E+3,6.000000E+3,7.000000E+3,7.000000E+3,7.000000E+3,7.000000E+3,7.000000E+3,8.000000E+3,8.000000E+3,8.000000E+3,8.000000E+3,9.000000E+3,9.000000E+3,9.000000E+3,9.000000E+3,1.000000E+4,1.000000E+4,1.000000E+4,1.100000E+4,1.100000E+4,1.100000E+4,1.200000E+4,1.200000E+4,1.200000E+4,1.300000E+4,1.300000E+4,1.300000E+4,1.400000E+4,1.400000E+4,1.500000E+4,1.500000E+4,1.500000E+4,1.600000E+4,1.600000E+4,1.700000E+4,1.700000E+4,1.800000E+4,1.800000E+4,1.900000E+4,1.900000E+4,2.000000E+4,2.000000E+4,2.100000E+4,2.100000E+4,2.200000E+4,2.200000E+4,2.300000E+4,2.400000E+4,2.400000E+4,2.500000E+4,2.600000E+4,2.600000E+4,2.700000E+4,2.800000E+4,2.800000E+4,2.900000E+4,3.000000E+4,3.000000E+4,3.100000E+4,3.200000E+4,3.300000E+4,3.400000E+4,3.500000E+4,3.500000E+4,3.600000E+4,3.700000E+4,3.800000E+4,3.900000E+4,4.000000E+4,4.100000E+4,4.200000E+4,4.300000E+4,4.400000E+4,4.500000E+4,4.600000E+4,4.700000E+4,4.800000E+4,4.900000E+4,5.100000E+4,5.200000E+4,5.300000E+4,5.400000E+4,5.500000E+4,5.700000E+4,5.800000E+4,5.900000E+4,6.100000E+4,6.200000E+4,6.400000E+4,6.500000E+4,6.700000E+4,6.800000E+4,7.000000E+4,7.100000E+4,7.300000E+4,7.400000E+4,7.600000E+4,7.800000E+4])
+        self.Tmin        = np.min(self.temperature.data)
+        self.Tmax        = np.max(self.temperature.data)
+        self.TminPsat    = np.min(self.temperature.data[~np.isnan(self.saturation_pressure.data)])
+        self.name        = "PMR"
+        self.description = "Paratherm "+ self.name[1:]
+        self.reference   = "Paratherm2013"
+        self.reshapeAll()
+
+
+
+
+class PNF(PureData):
+    """
+    The Paratherm NF heat transfer fluid is highly efficient, thermally stable
+    and cost-effective. Completely non-toxic, it is exceptionally safe to use
+    and is easy to dispose. Used fluid can be safely combined with spent
+    lubricating oils and recycled locally (EPA, citation 57FR21524). The NF
+    fluid is specified in a broad variety of applications, world wide. It is
+    tough and durable with a proven record of success under demanding
+    conditions, yet is easy and safe to handle.
+    """
+    def __init__(self):
+        PureData.__init__(self)
+        self.density.source           = self.density.SOURCE_DATA
+        self.viscosity.source         = self.viscosity.SOURCE_DATA
+        self.specific_heat.source     = self.specific_heat.SOURCE_DATA
+        self.conductivity.source      = self.conductivity.SOURCE_DATA
+        self.saturation_pressure.source = self.saturation_pressure.SOURCE_DATA
+        self.temperature.data         = np.array([2.631500E+2,2.641500E+2,2.651500E+2,2.661500E+2,2.671500E+2,2.681500E+2,2.691500E+2,2.701500E+2,2.711500E+2,2.721500E+2,2.731500E+2,2.741500E+2,2.751500E+2,2.761500E+2,2.771500E+2,2.781500E+2,2.791500E+2,2.801500E+2,2.811500E+2,2.821500E+2,2.831500E+2,2.841500E+2,2.851500E+2,2.861500E+2,2.871500E+2,2.881500E+2,2.891500E+2,2.901500E+2,2.911500E+2,2.921500E+2,2.931500E+2,2.941500E+2,2.951500E+2,2.961500E+2,2.971500E+2,2.981500E+2,2.991500E+2,3.001500E+2,3.011500E+2,3.021500E+2,3.031500E+2,3.041500E+2,3.051500E+2,3.061500E+2,3.071500E+2,3.081500E+2,3.091500E+2,3.101500E+2,3.111500E+2,3.121500E+2,3.131500E+2,3.141500E+2,3.151500E+2,3.161500E+2,3.171500E+2,3.181500E+2,3.191500E+2,3.201500E+2,3.211500E+2,3.221500E+2,3.231500E+2,3.241500E+2,3.251500E+2,3.261500E+2,3.271500E+2,3.281500E+2,3.291500E+2,3.301500E+2,3.311500E+2,3.321500E+2,3.331500E+2,3.341500E+2,3.351500E+2,3.361500E+2,3.371500E+2,3.381500E+2,3.391500E+2,3.401500E+2,3.411500E+2,3.421500E+2,3.431500E+2,3.441500E+2,3.451500E+2,3.461500E+2,3.471500E+2,3.481500E+2,3.491500E+2,3.501500E+2,3.511500E+2,3.521500E+2,3.531500E+2,3.541500E+2,3.551500E+2,3.561500E+2,3.571500E+2,3.581500E+2,3.591500E+2,3.601500E+2,3.611500E+2,3.621500E+2,3.631500E+2,3.641500E+2,3.651500E+2,3.661500E+2,3.671500E+2,3.681500E+2,3.691500E+2,3.701500E+2,3.711500E+2,3.721500E+2,3.731500E+2,3.741500E+2,3.751500E+2,3.761500E+2,3.771500E+2,3.781500E+2,3.791500E+2,3.801500E+2,3.811500E+2,3.821500E+2,3.831500E+2,3.841500E+2,3.851500E+2,3.861500E+2,3.871500E+2,3.881500E+2,3.891500E+2,3.901500E+2,3.911500E+2,3.921500E+2,3.931500E+2,3.941500E+2,3.951500E+2,3.961500E+2,3.971500E+2,3.981500E+2,3.991500E+2,4.001500E+2,4.011500E+2,4.021500E+2,4.031500E+2,4.041500E+2,4.051500E+2,4.061500E+2,4.071500E+2,4.081500E+2,4.091500E+2,4.101500E+2,4.111500E+2,4.121500E+2,4.131500E+2,4.141500E+2,4.151500E+2,4.161500E+2,4.171500E+2,4.181500E+2,4.191500E+2,4.201500E+2,4.211500E+2,4.221500E+2,4.231500E+2,4.241500E+2,4.251500E+2,4.261500E+2,4.271500E+2,4.281500E+2,4.291500E+2,4.301500E+2,4.311500E+2,4.321500E+2,4.331500E+2,4.341500E+2,4.351500E+2,4.361500E+2,4.371500E+2,4.381500E+2,4.391500E+2,4.401500E+2,4.411500E+2,4.421500E+2,4.431500E+2,4.441500E+2,4.451500E+2,4.461500E+2,4.471500E+2,4.481500E+2,4.491500E+2,4.501500E+2,4.511500E+2,4.521500E+2,4.531500E+2,4.541500E+2,4.551500E+2,4.561500E+2,4.571500E+2,4.581500E+2,4.591500E+2,4.601500E+2,4.611500E+2,4.621500E+2,4.631500E+2,4.641500E+2,4.651500E+2,4.661500E+2,4.671500E+2,4.681500E+2,4.691500E+2,4.701500E+2,4.711500E+2,4.721500E+2,4.731500E+2,4.741500E+2,4.751500E+2,4.761500E+2,4.771500E+2,4.781500E+2,4.791500E+2,4.801500E+2,4.811500E+2,4.821500E+2,4.831500E+2,4.841500E+2,4.851500E+2,4.861500E+2,4.871500E+2,4.881500E+2,4.891500E+2,4.901500E+2,4.911500E+2,4.921500E+2,4.931500E+2,4.941500E+2,4.951500E+2,4.961500E+2,4.971500E+2,4.981500E+2,4.991500E+2,5.001500E+2,5.011500E+2,5.021500E+2,5.031500E+2,5.041500E+2,5.051500E+2,5.061500E+2,5.071500E+2,5.081500E+2,5.091500E+2,5.101500E+2,5.111500E+2,5.121500E+2,5.131500E+2,5.141500E+2,5.151500E+2,5.161500E+2,5.171500E+2,5.181500E+2,5.191500E+2,5.201500E+2,5.211500E+2,5.221500E+2,5.231500E+2,5.241500E+2,5.251500E+2,5.261500E+2,5.271500E+2,5.281500E+2,5.291500E+2,5.301500E+2,5.311500E+2,5.321500E+2,5.331500E+2,5.341500E+2,5.351500E+2,5.361500E+2,5.371500E+2,5.381500E+2,5.391500E+2,5.401500E+2,5.411500E+2,5.421500E+2,5.431500E+2,5.441500E+2,5.451500E+2,5.461500E+2,5.471500E+2,5.481500E+2,5.491500E+2,5.501500E+2,5.511500E+2,5.521500E+2,5.531500E+2,5.541500E+2,5.551500E+2,5.561500E+2,5.571500E+2,5.581500E+2,5.591500E+2,5.601500E+2,5.611500E+2,5.621500E+2,5.631500E+2,5.641500E+2,5.651500E+2,5.661500E+2,5.671500E+2,5.681500E+2,5.691500E+2,5.701500E+2,5.711500E+2,5.721500E+2,5.731500E+2,5.741500E+2,5.751500E+2,5.761500E+2,5.771500E+2,5.781500E+2,5.791500E+2,5.801500E+2,5.811500E+2,5.821500E+2,5.831500E+2,5.841500E+2,5.851500E+2,5.861500E+2,5.871500E+2,5.881500E+2])
+        self.density.data             = np.array([9.040000E+2,9.030000E+2,9.030000E+2,9.020000E+2,9.010000E+2,9.010000E+2,9.000000E+2,8.990000E+2,8.990000E+2,8.980000E+2,8.970000E+2,8.970000E+2,8.960000E+2,8.950000E+2,8.950000E+2,8.940000E+2,8.930000E+2,8.930000E+2,8.920000E+2,8.910000E+2,8.910000E+2,8.900000E+2,8.890000E+2,8.890000E+2,8.880000E+2,8.870000E+2,8.870000E+2,8.860000E+2,8.850000E+2,8.850000E+2,8.840000E+2,8.830000E+2,8.830000E+2,8.820000E+2,8.810000E+2,8.810000E+2,8.800000E+2,8.790000E+2,8.790000E+2,8.780000E+2,8.780000E+2,8.770000E+2,8.760000E+2,8.760000E+2,8.750000E+2,8.740000E+2,8.740000E+2,8.730000E+2,8.720000E+2,8.720000E+2,8.710000E+2,8.700000E+2,8.700000E+2,8.690000E+2,8.680000E+2,8.680000E+2,8.670000E+2,8.660000E+2,8.660000E+2,8.650000E+2,8.640000E+2,8.640000E+2,8.630000E+2,8.620000E+2,8.620000E+2,8.610000E+2,8.600000E+2,8.600000E+2,8.590000E+2,8.580000E+2,8.580000E+2,8.570000E+2,8.560000E+2,8.560000E+2,8.550000E+2,8.540000E+2,8.540000E+2,8.530000E+2,8.520000E+2,8.520000E+2,8.510000E+2,8.500000E+2,8.500000E+2,8.490000E+2,8.480000E+2,8.480000E+2,8.470000E+2,8.460000E+2,8.460000E+2,8.450000E+2,8.440000E+2,8.440000E+2,8.430000E+2,8.420000E+2,8.420000E+2,8.410000E+2,8.400000E+2,8.400000E+2,8.390000E+2,8.380000E+2,8.380000E+2,8.370000E+2,8.360000E+2,8.360000E+2,8.350000E+2,8.340000E+2,8.340000E+2,8.330000E+2,8.320000E+2,8.320000E+2,8.310000E+2,8.300000E+2,8.300000E+2,8.290000E+2,8.280000E+2,8.280000E+2,8.270000E+2,8.260000E+2,8.260000E+2,8.250000E+2,8.240000E+2,8.240000E+2,8.230000E+2,8.220000E+2,8.220000E+2,8.210000E+2,8.200000E+2,8.200000E+2,8.190000E+2,8.190000E+2,8.180000E+2,8.170000E+2,8.170000E+2,8.160000E+2,8.150000E+2,8.150000E+2,8.140000E+2,8.130000E+2,8.130000E+2,8.120000E+2,8.110000E+2,8.110000E+2,8.100000E+2,8.090000E+2,8.090000E+2,8.080000E+2,8.070000E+2,8.070000E+2,8.060000E+2,8.050000E+2,8.050000E+2,8.040000E+2,8.030000E+2,8.030000E+2,8.020000E+2,8.010000E+2,8.010000E+2,8.000000E+2,7.990000E+2,7.990000E+2,7.980000E+2,7.970000E+2,7.970000E+2,7.960000E+2,7.950000E+2,7.950000E+2,7.940000E+2,7.930000E+2,7.930000E+2,7.920000E+2,7.910000E+2,7.910000E+2,7.900000E+2,7.890000E+2,7.890000E+2,7.880000E+2,7.870000E+2,7.870000E+2,7.860000E+2,7.850000E+2,7.850000E+2,7.840000E+2,7.830000E+2,7.830000E+2,7.820000E+2,7.810000E+2,7.810000E+2,7.800000E+2,7.790000E+2,7.790000E+2,7.780000E+2,7.770000E+2,7.770000E+2,7.760000E+2,7.750000E+2,7.750000E+2,7.740000E+2,7.730000E+2,7.730000E+2,7.720000E+2,7.710000E+2,7.710000E+2,7.700000E+2,7.690000E+2,7.690000E+2,7.680000E+2,7.670000E+2,7.670000E+2,7.660000E+2,7.650000E+2,7.650000E+2,7.640000E+2,7.630000E+2,7.630000E+2,7.620000E+2,7.610000E+2,7.610000E+2,7.600000E+2,7.590000E+2,7.590000E+2,7.580000E+2,7.580000E+2,7.570000E+2,7.560000E+2,7.560000E+2,7.550000E+2,7.540000E+2,7.540000E+2,7.530000E+2,7.520000E+2,7.520000E+2,7.510000E+2,7.500000E+2,7.500000E+2,7.490000E+2,7.480000E+2,7.480000E+2,7.470000E+2,7.460000E+2,7.460000E+2,7.450000E+2,7.440000E+2,7.440000E+2,7.430000E+2,7.420000E+2,7.420000E+2,7.410000E+2,7.400000E+2,7.400000E+2,7.390000E+2,7.380000E+2,7.380000E+2,7.370000E+2,7.360000E+2,7.360000E+2,7.350000E+2,7.340000E+2,7.340000E+2,7.330000E+2,7.320000E+2,7.320000E+2,7.310000E+2,7.300000E+2,7.300000E+2,7.290000E+2,7.280000E+2,7.280000E+2,7.270000E+2,7.260000E+2,7.260000E+2,7.250000E+2,7.240000E+2,7.240000E+2,7.230000E+2,7.220000E+2,7.220000E+2,7.210000E+2,7.200000E+2,7.200000E+2,7.190000E+2,7.180000E+2,7.180000E+2,7.170000E+2,7.160000E+2,7.160000E+2,7.150000E+2,7.140000E+2,7.140000E+2,7.130000E+2,7.120000E+2,7.120000E+2,7.110000E+2,7.100000E+2,7.100000E+2,7.090000E+2,7.080000E+2,7.080000E+2,7.070000E+2,7.060000E+2,7.060000E+2,7.050000E+2,7.040000E+2,7.040000E+2,7.030000E+2,7.020000E+2,7.020000E+2,7.010000E+2,7.000000E+2,7.000000E+2,6.990000E+2,6.980000E+2,6.980000E+2,6.970000E+2,6.970000E+2,6.960000E+2,6.950000E+2,6.950000E+2,6.940000E+2,6.930000E+2,6.930000E+2,6.920000E+2,6.910000E+2,6.910000E+2,6.900000E+2,6.890000E+2,6.890000E+2])
+        self.viscosity.data           = np.array([4.760000E-4,4.380000E-4,4.040000E-4,3.720000E-4,3.430000E-4,3.160000E-4,2.910000E-4,2.680000E-4,2.470000E-4,2.280000E-4,2.100000E-4,1.940000E-4,1.780000E-4,1.640000E-4,1.510000E-4,1.400000E-4,1.290000E-4,1.180000E-4,1.090000E-4,1.010000E-4,9.300000E-5,8.600000E-5,8.000000E-5,7.400000E-5,6.900000E-5,6.400000E-5,5.900000E-5,5.500000E-5,5.100000E-5,4.800000E-5,4.500000E-5,4.200000E-5,3.900000E-5,3.700000E-5,3.400000E-5,3.200000E-5,3.100000E-5,2.900000E-5,2.700000E-5,2.600000E-5,2.500000E-5,2.400000E-5,2.200000E-5,2.100000E-5,2.100000E-5,2.000000E-5,1.900000E-5,1.800000E-5,1.800000E-5,1.700000E-5,1.600000E-5,1.500000E-5,1.500000E-5,1.400000E-5,1.400000E-5,1.300000E-5,1.300000E-5,1.200000E-5,1.200000E-5,1.100000E-5,1.100000E-5,1.000000E-5,1.000000E-5,9.700000E-6,9.400000E-6,9.100000E-6,8.800000E-6,8.500000E-6,8.200000E-6,8.000000E-6,7.800000E-6,7.500000E-6,7.300000E-6,7.100000E-6,6.900000E-6,6.700000E-6,6.500000E-6,6.300000E-6,6.200000E-6,6.000000E-6,5.900000E-6,5.700000E-6,5.600000E-6,5.400000E-6,5.300000E-6,5.200000E-6,5.100000E-6,4.900000E-6,4.800000E-6,4.700000E-6,4.600000E-6,4.500000E-6,4.400000E-6,4.300000E-6,4.200000E-6,4.100000E-6,4.000000E-6,3.900000E-6,3.900000E-6,3.800000E-6,3.700000E-6,3.600000E-6,3.600000E-6,3.500000E-6,3.400000E-6,3.400000E-6,3.300000E-6,3.200000E-6,3.200000E-6,3.100000E-6,3.100000E-6,3.000000E-6,3.000000E-6,2.900000E-6,2.800000E-6,2.800000E-6,2.800000E-6,2.700000E-6,2.700000E-6,2.600000E-6,2.600000E-6,2.500000E-6,2.500000E-6,2.500000E-6,2.400000E-6,2.400000E-6,2.300000E-6,2.300000E-6,2.300000E-6,2.200000E-6,2.200000E-6,2.200000E-6,2.100000E-6,2.100000E-6,2.100000E-6,2.000000E-6,2.000000E-6,2.000000E-6,2.000000E-6,1.900000E-6,1.900000E-6,1.900000E-6,1.800000E-6,1.800000E-6,1.800000E-6,1.800000E-6,1.700000E-6,1.700000E-6,1.700000E-6,1.700000E-6,1.600000E-6,1.600000E-6,1.600000E-6,1.600000E-6,1.600000E-6,1.500000E-6,1.500000E-6,1.500000E-6,1.500000E-6,1.500000E-6,1.500000E-6,1.400000E-6,1.400000E-6,1.400000E-6,1.400000E-6,1.400000E-6,1.400000E-6,1.300000E-6,1.300000E-6,1.300000E-6,1.300000E-6,1.300000E-6,1.300000E-6,1.200000E-6,1.200000E-6,1.200000E-6,1.200000E-6,1.200000E-6,1.200000E-6,1.200000E-6,1.200000E-6,1.100000E-6,1.100000E-6,1.100000E-6,1.100000E-6,1.100000E-6,1.100000E-6,1.100000E-6,1.100000E-6,1.100000E-6,1.000000E-6,1.000000E-6,1.000000E-6,1.000000E-6,1.000000E-6,9.900000E-7,9.800000E-7,9.700000E-7,9.600000E-7,9.600000E-7,9.500000E-7,9.400000E-7,9.300000E-7,9.200000E-7,9.100000E-7,9.000000E-7,8.900000E-7,8.800000E-7,8.600000E-7,8.500000E-7,8.400000E-7,8.300000E-7,8.100000E-7,8.000000E-7,7.900000E-7,7.800000E-7,7.700000E-7,7.500000E-7,7.400000E-7,7.300000E-7,7.200000E-7,7.100000E-7,7.000000E-7,6.900000E-7,6.800000E-7,6.700000E-7,6.600000E-7,6.500000E-7,6.400000E-7,6.300000E-7,6.200000E-7,6.100000E-7,6.000000E-7,5.900000E-7,5.800000E-7,5.700000E-7,5.600000E-7,5.600000E-7,5.500000E-7,5.400000E-7,5.300000E-7,5.200000E-7,5.200000E-7,5.100000E-7,5.000000E-7,4.900000E-7,4.800000E-7,4.800000E-7,4.700000E-7,4.600000E-7,4.600000E-7,4.500000E-7,4.400000E-7,4.400000E-7,4.300000E-7,4.200000E-7,4.200000E-7,4.100000E-7,4.000000E-7,4.000000E-7,3.900000E-7,3.900000E-7,3.800000E-7,3.700000E-7,3.700000E-7,3.600000E-7,3.600000E-7,3.500000E-7,3.500000E-7,3.400000E-7,3.400000E-7,3.300000E-7,3.300000E-7,3.200000E-7,3.200000E-7,3.100000E-7,3.100000E-7,3.000000E-7,3.000000E-7,2.900000E-7,2.900000E-7,2.800000E-7,2.800000E-7,2.800000E-7,2.700000E-7,2.700000E-7,2.600000E-7,2.600000E-7,2.600000E-7,2.500000E-7,2.500000E-7,2.400000E-7,2.400000E-7,2.400000E-7,2.300000E-7,2.300000E-7,2.300000E-7,2.200000E-7,2.200000E-7,2.200000E-7,2.100000E-7,2.100000E-7,2.100000E-7,2.000000E-7,2.000000E-7,2.000000E-7,1.900000E-7,1.900000E-7,1.900000E-7,1.900000E-7,1.800000E-7,1.800000E-7,1.800000E-7,1.800000E-7,1.700000E-7,1.700000E-7,1.700000E-7,1.600000E-7,1.600000E-7,1.600000E-7,1.600000E-7,1.600000E-7,1.500000E-7,1.500000E-7,1.500000E-7,1.500000E-7])
+        self.specific_heat.data       = np.array([1.700000E+3,1.700000E+3,1.700000E+3,1.700000E+3,1.700000E+3,1.700000E+3,1.700000E+3,1.700000E+3,1.700000E+3,1.700000E+3,1.700000E+3,1.700000E+3,1.800000E+3,1.800000E+3,1.800000E+3,1.800000E+3,1.800000E+3,1.800000E+3,1.800000E+3,1.800000E+3,1.800000E+3,1.800000E+3,1.800000E+3,1.800000E+3,1.800000E+3,1.800000E+3,1.800000E+3,1.800000E+3,1.800000E+3,1.800000E+3,1.800000E+3,1.800000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,1.900000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.000000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.100000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.200000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.300000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.400000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.500000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.600000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.700000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.800000E+3,2.900000E+3,2.900000E+3,2.900000E+3,2.900000E+3,2.900000E+3,2.900000E+3,2.900000E+3,2.900000E+3,2.900000E+3,2.900000E+3,2.900000E+3,2.900000E+3,2.900000E+3,2.900000E+3,2.900000E+3,2.900000E+3,2.900000E+3,2.900000E+3,3.000000E+3,3.000000E+3,3.000000E+3,3.000000E+3,3.000000E+3,3.000000E+3,3.000000E+3,3.000000E+3,3.000000E+3,3.000000E+3,3.000000E+3,3.000000E+3,3.000000E+3,3.000000E+3,3.000000E+3,3.000000E+3,3.000000E+3,3.000000E+3,3.000000E+3,3.000000E+3,3.000000E+3,3.100000E+3,3.100000E+3,3.100000E+3,3.100000E+3,3.100000E+3,3.100000E+3,3.100000E+3,3.100000E+3,3.100000E+3,3.100000E+3,3.100000E+3,3.100000E+3,3.100000E+3,3.100000E+3,3.100000E+3,3.100000E+3,3.100000E+3,3.100000E+3,3.200000E+3,3.200000E+3,3.200000E+3,3.200000E+3,3.200000E+3,3.200000E+3,3.200000E+3,3.200000E+3,3.200000E+3,3.200000E+3,3.200000E+3,3.200000E+3,3.200000E+3,3.200000E+3,3.200000E+3,3.200000E+3,3.200000E+3,3.200000E+3,3.200000E+3,3.200000E+3,3.200000E+3,3.300000E+3,3.300000E+3,3.300000E+3,3.300000E+3,3.300000E+3,3.300000E+3,3.300000E+3,3.300000E+3,3.300000E+3,3.300000E+3,3.300000E+3,3.300000E+3,3.300000E+3,3.300000E+3,3.300000E+3,3.300000E+3,3.300000E+3,3.300000E+3,3.300000E+3,3.300000E+3])
+        self.conductivity.data        = np.array([1.070000E-1,1.070000E-1,1.060000E-1,1.060000E-1,1.060000E-1,1.060000E-1,1.060000E-1,1.060000E-1,1.060000E-1,1.060000E-1,1.060000E-1,1.060000E-1,1.060000E-1,1.060000E-1,1.060000E-1,1.060000E-1,1.060000E-1,1.060000E-1,1.060000E-1,1.050000E-1,1.050000E-1,1.050000E-1,1.050000E-1,1.050000E-1,1.050000E-1,1.050000E-1,1.050000E-1,1.050000E-1,1.050000E-1,1.050000E-1,1.050000E-1,1.050000E-1,1.050000E-1,1.050000E-1,1.050000E-1,1.050000E-1,1.040000E-1,1.040000E-1,1.040000E-1,1.040000E-1,1.040000E-1,1.040000E-1,1.040000E-1,1.040000E-1,1.040000E-1,1.040000E-1,1.040000E-1,1.040000E-1,1.040000E-1,1.040000E-1,1.040000E-1,1.040000E-1,1.040000E-1,1.030000E-1,1.030000E-1,1.030000E-1,1.030000E-1,1.030000E-1,1.030000E-1,1.030000E-1,1.030000E-1,1.030000E-1,1.030000E-1,1.030000E-1,1.030000E-1,1.030000E-1,1.030000E-1,1.030000E-1,1.030000E-1,1.030000E-1,1.030000E-1,1.020000E-1,1.020000E-1,1.020000E-1,1.020000E-1,1.020000E-1,1.020000E-1,1.020000E-1,1.020000E-1,1.020000E-1,1.020000E-1,1.020000E-1,1.020000E-1,1.020000E-1,1.020000E-1,1.020000E-1,1.020000E-1,1.020000E-1,1.020000E-1,1.010000E-1,1.010000E-1,1.010000E-1,1.010000E-1,1.010000E-1,1.010000E-1,1.010000E-1,1.010000E-1,1.010000E-1,1.010000E-1,1.010000E-1,1.010000E-1,1.010000E-1,1.010000E-1,1.010000E-1,1.010000E-1,1.010000E-1,1.010000E-1,1.000000E-1,1.000000E-1,1.000000E-1,1.000000E-1,1.000000E-1,1.000000E-1,1.000000E-1,1.000000E-1,1.000000E-1,1.000000E-1,1.000000E-1,1.000000E-1,1.000000E-1,1.000000E-1,1.000000E-1,1.000000E-1,1.000000E-1,1.000000E-1,9.900000E-2,9.900000E-2,9.900000E-2,9.900000E-2,9.900000E-2,9.900000E-2,9.900000E-2,9.900000E-2,9.900000E-2,9.900000E-2,9.900000E-2,9.900000E-2,9.900000E-2,9.900000E-2,9.900000E-2,9.900000E-2,9.900000E-2,9.900000E-2,9.900000E-2,9.800000E-2,9.800000E-2,9.800000E-2,9.800000E-2,9.800000E-2,9.800000E-2,9.800000E-2,9.800000E-2,9.800000E-2,9.800000E-2,9.800000E-2,9.800000E-2,9.800000E-2,9.800000E-2,9.800000E-2,9.800000E-2,9.800000E-2,9.800000E-2,9.800000E-2,9.800000E-2,9.700000E-2,9.700000E-2,9.700000E-2,9.700000E-2,9.700000E-2,9.700000E-2,9.700000E-2,9.700000E-2,9.700000E-2,9.700000E-2,9.700000E-2,9.700000E-2,9.700000E-2,9.700000E-2,9.700000E-2,9.700000E-2,9.700000E-2,9.700000E-2,9.700000E-2,9.700000E-2,9.600000E-2,9.600000E-2,9.600000E-2,9.600000E-2,9.600000E-2,9.600000E-2,9.600000E-2,9.600000E-2,9.600000E-2,9.600000E-2,9.600000E-2,9.600000E-2,9.600000E-2,9.600000E-2,9.600000E-2,9.600000E-2,9.600000E-2,9.600000E-2,9.600000E-2,9.600000E-2,9.500000E-2,9.500000E-2,9.500000E-2,9.500000E-2,9.500000E-2,9.500000E-2,9.500000E-2,9.500000E-2,9.500000E-2,9.500000E-2,9.500000E-2,9.500000E-2,9.500000E-2,9.500000E-2,9.500000E-2,9.500000E-2,9.500000E-2,9.500000E-2,9.500000E-2,9.500000E-2,9.500000E-2,9.400000E-2,9.400000E-2,9.400000E-2,9.400000E-2,9.400000E-2,9.400000E-2,9.400000E-2,9.400000E-2,9.400000E-2,9.400000E-2,9.400000E-2,9.400000E-2,9.400000E-2,9.400000E-2,9.400000E-2,9.400000E-2,9.400000E-2,9.400000E-2,9.400000E-2,9.400000E-2,9.400000E-2,9.400000E-2,9.300000E-2,9.300000E-2,9.300000E-2,9.300000E-2,9.300000E-2,9.300000E-2,9.300000E-2,9.300000E-2,9.300000E-2,9.300000E-2,9.300000E-2,9.300000E-2,9.300000E-2,9.300000E-2,9.300000E-2,9.300000E-2,9.300000E-2,9.300000E-2,9.300000E-2,9.300000E-2,9.300000E-2,9.300000E-2,9.200000E-2,9.200000E-2,9.200000E-2,9.200000E-2,9.200000E-2,9.200000E-2,9.200000E-2,9.200000E-2,9.200000E-2,9.200000E-2,9.200000E-2,9.200000E-2,9.200000E-2,9.200000E-2,9.200000E-2,9.200000E-2,9.200000E-2,9.200000E-2,9.200000E-2,9.200000E-2,9.200000E-2,9.200000E-2,9.200000E-2,9.100000E-2,9.100000E-2,9.100000E-2,9.100000E-2,9.100000E-2,9.100000E-2,9.100000E-2,9.100000E-2,9.100000E-2,9.100000E-2,9.100000E-2,9.100000E-2,9.100000E-2,9.100000E-2,9.100000E-2,9.100000E-2,9.100000E-2,9.100000E-2,9.100000E-2,9.100000E-2,9.100000E-2,9.100000E-2,9.100000E-2,9.000000E-2,9.000000E-2,9.000000E-2,9.000000E-2,9.000000E-2,9.000000E-2,9.000000E-2,9.000000E-2,9.000000E-2,9.000000E-2,9.000000E-2])
+        self.saturation_pressure.data = np.array([     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,     np.NAN,3.000000E+3,3.000000E+3,3.000000E+3,3.000000E+3,3.000000E+3,3.000000E+3,3.000000E+3,3.000000E+3,3.000000E+3,3.000000E+3,3.000000E+3,3.000000E+3,3.000000E+3,3.000000E+3,3.000000E+3,3.000000E+3,3.000000E+3,3.000000E+3,3.000000E+3,4.000000E+3,4.000000E+3,4.000000E+3,4.000000E+3,4.000000E+3,4.000000E+3,4.000000E+3,4.000000E+3,4.000000E+3,4.000000E+3,4.000000E+3,4.000000E+3,4.000000E+3,4.000000E+3,4.000000E+3,4.000000E+3,5.000000E+3,5.000000E+3,5.000000E+3,5.000000E+3,5.000000E+3,5.000000E+3,5.000000E+3,5.000000E+3,5.000000E+3,5.000000E+3,5.000000E+3,5.000000E+3,5.000000E+3,5.000000E+3,6.000000E+3,6.000000E+3,6.000000E+3,6.000000E+3,6.000000E+3,6.000000E+3,6.000000E+3,6.000000E+3,6.000000E+3,6.000000E+3,6.000000E+3,7.000000E+3,7.000000E+3,7.000000E+3,7.000000E+3,7.000000E+3,7.000000E+3,7.000000E+3,7.000000E+3,7.000000E+3,7.000000E+3,7.000000E+3,8.000000E+3,8.000000E+3,8.000000E+3,8.000000E+3,8.000000E+3,8.000000E+3,8.000000E+3,8.000000E+3,8.000000E+3,8.000000E+3,9.000000E+3,9.000000E+3,9.000000E+3,9.000000E+3,9.000000E+3,9.000000E+3,9.000000E+3,9.000000E+3,1.000000E+4,1.000000E+4,1.000000E+4,1.000000E+4,1.000000E+4,1.000000E+4,1.000000E+4,1.000000E+4,1.000000E+4,1.100000E+4,1.100000E+4,1.100000E+4,1.100000E+4,1.100000E+4,1.100000E+4,1.100000E+4,1.200000E+4,1.200000E+4,1.200000E+4,1.200000E+4,1.200000E+4,1.200000E+4,1.200000E+4,1.300000E+4,1.300000E+4,1.300000E+4,1.300000E+4,1.300000E+4,1.300000E+4,1.300000E+4,1.400000E+4,1.400000E+4,1.400000E+4,1.400000E+4])
+        self.Tmin        = np.min(self.temperature.data)
+        self.Tmax        = np.max(self.temperature.data)
+        self.TminPsat    = np.min(self.temperature.data[~np.isnan(self.saturation_pressure.data)])
+        self.name        = "PNF"
+        self.description = "Paratherm "+ self.name[1:]
+        self.reference   = "Paratherm2013"
         self.reshapeAll()
 
-    

dev/incompressible_liquids/CPIncomp/SecCoolFluids.py

@@ -295,104 +295,104 @@ class SecCoolSolutionData(DigitalData):
         """
         print("Loading SecCool fluids: ", end="")
         sec = []
-        sec += [SecCoolSolutionData(sFile='Antifrogen KF'           ,sFolder='xVolume',name='AKF',desc='Antifrogen KF, Potassium Formate'       ,ref='Clariant GmbH Jan. 2000, SecCool software')]
+        sec += [SecCoolSolutionData(sFile='Antifrogen KF'           ,sFolder='xVolume',name='AKF',desc='Antifrogen KF, Potassium Formate'       ,ref='Clariant2000,Skovrup2013')]
         print("{0}".format(sec[-1].name), end="")
-        sec += [SecCoolSolutionData(sFile='Antifrogen L'            ,sFolder='xVolume',name='AL' ,desc='Antifrogen L, Propylene Glycol'         ,ref='Clariant GmbH Jan. 2000, SecCool software')]
+        sec += [SecCoolSolutionData(sFile='Antifrogen L'            ,sFolder='xVolume',name='AL' ,desc='Antifrogen L, Propylene Glycol'         ,ref='Clariant2000,Skovrup2013')]
         print(", {0}".format(sec[-1].name), end="")
-        sec += [SecCoolSolutionData(sFile='Antifrogen N'            ,sFolder='xVolume',name='AN' ,desc='Antifrogen N, Ethylene Glycol'          ,ref='Clariant GmbH Jan. 2000, SecCool software')]
+        sec += [SecCoolSolutionData(sFile='Antifrogen N'            ,sFolder='xVolume',name='AN' ,desc='Antifrogen N, Ethylene Glycol'          ,ref='Clariant2000,Skovrup2013')]
         print(", {0}".format(sec[-1].name), end="")
-        sec += [SecCoolSolutionData(sFile='ASHRAE, Ethylene Glycol' ,sFolder='xVolume',name='AEG',desc='ASHRAE, Ethylene Glycol'                ,ref='ASHRAE Fundamentals Handbook 2001, SecCool software')]
+        sec += [SecCoolSolutionData(sFile='ASHRAE, Ethylene Glycol' ,sFolder='xVolume',name='AEG',desc='ASHRAE, Ethylene Glycol'                ,ref='ASHRAE2001,Skovrup2013')]
         print(", {0}".format(sec[-1].name), end="")
-        sec += [SecCoolSolutionData(sFile='ASHRAE, Propylene Glycol',sFolder='xVolume',name='APG',desc='ASHRAE, Propylene Glycol'               ,ref='ASHRAE Fundamentals Handbook 2001, SecCool software')]
+        sec += [SecCoolSolutionData(sFile='ASHRAE, Propylene Glycol',sFolder='xVolume',name='APG',desc='ASHRAE, Propylene Glycol'               ,ref='ASHRAE2001,Skovrup2013')]
         print(", {0}".format(sec[-1].name), end="")
-        sec += [SecCoolSolutionData(sFile='Glykosol N'              ,sFolder='xVolume',name='GKN',desc='Glykosol N, Ethylene Glycol'            ,ref='pro KUEHLSOLE GmbH, SecCool software')]
+        sec += [SecCoolSolutionData(sFile='Glykosol N'              ,sFolder='xVolume',name='GKN',desc='Glykosol N, Ethylene Glycol'            ,ref='PKS2005,Skovrup2013')]
         print(", {0}".format(sec[-1].name), end="")
-        sec += [SecCoolSolutionData(sFile='Pekasol 2000'            ,sFolder='xVolume',name='PK2',desc='Pekasol 2000, Potassium acetate/formate',ref='pro KUEHLSOLE GmbH, SecCool software')]
+        sec += [SecCoolSolutionData(sFile='Pekasol 2000'            ,sFolder='xVolume',name='PK2',desc='Pekasol 2000, Potassium acetate/formate',ref='PKS2005,Skovrup2013')]
         print(", {0}".format(sec[-1].name), end="")
-        sec += [SecCoolSolutionData(sFile='Pekasol L'               ,sFolder='xVolume',name='PKL',desc='Pekasol L, Propylene Glycol'            ,ref='pro KUEHLSOLE GmbH, SecCool software')]
+        sec += [SecCoolSolutionData(sFile='Pekasol L'               ,sFolder='xVolume',name='PKL',desc='Pekasol L, Propylene Glycol'            ,ref='PKS2005,Skovrup2013')]
         print(", {0}".format(sec[-1].name), end="")
-        sec += [SecCoolSolutionData(sFile='Zitrec AC'               ,sFolder='xVolume',name='ZAC',desc='Zitrec AC, Corrosion Inhibitor'         ,ref='Arteco, SecCool software')]
+        sec += [SecCoolSolutionData(sFile='Zitrec AC'               ,sFolder='xVolume',name='ZAC',desc='Zitrec AC, Corrosion Inhibitor'         ,ref='Arteco2010,Skovrup2013')]
         print(", {0}".format(sec[-1].name), end="")
-        sec += [SecCoolSolutionData(sFile='Zitrec FC'               ,sFolder='xVolume',name='ZFC',desc='Zitrec FC, Propylene Glycol'            ,ref='Arteco, SecCool software')]
+        sec += [SecCoolSolutionData(sFile='Zitrec FC'               ,sFolder='xVolume',name='ZFC',desc='Zitrec FC, Propylene Glycol'            ,ref='Arteco2010,Skovrup2013')]
         print(", {0}".format(sec[-1].name), end="")
-        sec += [SecCoolSolutionData(sFile='Zitrec LC'               ,sFolder='xVolume',name='ZLC',desc='Zitrec LC, Propylene Glycol'            ,ref='Arteco, SecCool software')]
+        sec += [SecCoolSolutionData(sFile='Zitrec LC'               ,sFolder='xVolume',name='ZLC',desc='Zitrec LC, Propylene Glycol'            ,ref='Arteco2010,Skovrup2013')]
         print(", {0}".format(sec[-1].name), end="")
-        sec += [SecCoolSolutionData(sFile='Zitrec MC'               ,sFolder='xVolume',name='ZMC',desc='Zitrec MC, Ethylene Glycol'             ,ref='Arteco, SecCool software')]
+        sec += [SecCoolSolutionData(sFile='Zitrec MC'               ,sFolder='xVolume',name='ZMC',desc='Zitrec MC, Ethylene Glycol'             ,ref='Arteco2010,Skovrup2013')]
         print(", {0}".format(sec[-1].name), end="")
-        sec += [SecCoolSolutionData(sFile='Zitrec M'                ,sFolder='xVolume',name='ZM' ,desc='Zitrec M, Ethylene Glycol'              ,ref='Arteco, SecCool software')]
+        sec += [SecCoolSolutionData(sFile='Zitrec M'                ,sFolder='xVolume',name='ZM' ,desc='Zitrec M, Ethylene Glycol'              ,ref='Arteco2010,Skovrup2013')]
         print(", {0}".format(sec[-1].name), end="")
 
-        sec += [SecCoolSolutionData(sFile='Melinder, Ammonia'            ,sFolder='xMass',name='MAM2',desc='Melinder, Ammonia'            ,ref='Melinder-BOOK-2010, SecCool software')]
+        sec += [SecCoolSolutionData(sFile='Melinder, Ammonia'            ,sFolder='xMass',name='MAM2',desc='Melinder, Ammonia'            ,ref='Melinder2010,Skovrup2013')]
         print(", {0}".format(sec[-1].name), end="")
-        sec += [SecCoolSolutionData(sFile='Melinder, Calcium Cloride'    ,sFolder='xMass',name='MCA2',desc='Melinder, Calcium Chloride'   ,ref='Melinder-BOOK-2010, SecCool software')]
+        sec += [SecCoolSolutionData(sFile='Melinder, Calcium Cloride'    ,sFolder='xMass',name='MCA2',desc='Melinder, Calcium Chloride'   ,ref='Melinder2010,Skovrup2013')]
         print(", {0}".format(sec[-1].name), end="")
-        sec += [SecCoolSolutionData(sFile='Melinder, Ethanol'            ,sFolder='xMass',name='MEA2',desc='Melinder, Ethanol'            ,ref='Melinder-BOOK-2010, SecCool software')]
+        sec += [SecCoolSolutionData(sFile='Melinder, Ethanol'            ,sFolder='xMass',name='MEA2',desc='Melinder, Ethanol'            ,ref='Melinder2010,Skovrup2013')]
         print(", {0}".format(sec[-1].name), end="")
-        sec += [SecCoolSolutionData(sFile='Melinder, Ethylene glycol'    ,sFolder='xMass',name='MEG2',desc='Melinder, Ethylene Glycol'    ,ref='Melinder-BOOK-2010, SecCool software')]
+        sec += [SecCoolSolutionData(sFile='Melinder, Ethylene glycol'    ,sFolder='xMass',name='MEG2',desc='Melinder, Ethylene Glycol'    ,ref='Melinder2010,Skovrup2013')]
         print(", {0}".format(sec[-1].name), end="")
-        sec += [SecCoolSolutionData(sFile='Melinder, Glycerol'           ,sFolder='xMass',name='MGL2',desc='Melinder, Glycerol'           ,ref='Melinder-BOOK-2010, SecCool software')]
+        sec += [SecCoolSolutionData(sFile='Melinder, Glycerol'           ,sFolder='xMass',name='MGL2',desc='Melinder, Glycerol'           ,ref='Melinder2010,Skovrup2013')]
         print(", {0}".format(sec[-1].name), end="")
-        sec += [SecCoolSolutionData(sFile='Melinder, Magnesium Chloride' ,sFolder='xMass',name='MMG2',desc='Melinder, Magnesium Chloride' ,ref='Melinder-BOOK-2010, SecCool software')]
+        sec += [SecCoolSolutionData(sFile='Melinder, Magnesium Chloride' ,sFolder='xMass',name='MMG2',desc='Melinder, Magnesium Chloride' ,ref='Melinder2010,Skovrup2013')]
         print(", {0}".format(sec[-1].name), end="")
-        sec += [SecCoolSolutionData(sFile='Melinder, Methanol'           ,sFolder='xMass',name='MMA2',desc='Melinder, Methanol'           ,ref='Melinder-BOOK-2010, SecCool software')]
+        sec += [SecCoolSolutionData(sFile='Melinder, Methanol'           ,sFolder='xMass',name='MMA2',desc='Melinder, Methanol'           ,ref='Melinder2010,Skovrup2013')]
         print(", {0}".format(sec[-1].name), end="")
-        sec += [SecCoolSolutionData(sFile='Melinder, Potassium Acetate'  ,sFolder='xMass',name='MKA2',desc='Melinder, Potassium Acetate'  ,ref='Melinder-BOOK-2010, SecCool software')]
+        sec += [SecCoolSolutionData(sFile='Melinder, Potassium Acetate'  ,sFolder='xMass',name='MKA2',desc='Melinder, Potassium Acetate'  ,ref='Melinder2010,Skovrup2013')]
         print(", {0}".format(sec[-1].name), end="")
-        sec += [SecCoolSolutionData(sFile='Melinder, Potassium Carbonate',sFolder='xMass',name='MKC2',desc='Melinder, Potassium Carbonate',ref='Melinder-BOOK-2010, SecCool software')]
+        sec += [SecCoolSolutionData(sFile='Melinder, Potassium Carbonate',sFolder='xMass',name='MKC2',desc='Melinder, Potassium Carbonate',ref='Melinder2010,Skovrup2013')]
         print(", {0}".format(sec[-1].name), end="")
-        sec += [SecCoolSolutionData(sFile='Melinder, Propylene Glycol'   ,sFolder='xMass',name='MPG2',desc='Melinder, Propylene Glycol'   ,ref='Melinder-BOOK-2010, SecCool software')]
+        sec += [SecCoolSolutionData(sFile='Melinder, Propylene Glycol'   ,sFolder='xMass',name='MPG2',desc='Melinder, Propylene Glycol'   ,ref='Melinder2010,Skovrup2013')]
         print(", {0}".format(sec[-1].name), end="")
-        sec += [SecCoolSolutionData(sFile='Melinder, Sodium Chloride'    ,sFolder='xMass',name='MNA2',desc='Melinder, Sodium Chloride'    ,ref='Melinder-BOOK-2010, SecCool software')]
+        sec += [SecCoolSolutionData(sFile='Melinder, Sodium Chloride'    ,sFolder='xMass',name='MNA2',desc='Melinder, Sodium Chloride'    ,ref='Melinder2010,Skovrup2013')]
         print(", {0}".format(sec[-1].name), end="")
-        sec += [SecCoolSolutionData(sFile='VDI, Calcium Cloride'         ,sFolder='xMass',name='VCA' ,desc='VDI, Calcium Cloride'         ,ref='VDI Waermeatlas 9th Edition 2002, SecCool software')]
+        sec += [SecCoolSolutionData(sFile='VDI, Calcium Cloride'         ,sFolder='xMass',name='VCA' ,desc='VDI, Calcium Cloride'         ,ref='Preisegger2010,Skovrup2013')]
         print(", {0}".format(sec[-1].name), end="")
-        sec += [SecCoolSolutionData(sFile='VDI, Magnesium Chloride'      ,sFolder='xMass',name='VMG' ,desc='VDI, Magnesium Chloride'      ,ref='VDI Waermeatlas 9th Edition 2002, SecCool software')]
+        sec += [SecCoolSolutionData(sFile='VDI, Magnesium Chloride'      ,sFolder='xMass',name='VMG' ,desc='VDI, Magnesium Chloride'      ,ref='Preisegger2010,Skovrup2013')]
         print(", {0}".format(sec[-1].name), end="")
-        sec += [SecCoolSolutionData(sFile='VDI, Methanol'                ,sFolder='xMass',name='VMA' ,desc='VDI, Methanol'                ,ref='VDI Waermeatlas 9th Edition 2002, SecCool software')]
+        sec += [SecCoolSolutionData(sFile='VDI, Methanol'                ,sFolder='xMass',name='VMA' ,desc='VDI, Methanol'                ,ref='Preisegger2010,Skovrup2013')]
         print(", {0}".format(sec[-1].name), end="")
-        sec += [SecCoolSolutionData(sFile='VDI, Potassium Carbonate'     ,sFolder='xMass',name='VKC' ,desc='VDI, Potassium Carbonate'     ,ref='VDI Waermeatlas 9th Edition 2002, SecCool software')]
+        sec += [SecCoolSolutionData(sFile='VDI, Potassium Carbonate'     ,sFolder='xMass',name='VKC' ,desc='VDI, Potassium Carbonate'     ,ref='Preisegger2010,Skovrup2013')]
         print(", {0}".format(sec[-1].name), end="")
-        sec += [SecCoolSolutionData(sFile='VDI, Sodium Chloride'         ,sFolder='xMass',name='VNA' ,desc='VDI, Sodium Chloride'         ,ref='VDI Waermeatlas 9th Edition 2002, SecCool software')]
+        sec += [SecCoolSolutionData(sFile='VDI, Sodium Chloride'         ,sFolder='xMass',name='VNA' ,desc='VDI, Sodium Chloride'         ,ref='Preisegger2010,Skovrup2013')]
         print(", {0}".format(sec[-1].name), end="")
 
-        sec += [SecCoolSolutionData(sFile='HFE-7100'     ,sFolder='xPure',name='HFE2' ,desc='HFE-7100, Hydrofluoroether'                      ,ref='3M Novec, SecCool software')]
+        sec += [SecCoolSolutionData(sFile='HFE-7100'     ,sFolder='xPure',name='HFE2' ,desc='HFE-7100, Hydrofluoroether'                      ,ref='3M2007,Skovrup2013')]
         print(", {0}".format(sec[-1].name), end="")
-        sec += [SecCoolSolutionData(sFile='NBS, Water'   ,sFolder='xPure',name='NBS' ,desc='NBS, Water'                                      ,ref='Properties of Water and Steam in SI-Units, 2nd Revised and Updated Printing, Springer 1979, pp. 175 ff., SecCool software')]
+        sec += [SecCoolSolutionData(sFile='NBS, Water'   ,sFolder='xPure',name='NBS' ,desc='NBS, Water'                                      ,ref='Schmidt1979,Skovrup2013')]
         print(", {0}".format(sec[-1].name), end="")
-        sec += [SecCoolSolutionData(sFile='Paracryol'    ,sFolder='xPure',name='PCL' ,desc='Paracryol, Aliphatic Hydrocarbon'                ,ref='Sulzer Chemtech AG, SecCool software')]
+        sec += [SecCoolSolutionData(sFile='Paracryol'    ,sFolder='xPure',name='PCL' ,desc='Paracryol, Aliphatic Hydrocarbon'                ,ref='Sulzer1999,Skovrup2013')]
         print(", {0}".format(sec[-1].name), end="")
-        sec += [SecCoolSolutionData(sFile='Paratherm NF' ,sFolder='xPure',name='PNF' ,desc='Paratherm NF, Hydrotreated mineral oil'          ,ref='Paratherm Ltd, SecCool software')]
+        sec += [SecCoolSolutionData(sFile='Paratherm NF' ,sFolder='xPure',name='PNF2' ,desc='Paratherm NF, Hydrotreated mineral oil'          ,ref='Paratherm2013,Skovrup2013')]
         print(", {0}".format(sec[-1].name), end="")
-        sec += [SecCoolSolutionData(sFile='Tyfoxit 1.10' ,sFolder='xPure',name='TY10',desc='Tyfoxit 1.10, Potassium Acetate'                 ,ref='Tyforop Chemie Gmbh - Technical information 09/99, SecCool software')]
+        sec += [SecCoolSolutionData(sFile='Tyfoxit 1.10' ,sFolder='xPure',name='TY10',desc='Tyfoxit 1.10, Potassium Acetate'                 ,ref='Tyfoprop1999,Skovrup2013')]
         print(", {0}".format(sec[-1].name), end="")
-        sec += [SecCoolSolutionData(sFile='Tyfoxit 1.15' ,sFolder='xPure',name='TY15',desc='Tyfoxit 1.15, Potassium Acetate'                 ,ref='Tyforop Chemie Gmbh - Technical information 09/99, SecCool software')]
+        sec += [SecCoolSolutionData(sFile='Tyfoxit 1.15' ,sFolder='xPure',name='TY15',desc='Tyfoxit 1.15, Potassium Acetate'                 ,ref='Tyfoprop1999,Skovrup2013')]
         print(", {0}".format(sec[-1].name), end="")
-        sec += [SecCoolSolutionData(sFile='Tyfoxit 1.20' ,sFolder='xPure',name='TY20',desc='Tyfoxit 1.20, Potassium Acetate'                 ,ref='Tyforop Chemie Gmbh - Technical information 09/99, SecCool software')]
+        sec += [SecCoolSolutionData(sFile='Tyfoxit 1.20' ,sFolder='xPure',name='TY20',desc='Tyfoxit 1.20, Potassium Acetate'                 ,ref='Tyfoprop1999,Skovrup2013')]
         print(", {0}".format(sec[-1].name), end="")
-        sec += [SecCoolSolutionData(sFile='Tyfoxit 1.24' ,sFolder='xPure',name='TY24',desc='Tyfoxit 1.24, Potassium Acetate'                 ,ref='Tyforop Chemie Gmbh - Technical information 09/99, SecCool software')]
+        sec += [SecCoolSolutionData(sFile='Tyfoxit 1.24' ,sFolder='xPure',name='TY24',desc='Tyfoxit 1.24, Potassium Acetate'                 ,ref='Tyfoprop1999,Skovrup2013')]
         print(", {0}".format(sec[-1].name), end="")
-        sec += [SecCoolSolutionData(sFile='Zitrec S10'   ,sFolder='xPure',name='ZS10',desc='Zitrec S10, Potassium formate/Sodium propionate' ,ref='Arteco, SecCool software')]
+        sec += [SecCoolSolutionData(sFile='Zitrec S10'   ,sFolder='xPure',name='ZS10',desc='Zitrec S10, Potassium formate/Sodium propionate' ,ref='Arteco2010,Skovrup2013')]
         print(", {0}".format(sec[-1].name), end="")
-        sec += [SecCoolSolutionData(sFile='Zitrec S25'   ,sFolder='xPure',name='ZS25',desc='Zitrec S25, Potassium formate/Sodium propionate' ,ref='Arteco, SecCool software')]
+        sec += [SecCoolSolutionData(sFile='Zitrec S25'   ,sFolder='xPure',name='ZS25',desc='Zitrec S25, Potassium formate/Sodium propionate' ,ref='Arteco2010,Skovrup2013')]
         print(", {0}".format(sec[-1].name), end="")
-        sec += [SecCoolSolutionData(sFile='Zitrec S40'   ,sFolder='xPure',name='ZS40',desc='Zitrec S40, Potassium formate/Sodium propionate' ,ref='Arteco, SecCool software')]
+        sec += [SecCoolSolutionData(sFile='Zitrec S40'   ,sFolder='xPure',name='ZS40',desc='Zitrec S40, Potassium formate/Sodium propionate' ,ref='Arteco2010,Skovrup2013')]
         print(", {0}".format(sec[-1].name), end="")
-        sec += [SecCoolSolutionData(sFile='Zitrec S45'   ,sFolder='xPure',name='ZS45',desc='Zitrec S45, Potassium formate/Sodium propionate' ,ref='Arteco, SecCool software')]
+        sec += [SecCoolSolutionData(sFile='Zitrec S45'   ,sFolder='xPure',name='ZS45',desc='Zitrec S45, Potassium formate/Sodium propionate' ,ref='Arteco2010,Skovrup2013')]
         print(", {0}".format(sec[-1].name), end="")
-        sec += [SecCoolSolutionData(sFile='Zitrec S55'   ,sFolder='xPure',name='ZS55',desc='Zitrec S55, Potassium formate/Sodium propionate' ,ref='Arteco, SecCool software')]
+        sec += [SecCoolSolutionData(sFile='Zitrec S55'   ,sFolder='xPure',name='ZS55',desc='Zitrec S55, Potassium formate/Sodium propionate' ,ref='Arteco2010,Skovrup2013')]
         print(", {0}".format(sec[-1].name), end="")
-        sec += [SecCoolSolutionData(sFile='Syltherm XLT'   ,sFolder='xPure',name='XLT2',desc='Syltherm XLT, Polydimethylsiloxan' ,ref='Dow Chemicals, SecCool software')]
+        sec += [SecCoolSolutionData(sFile='Syltherm XLT'   ,sFolder='xPure',name='XLT2',desc='Syltherm XLT, Polydimethylsiloxan' ,ref='Dow1997,Skovrup2013')]
         print(", {0}".format(sec[-1].name), end="")
-        sec += [SecCoolSolutionData(sFile='Dowtherm J'   ,sFolder='xPure',name='DowJ2',desc='Dowtherm J, Diethylbenzene mixture' ,ref='Dow Chemicals, SecCool software')]
+        sec += [SecCoolSolutionData(sFile='Dowtherm J'   ,sFolder='xPure',name='DowJ2',desc='Dowtherm J, Diethylbenzene mixture' ,ref='Dow1997,Skovrup2013')]
         print(", {0}".format(sec[-1].name), end="")
-        sec += [SecCoolSolutionData(sFile='Dowtherm Q'   ,sFolder='xPure',name='DowQ2',desc='Dowtherm Q, Diphenylethane/alkylated aromatics' ,ref='Dow Chemicals, SecCool software')]
+        sec += [SecCoolSolutionData(sFile='Dowtherm Q'   ,sFolder='xPure',name='DowQ2',desc='Dowtherm Q, Diphenylethane/alkylated aromatics' ,ref='Dow1997,Skovrup2013')]
         print(", {0}".format(sec[-1].name), end="")
 
-        sec += [SecCoolIceData(sFile='IceEA'   ,sFolder='xMass',name='IceEA',desc='Ice slurry with Ethanol' ,ref='Danish Technological Institute, SecCool software')]
+        sec += [SecCoolIceData(sFile='IceEA'   ,sFolder='xMass',name='IceEA',desc='Ice slurry with Ethanol' ,ref='Kauffeld2001,Skovrup2013')]
         print(", {0}".format(sec[-1].name), end="")
-        sec += [SecCoolIceData(sFile='IceNA'   ,sFolder='xMass',name='IceNA',desc='Ice slurry with NaCl' ,ref='Danish Technological Institute, SecCool software')]
+        sec += [SecCoolIceData(sFile='IceNA'   ,sFolder='xMass',name='IceNA',desc='Ice slurry with NaCl' ,ref='Kauffeld2001,Skovrup2013')]
         print(", {0}".format(sec[-1].name), end="")
-        sec += [SecCoolIceData(sFile='IcePG'   ,sFolder='xMass',name='IcePG',desc='Ice slurry with Propylene Glycol' ,ref='Danish Technological Institute, SecCool software')]
+        sec += [SecCoolIceData(sFile='IcePG'   ,sFolder='xMass',name='IcePG',desc='Ice slurry with Propylene Glycol' ,ref='Kauffeld2001,Skovrup2013')]
         print(", {0}".format(sec[-1].name), end="")
 
         sec += [ThermogenVP1869()]
@@ -421,7 +421,7 @@ class SecCoolIceData(SecCoolSolutionData):
     A base class that can be fed with a fluid ID from SecCool
     to read data files sitting in data/SecCool/xTables.
     """
-    def __init__(self,sFile=None,sFolder=None,name=None,desc=None,ref='Danish Technological Institute, SecCool software'):
+    def __init__(self,sFile=None,sFolder=None,name=None,desc=None,ref='Danish Technological Institute,Skovrup2013'):
         SecCoolSolutionData.__init__(self,sFile=sFile,sFolder=sFolder,name=name,desc=desc,ref=ref)
 
         #self.density.xData,self.density.yData,self.density.data = self.getArray(dataID="Rho")
@@ -520,7 +520,7 @@ class ThermogenVP1869(PureData,DigitalData):
         PureData.__init__(self)
         self.name = "TVP1869"
         self.description = "Thermogen VP 1869"
-        self.reference = "Hoechst, SecCool software"
+        self.reference = "Hoechst1995,Skovrup2013"
 
         self.Tmax =  20 + 273.15
         self.Tmin = -80 + 273.15
@@ -569,7 +569,7 @@ class Freezium(DigitalData):
 
         self.name = "FRE"
         self.description = "Freezium, Potassium Formate"
-        self.reference = "Kemira Chemicals OY, SecCool software"
+        self.reference = "Kemira1998,Skovrup2013"
 
         self.Tmin = -40 + 273.00
         self.Tmax = +40 + 273.00
@@ -668,7 +668,7 @@ class AS10(PureData,DigitalData):
         PureData.__init__(self)
         self.name = "AS10"
         self.description = "Aspen Temper -10, Potassium acetate/formate"
-        self.reference = "Aspen Petroleum AB, SecCool software"
+        self.reference = "Aspen2001,Skovrup2013"
 
         self.Tmax = 30 + 273.15
         self.Tmin = -10 + 273.15
@@ -704,7 +704,7 @@ class AS20(PureData,DigitalData):
         PureData.__init__(self)
         self.name = "AS20"
         self.description = "Aspen Temper -20, Potassium acetate/formate"
-        self.reference = "Aspen Petroleum AB, SecCool software"
+        self.reference = "Aspen2001,Skovrup2013"
 
         self.Tmax = 30 + 273.15
         self.Tmin = -20 + 273.15
@@ -752,7 +752,7 @@ class AS30(PureData,DigitalData):
         PureData.__init__(self)
         self.name = "AS30"
         self.description = "Aspen Temper -30, Potassium acetate/formate"
-        self.reference = "Aspen Petroleum AB, SecCool software"
+        self.reference = "Aspen2001,Skovrup2013"
 
         self.Tmax = 30 + 273.15
         self.Tmin = -30 + 273.15
@@ -800,7 +800,7 @@ class AS40(PureData,DigitalData):
         PureData.__init__(self)
         self.name = "AS40"
         self.description = "Aspen Temper -40, Potassium acetate/formate"
-        self.reference = "Aspen Petroleum AB, SecCool software"
+        self.reference = "Aspen2001,Skovrup2013"
 
         self.Tmax = 30 + 273.15
         self.Tmin = -40 + 273.15
@@ -847,7 +847,7 @@ class AS55(PureData,DigitalData):
         PureData.__init__(self)
         self.name = "AS55"
         self.description = "Aspen Temper -55, Potassium acetate/formate"
-        self.reference = "Aspen Petroleum AB, SecCool software"
+        self.reference = "Aspen2001,Skovrup2013"
 
         self.Tmax = 30 + 273.15
         self.Tmin = -55 + 273.15

dev/incompressible_liquids/CPIncomp/SolutionFluids.py

@@ -11,7 +11,7 @@ class LiBrData(SolutionData):
     def __init__(self):
         SolutionData.__init__(self)
         self.name = "LiBr"
-        self.description = "Lithium-Bromide solution from Patek2006"
+        self.description = "Aqueous lithium-bromide solution "
         self.reference = "Patek2006"
 
         self.temperature.data         = np.array([2.73000e+02, 2.84947e+02, 2.96895e+02, 3.08842e+02, 3.20789e+02, 3.32737e+02, 3.44684e+02, 3.56632e+02, 3.68579e+02, 3.80526e+02, 3.92474e+02, 4.04421e+02, 4.16368e+02, 4.28316e+02, 4.40263e+02, 4.52211e+02, 4.64158e+02, 4.76105e+02, 4.88053e+02, 5.00000e+02]) # Kelvin

dev/incompressible_liquids/CPIncomp/WriterObjects.py

@@ -1,3 +1,5 @@
+#!/usr/bin/env python
+# -*- coding: utf8 -*-
 from __future__ import division, print_function
 import numpy as np
 
@@ -10,9 +12,73 @@ from matplotlib.patches import Rectangle
 from matplotlib.ticker import MaxNLocator
 from matplotlib.backends.backend_pdf import PdfPages
 import itertools
-from datetime import datetime
 import matplotlib
-import csv
+from CoolProp.BibtexParser import BibTeXerClass
+from warnings import warn
+
+# See: https://docs.python.org/2/library/csv.html#csv-examples
+import csv, codecs, cStringIO
+
+class UTF8Recoder:
+    """
+    Iterator that reads an encoded stream and reencodes the input to UTF-8
+    """
+    def __init__(self, f, encoding):
+        self.reader = codecs.getreader(encoding)(f)
+
+    def __iter__(self):
+        return self
+
+    def next(self):
+        return self.reader.next().encode("utf-8")
+
+class UnicodeReader:
+    """
+    A CSV reader which will iterate over lines in the CSV file "f",
+    which is encoded in the given encoding.
+    """
+
+    def __init__(self, f, dialect=csv.excel, encoding="utf-8", **kwds):
+        f = UTF8Recoder(f, encoding)
+        self.reader = csv.reader(f, dialect=dialect, **kwds)
+
+    def next(self):
+        row = self.reader.next()
+        return [unicode(s, "utf-8") for s in row]
+
+    def __iter__(self):
+        return self
+
+class UnicodeWriter:
+    """
+    A CSV writer which will write rows to CSV file "f",
+    which is encoded in the given encoding.
+    """
+
+    def __init__(self, f, dialect=csv.excel, encoding="utf-8", **kwds):
+        # Redirect output to a queue
+        self.queue = cStringIO.StringIO()
+        self.writer = csv.writer(self.queue, dialect=dialect, **kwds)
+        self.stream = f
+        self.encoder = codecs.getincrementalencoder(encoding)()
+
+    def writerow(self, row):
+        self.writer.writerow([s.encode("utf-8") for s in row])
+        # Fetch UTF-8 output from the queue ...
+        data = self.queue.getvalue()
+        data = data.decode("utf-8")
+        # ... and reencode it into the target encoding
+        data = self.encoder.encode(data)
+        # write to the target stream
+        self.stream.write(data)
+        # empty queue
+        self.queue.truncate(0)
+
+    def writerows(self, rows):
+        for row in rows:
+            self.writerow(row)
+
+
 
 class SolutionDataWriter(object):
     """
@@ -22,7 +88,8 @@ class SolutionDataWriter(object):
     information came from.
     """
     def __init__(self):
-        pass
+        bibFile = os.path.join(os.path.dirname(__file__),'../../../Web/fluid_properties/Incompressibles.bib')
+        self.bibtexer = BibTeXerClass(bibFile)
 
     def fitAll(self, fluidObject=SolutionData()):
 
@@ -660,6 +727,9 @@ class SolutionDataWriter(object):
             axVal.plot(pFree, zFree, alpha=0.25, ls=':', color=fitFormatter["color"])
 
         if not zError is None and not axErr is None:
+            #formatter = matplotlib.ticker.ScalarFormatter(useOffset=False)
+            #axErr.yaxis.set_major_formatter(formatter)
+            #axErr.yaxis.get_major_formatter().useOffset=False
             axErr.plot(pData, zError, label='error' , **errorFormatter)
 
         elif not axErr is None:
@@ -732,7 +802,27 @@ class SolutionDataWriter(object):
 
         #myAnnotate('Name: ',solObj.name,x=x,y=y); x += .0; y -= dy
         myAnnotate('Description: ',solObj.description,x=x,y=y); x += .0; y -= dy
-        myAnnotate('Source: ',solObj.reference,x=x,y=y); x += .0; y -= dy
+
+        # TODO: Debug bibtexer
+        refs = solObj.reference.split(",")
+        maxLength = 75
+        for i in range(len(refs)):
+            refs[i] = refs[i].strip()
+            try:
+                refs[i] = self.bibtexer.getEntry(key=refs[i], fmt='plaintext').strip()
+            except Exception as e:
+                warn("Your string \"{0}\"was not a valid Bibtex key, I will use it directly: {1}".format(refs[i],e))
+                pass
+            if len(refs[i])>maxLength:
+                refs[i] = refs[i][0:maxLength-3]+u'...'
+
+            if i==0:
+                myAnnotate('Source: ',refs[i],x=x,y=y); x += .0 #; y -= 2*dy
+            elif i==1:
+                myAnnotate(   '     ',refs[i],x=x,y=y-dy); x += .0 #; y -= 2*dy
+
+        y -= 2*dy
+        yRestart = y
         myAnnotate('Temperature: ',u'{0} \u00B0C to {1} \u00B0C'.format(solObj.Tmin-273.15, solObj.Tmax-273.15),x=x,y=y); x += .0; y -= dy
         conc = False
         if solObj.xid==solObj.ifrac_mass: conc=True
@@ -755,7 +845,7 @@ class SolutionDataWriter(object):
             myAnnotate('Spec. Heat: ','no information',x=x,y=y)
         x += .0; y -= dy
 
-        x = xStart + dx; y = yStart-dy-dy
+        x = xStart + dx; y = yRestart
         if solObj.conductivity.source!=solObj.conductivity.SOURCE_NOT_SET:
             myAnnotate('Th. Cond.: ',u'{0} to {1} {2}'.format(solObj.conductivity.source, solObj.conductivity.type, solObj.conductivity.coeffs.shape),x=x,y=y)
         else:
@@ -968,7 +1058,7 @@ class SolutionDataWriter(object):
 
         table_axis.legend(
           legVal, legKey,
-          bbox_to_anchor=(0.0, -0.025, 1., -0.025),
+          bbox_to_anchor=(0.0, -0.03, 1., -0.03),
           ncol=len(legKey), mode="expand", borderaxespad=0.,
           numpoints=1)
         #table_axis.legend(handles, labels, bbox_to_anchor=(0.0, -0.1), loc=2, ncol=3)
@@ -1101,26 +1191,26 @@ class SolutionDataWriter(object):
         return rst
 
     def table_div(self, max_cols, header_flag=1, indent=2):
-        out = ""
+        out = u""
         for i in range(indent):
-            out += " "
+            out += u" "
         if header_flag == 1:
-            style = "="
+            style = u"="
         else:
-            style = "-"
+            style = u"-"
         for max_col in max_cols:
-            out += max_col * style + " "
-        out += "\n"
+            out += max_col * style + u" "
+        out += u"\n"
         return out
 
     def normalize_row(self, row, max_cols, indent=2):
-        r = ""
+        r = u""
         for i in range(indent):
-            r += " "
+            r += u" "
 
         for i, max_col in enumerate(max_cols):
-            r += row[i] + (max_col  - len(row[i]) + 1) * " "
-        return r + "\n"
+            r += row[i] + (max_col  - len(row[i]) + 1) * u" "
+        return r + u"\n"
 
 
     def writeTextToFile(self, path,text):
@@ -1128,19 +1218,21 @@ class SolutionDataWriter(object):
         if not os.path.exists(os.path.dirname(path)):
             os.makedirs(os.path.dirname(path))
         with open(path, 'w') as f:
-            f.write(text)
+            f.write(text.encode('utf-8'))
+
         return True
 
-    def writeTxtTableToFile(self, path,table,head=""):
-        if not head == "":
-            return self.writeTextToFile(path+".txt", head+"\n\n"+self.make_table(table))
+    def writeTxtTableToFile(self, path,table,head=u""):
+        if not head == u"":
+            return self.writeTextToFile(path+".txt", head+u"\n\n"+self.make_table(table))
         return self.writeTextToFile(path+".txt", self.make_table(table))
 
     def writeCsvTableToFile(self, path,table):
         if not os.path.exists(os.path.dirname(path+".csv")):
             os.makedirs(os.path.dirname(path+".csv"))
         with open(path+".csv", 'wb') as f:
-            writer = csv.writer(f)
+            #writer = csv.writer(f)
+            writer = UnicodeWriter(f)
             writer.writerows(table)
         return True
 
@@ -1156,6 +1248,10 @@ class SolutionDataWriter(object):
         reportFile = os.path.join("..","_static","fluid_properties","incompressible","report","{0}_fitreport.pdf".format(name))
         return self.d(name,reportFile)
 
+    def getCitation(self, keys):
+        return u":cite:`{0}`".format(keys)
+
+
     def checkForNumber(self, number):
         try:
             n = float(number)
@@ -1174,20 +1270,20 @@ class SolutionDataWriter(object):
 #             link = "{0}".format(text)
 #             pass
         # TODO: Fix this!
-        link = ":download:`{0}<{1}>`".format(text,target)
+        link = u":download:`{0}<{1}>`".format(text,target)
         return link
 
     def m(self, math):
-        text = ":math:`{0}`".format(math)
+        text = u":math:`{0}`".format(math)
         return text
 
     def c(self, number):
         #text = "{0:5.2f} |degC|".format(self.checkForNumber(number)-273.15)
-        text = "{0:5.2f}".format(self.checkForNumber(number)-273.15)
+        text = u"{0:5.2f}".format(self.checkForNumber(number)-273.15)
         return text
 
     def x(self, number):
-        text = "{0:3.2f}".format(self.checkForNumber(number))
+        text = u"{0:3.2f}".format(self.checkForNumber(number))
         return text
 
 
@@ -1200,14 +1296,20 @@ class SolutionDataWriter(object):
         if np.any(xmin>0.0) and np.any(xmax<1.0): use_x = True
         else: use_x = False
 
-        header = ['Name', 'Description', 'Reference', \
-          self.m('T_{min}')+" (|degC|)", self.m('T_{max}')+" (|degC|)"]
-        if use_x: header.extend([self.m('x_{min}'), self.m('x_{max}')])
+        header = [u'Name', u'Description', u'Reference', \
+          self.m(u'T_\\text{min}')+u" (°C)", self.m(u'T_\\text{max}')+u" (°C)"]
+        if use_x: header.extend([self.m(u'x_\\text{min}'), self.m(u'x_\\text{max}')])
 
         testTable = []
         testTable.append(header) # Headline
         for fluid in solObjs:
-            testTable.append([self.getReportLink(fluid.name), fluid.description, fluid.reference, self.c(fluid.Tmin), self.c(fluid.Tmax)])
+            testTable.append([
+                self.getReportLink(fluid.name),
+                fluid.description,
+                self.getCitation(fluid.reference),
+                self.c(fluid.Tmin),
+                self.c(fluid.Tmax)
+            ])
             if use_x: testTable[-1].extend([self.x(fluid.xmin), self.x(fluid.xmax)])
 
         self.writeTableToFile(path, testTable)

dev/incompressible_liquids/all_incompressibles.py

@@ -65,23 +65,13 @@ if __name__ == '__main__':
     if runTest:
         solObjs = []
         from CPIncomp.SecCoolFluids import SecCoolSolutionData,SecCoolIceData,ThermogenVP1869
-        from CPIncomp.PureFluids import Texatherm22
-        solObjs += [SecCoolSolutionData(sFile='Melinder, Ammonia'            ,sFolder='xMass',name='MAM2',desc='Melinder, Ammonia'            ,ref='Melinder-BOOK-2010, SecCool software')]
-        solObjs += [SecCoolIceData(sFile='IceNA'   ,sFolder='xMass',name='IceNA',desc='Ice slurry with NaCl' ,ref='Danish Technological Institute, SecCool software')]
-        #solObjs = [Freezium()]
-        #solObjs[0].density.DEBUG = True
-        #solObjs[0].specific_heat.DEBUG = True
-        #solObjs[0].conductivity.DEBUG = True
-        #solObjs[0].viscosity.DEBUG = True
-        #solObjs[0].T_freeze.DEBUG = True
-        #writer.fitSecCoolList(solObjs)
-        solObjs = [ThermogenVP1869()]#,Therminol72()]
-        solObjs[0].viscosity.DEBUG=True
-        #solObjs[0].saturation_pressure.DEBUG=True
-        #
-        ##from CPIncomp.ExampleObjects import SecCoolExample
-        ##solObjs = [SecCoolExample()]
-        #writer.fitFluidList(solObjs)
+        from CPIncomp.PureFluids import PMR
+        #from CPIncomp.PureFluids import Texatherm22
+        #solObjs += [SecCoolSolutionData(sFile='Melinder, Ethanol'            ,sFolder='xMass',name='MEA2',desc='Melinder, Ethanol'            ,ref='Melinder2010,Skovrup2013')]
+        #solObjs += [SecCoolSolutionData(sFile='Melinder, Ammonia'            ,sFolder='xMass',name='MAM2',desc='Melinder, Ammonia'            ,ref='Melinder2010,Skovrup2013')]
+
+        solObjs += [PMR()]
+
         writer.fitSecCoolList(solObjs)
         writer.writeFluidList(solObjs)
         writer.writeReportList(solObjs)

dev/incompressible_liquids/json/AEG.json

@@ -82,7 +82,7 @@
       [
         -9.073173e-07, 
         4.118599e-06, 
-        -4.391163e-06, 
+        -4.391164e-06, 
         0.000000e+00, 
         0.000000e+00, 
         0.000000e+00
@@ -109,7 +109,7 @@
     "type": "notdefined"
   }, 
   "name": "AEG", 
-  "reference": "ASHRAE Fundamentals Handbook 2001, SecCool software", 
+  "reference": "ASHRAE2001,Skovrup2013", 
   "saturation_pressure": {
     "coeffs": "null", 
     "type": "notdefined"

dev/incompressible_liquids/json/AKF.json

@@ -133,7 +133,7 @@
     "type": "notdefined"
   }, 
   "name": "AKF", 
-  "reference": "Clariant GmbH Jan. 2000, SecCool software", 
+  "reference": "Clariant2000,Skovrup2013", 
   "saturation_pressure": {
     "coeffs": "null", 
     "type": "notdefined"

dev/incompressible_liquids/json/AL.json

@@ -133,7 +133,7 @@
     "type": "notdefined"
   }, 
   "name": "AL", 
-  "reference": "Clariant GmbH Jan. 2000, SecCool software", 
+  "reference": "Clariant2000,Skovrup2013", 
   "saturation_pressure": {
     "coeffs": "null", 
     "type": "notdefined"

dev/incompressible_liquids/json/AN.json

@@ -133,7 +133,7 @@
     "type": "notdefined"
   }, 
   "name": "AN", 
-  "reference": "Clariant GmbH Jan. 2000, SecCool software", 
+  "reference": "Clariant2000,Skovrup2013", 
   "saturation_pressure": {
     "coeffs": "null", 
     "type": "notdefined"

dev/incompressible_liquids/json/APG.json

@@ -80,7 +80,7 @@
         0.000000e+00
       ], 
       [
-        3.270564e-08, 
+        3.270563e-08, 
         -2.261472e-07, 
         3.368862e-07, 
         0.000000e+00, 
@@ -109,7 +109,7 @@
     "type": "notdefined"
   }, 
   "name": "APG", 
-  "reference": "ASHRAE Fundamentals Handbook 2001, SecCool software", 
+  "reference": "ASHRAE2001,Skovrup2013", 
   "saturation_pressure": {
     "coeffs": "null", 
     "type": "notdefined"

dev/incompressible_liquids/json/AS10.json

@@ -45,7 +45,7 @@
     "type": "notdefined"
   }, 
   "name": "AS10", 
-  "reference": "Aspen Petroleum AB, SecCool software", 
+  "reference": "Aspen2001,Skovrup2013", 
   "saturation_pressure": {
     "coeffs": "null", 
     "type": "notdefined"

dev/incompressible_liquids/json/AS20.json

@@ -45,7 +45,7 @@
     "type": "notdefined"
   }, 
   "name": "AS20", 
-  "reference": "Aspen Petroleum AB, SecCool software", 
+  "reference": "Aspen2001,Skovrup2013", 
   "saturation_pressure": {
     "coeffs": "null", 
     "type": "notdefined"

dev/incompressible_liquids/json/AS30.json

@@ -45,7 +45,7 @@
     "type": "notdefined"
   }, 
   "name": "AS30", 
-  "reference": "Aspen Petroleum AB, SecCool software", 
+  "reference": "Aspen2001,Skovrup2013", 
   "saturation_pressure": {
     "coeffs": "null", 
     "type": "notdefined"

dev/incompressible_liquids/json/AS40.json

@@ -45,7 +45,7 @@
     "type": "notdefined"
   }, 
   "name": "AS40", 
-  "reference": "Aspen Petroleum AB, SecCool software", 
+  "reference": "Aspen2001,Skovrup2013", 
   "saturation_pressure": {
     "coeffs": "null", 
     "type": "notdefined"

dev/incompressible_liquids/json/AS55.json

@@ -48,7 +48,7 @@
     "type": "notdefined"
   }, 
   "name": "AS55", 
-  "reference": "Aspen Petroleum AB, SecCool software", 
+  "reference": "Aspen2001,Skovrup2013", 
   "saturation_pressure": {
     "coeffs": "null", 
     "type": "notdefined"

dev/incompressible_liquids/json/DEB.json

@@ -39,7 +39,7 @@
     "type": "notdefined"
   }, 
   "name": "DEB", 
-  "reference": "Melinder-BOOK-2010", 
+  "reference": "Melinder2010", 
   "saturation_pressure": {
     "coeffs": "null", 
     "type": "notdefined"

dev/incompressible_liquids/json/DowJ.json

@@ -51,7 +51,7 @@
     "type": "notdefined"
   }, 
   "name": "DowJ", 
-  "reference": "Dow Chemicals data sheet", 
+  "reference": "Dow1997", 
   "saturation_pressure": {
     "coeffs": [
       -2.573167e+02, 

dev/incompressible_liquids/json/DowJ2.json

@@ -51,7 +51,7 @@
     "type": "notdefined"
   }, 
   "name": "DowJ2", 
-  "reference": "Dow Chemicals, SecCool software", 
+  "reference": "Dow1997,Skovrup2013", 
   "saturation_pressure": {
     "coeffs": "null", 
     "type": "notdefined"

dev/incompressible_liquids/json/DowQ.json

@@ -51,7 +51,7 @@
     "type": "notdefined"
   }, 
   "name": "DowQ", 
-  "reference": "Dow Chemicals data sheet", 
+  "reference": "Dow1997", 
   "saturation_pressure": {
     "coeffs": [
       -3.242462e+02, 

dev/incompressible_liquids/json/DowQ2.json

@@ -51,7 +51,7 @@
     "type": "notdefined"
   }, 
   "name": "DowQ2", 
-  "reference": "Dow Chemicals, SecCool software", 
+  "reference": "Dow1997,Skovrup2013", 
   "saturation_pressure": {
     "coeffs": "null", 
     "type": "notdefined"

dev/incompressible_liquids/json/ExampleDigital.json

@@ -76,8 +76,8 @@
       ], 
       [
         -4.117538e-10, 
-        3.193137e-09, 
-        -3.138206e-09, 
+        3.193145e-09, 
+        -3.138209e-09, 
         0.000000e+00, 
         0.000000e+00, 
         0.000000e+00
@@ -160,9 +160,9 @@
         0.000000e+00
       ], 
       [
-        -1.548862e-10, 
-        8.615137e-10, 
-        -8.067025e-11, 
+        -1.548841e-10, 
+        8.615147e-10, 
+        -8.067086e-11, 
         0.000000e+00, 
         0.000000e+00, 
         0.000000e+00

dev/incompressible_liquids/json/ExampleMelinder.json

@@ -100,7 +100,7 @@
     "type": "notdefined"
   }, 
   "name": "ExampleMelinder", 
-  "reference": "Melinder Book", 
+  "reference": "Melinder2010", 
   "saturation_pressure": {
     "coeffs": "null", 
     "type": "notdefined"

dev/incompressible_liquids/json/ExampleSolution.json

@@ -95,7 +95,7 @@
     "type": "notdefined"
   }, 
   "name": "ExampleSolution", 
-  "reference": "SecCool software", 
+  "reference": "SecCool software,Skovrup2013", 
   "saturation_pressure": {
     "coeffs": [
       [

dev/incompressible_liquids/json/FRE.json

@@ -54,7 +54,7 @@
     "type": "notdefined"
   }, 
   "name": "FRE", 
-  "reference": "Kemira Chemicals OY, SecCool software", 
+  "reference": "Kemira1998,Skovrup2013", 
   "saturation_pressure": {
     "coeffs": "null", 
     "type": "notdefined"
@@ -86,9 +86,9 @@
         0.000000e+00
       ], 
       [
-        -6.379647e-08, 
+        -6.379657e-08, 
         3.363858e-07, 
-        -3.132405e-07, 
+        -3.132406e-07, 
         0.000000e+00, 
         0.000000e+00, 
         0.000000e+00
@@ -124,8 +124,8 @@
       ], 
       [
         -2.058397e-06, 
-        -3.348859e-10, 
-        5.467784e-10, 
+        -3.348864e-10, 
+        5.467785e-10, 
         0.000000e+00, 
         0.000000e+00, 
         0.000000e+00

dev/incompressible_liquids/json/GKN.json

@@ -45,7 +45,7 @@
       [
         1.059366e-09, 
         -2.212375e-09, 
-        2.522431e-10, 
+        2.522430e-10, 
         0.000000e+00, 
         0.000000e+00, 
         0.000000e+00
@@ -109,7 +109,7 @@
     "type": "notdefined"
   }, 
   "name": "GKN", 
-  "reference": "pro KUEHLSOLE GmbH, SecCool software", 
+  "reference": "PKS2005,Skovrup2013", 
   "saturation_pressure": {
     "coeffs": "null", 
     "type": "notdefined"

dev/incompressible_liquids/json/HC10.json

@@ -51,7 +51,7 @@
     "type": "notdefined"
   }, 
   "name": "HC10", 
-  "reference": "Dynalene data sheet", 
+  "reference": "Dynalene2014", 
   "saturation_pressure": {
     "coeffs": [
       [

dev/incompressible_liquids/json/HC20.json

@@ -16,7 +16,7 @@
         1.000000e-03
       ], 
       [
-        4.945495e-17
+        4.945410e-17
       ], 
       [
         4.446923e-20
@@ -51,7 +51,7 @@
     "type": "notdefined"
   }, 
   "name": "HC20", 
-  "reference": "Dynalene data sheet", 
+  "reference": "Dynalene2014", 
   "saturation_pressure": {
     "coeffs": [
       [

dev/incompressible_liquids/json/HC30.json

@@ -16,7 +16,7 @@
         1.000000e-03
       ], 
       [
-        3.613874e-17
+        3.613676e-17
       ], 
       [
         3.832824e-20
@@ -51,7 +51,7 @@
     "type": "notdefined"
   }, 
   "name": "HC30", 
-  "reference": "Dynalene data sheet", 
+  "reference": "Dynalene2014", 
   "saturation_pressure": {
     "coeffs": [
       [

dev/incompressible_liquids/json/HC40.json

@@ -16,7 +16,7 @@
         1.000000e-03
       ], 
       [
-        9.106767e-17
+        9.107021e-17
       ], 
       [
         1.979940e-20
@@ -51,7 +51,7 @@
     "type": "notdefined"
   }, 
   "name": "HC40", 
-  "reference": "Dynalene data sheet", 
+  "reference": "Dynalene2014", 
   "saturation_pressure": {
     "coeffs": [
       [

dev/incompressible_liquids/json/HC50.json

@@ -51,7 +51,7 @@
     "type": "notdefined"
   }, 
   "name": "HC50", 
-  "reference": "Dynalene data sheet", 
+  "reference": "Dynalene2014", 
   "saturation_pressure": {
     "coeffs": [
       [

dev/incompressible_liquids/json/HCB.json

@@ -39,7 +39,7 @@
     "type": "notdefined"
   }, 
   "name": "HCB", 
-  "reference": "Melinder-BOOK-2010", 
+  "reference": "Melinder2010", 
   "saturation_pressure": {
     "coeffs": "null", 
     "type": "notdefined"

dev/incompressible_liquids/json/HCM.json

@@ -39,7 +39,7 @@
     "type": "notdefined"
   }, 
   "name": "HCM", 
-  "reference": "Melinder-BOOK-2010", 
+  "reference": "Melinder2010", 
   "saturation_pressure": {
     "coeffs": "null", 
     "type": "notdefined"

dev/incompressible_liquids/json/HFE.json

@@ -39,7 +39,7 @@
     "type": "notdefined"
   }, 
   "name": "HFE", 
-  "reference": "Melinder-BOOK-2010", 
+  "reference": "Melinder2010", 
   "saturation_pressure": {
     "coeffs": "null", 
     "type": "notdefined"

dev/incompressible_liquids/json/HFE2.json

@@ -33,7 +33,7 @@
         -2.269000e+00
       ], 
       [
-        -1.492754e-13
+        -1.493085e-13
       ], 
       [
         1.637145e-16
@@ -51,7 +51,7 @@
     "type": "notdefined"
   }, 
   "name": "HFE2", 
-  "reference": "3M Novec, SecCool software", 
+  "reference": "3M2007,Skovrup2013", 
   "saturation_pressure": {
     "coeffs": "null", 
     "type": "notdefined"
@@ -65,7 +65,7 @@
         2.000000e+00
       ], 
       [
-        1.348892e-13
+        1.348812e-13
       ], 
       [
         -2.528359e-16

dev/incompressible_liquids/json/HY20.json

@@ -45,7 +45,7 @@
     "type": "notdefined"
   }, 
   "name": "HY20", 
-  "reference": "Hydro Chemicals", 
+  "reference": "Hydro2000", 
   "saturation_pressure": {
     "coeffs": [
       -5.000000e+03, 

dev/incompressible_liquids/json/HY30.json

@@ -35,7 +35,7 @@
     ], 
     "type": "polynomial"
   }, 
-  "description": "HYCOOL 30, Potassium formate", 
+  "description": "HyCool 30, Potassium formate", 
   "mass2input": {
     "coeffs": "null", 
     "type": "notdefined"
@@ -45,7 +45,7 @@
     "type": "notdefined"
   }, 
   "name": "HY30", 
-  "reference": "Hydro Chemicals", 
+  "reference": "Hydro2000", 
   "saturation_pressure": {
     "coeffs": [
       -5.000000e+03, 

dev/incompressible_liquids/json/HY40.json

@@ -29,7 +29,7 @@
     ], 
     "type": "polynomial"
   }, 
-  "description": "HYCOOL 40, Potassium formate", 
+  "description": "HyCool 40, Potassium formate", 
   "mass2input": {
     "coeffs": "null", 
     "type": "notdefined"
@@ -39,7 +39,7 @@
     "type": "notdefined"
   }, 
   "name": "HY40", 
-  "reference": "Hydro Chemicals", 
+  "reference": "Hydro2000", 
   "saturation_pressure": {
     "coeffs": [
       -5.000000e+03, 

dev/incompressible_liquids/json/HY45.json

@@ -29,7 +29,7 @@
     ], 
     "type": "polynomial"
   }, 
-  "description": "HYCOOL 45, Potassium formate", 
+  "description": "HyCool 45, Potassium formate", 
   "mass2input": {
     "coeffs": "null", 
     "type": "notdefined"
@@ -39,7 +39,7 @@
     "type": "notdefined"
   }, 
   "name": "HY45", 
-  "reference": "Hydro Chemicals", 
+  "reference": "Hydro2000", 
   "saturation_pressure": {
     "coeffs": [
       -5.000000e+03, 

dev/incompressible_liquids/json/HY50.json

@@ -29,7 +29,7 @@
     ], 
     "type": "polynomial"
   }, 
-  "description": "HYCOOL 50, Potassium formate", 
+  "description": "HyCool 50, Potassium formate", 
   "mass2input": {
     "coeffs": "null", 
     "type": "notdefined"
@@ -39,7 +39,7 @@
     "type": "notdefined"
   }, 
   "name": "HY50", 
-  "reference": "Hydro Chemicals", 
+  "reference": "Hydro2000", 
   "saturation_pressure": {
     "coeffs": [
       -5.000000e+03, 

dev/incompressible_liquids/json/IceEA.json

@@ -91,7 +91,7 @@
     "type": "notdefined"
   }, 
   "name": "IceEA", 
-  "reference": "Danish Technological Institute, SecCool software", 
+  "reference": "Kauffeld2001,Skovrup2013", 
   "saturation_pressure": {
     "coeffs": "null", 
     "type": "notdefined"

dev/incompressible_liquids/json/IceNA.json

@@ -91,7 +91,7 @@
     "type": "notdefined"
   }, 
   "name": "IceNA", 
-  "reference": "Danish Technological Institute, SecCool software", 
+  "reference": "Kauffeld2001,Skovrup2013", 
   "saturation_pressure": {
     "coeffs": "null", 
     "type": "notdefined"

dev/incompressible_liquids/json/IcePG.json

@@ -91,7 +91,7 @@
     "type": "notdefined"
   }, 
   "name": "IcePG", 
-  "reference": "Danish Technological Institute, SecCool software", 
+  "reference": "Kauffeld2001,Skovrup2013", 
   "saturation_pressure": {
     "coeffs": "null", 
     "type": "notdefined"

dev/incompressible_liquids/json/LiBr.json

@@ -85,7 +85,7 @@
     ], 
     "type": "polynomial"
   }, 
-  "description": "Lithium-Bromide solution from Patek2006", 
+  "description": "Aqueous lithium-bromide solution ", 
   "mass2input": {
     "coeffs": "null", 
     "type": "notdefined"
@@ -160,7 +160,7 @@
         0.000000e+00
       ], 
       [
-        3.074006e-05, 
+        3.074005e-05, 
         4.644860e-04, 
         -4.128601e-04, 
         0.000000e+00, 

dev/incompressible_liquids/json/MAM.json

@@ -100,7 +100,7 @@
     "type": "notdefined"
   }, 
   "name": "MAM", 
-  "reference": "Melinder Book", 
+  "reference": "Melinder2010", 
   "saturation_pressure": {
     "coeffs": "null", 
     "type": "notdefined"

dev/incompressible_liquids/json/MAM2.json

@@ -1,11 +1,13 @@
 {
   "T_freeze": {
     "coeffs": [
-      9.292564e+05, 
-      6.000000e+04, 
-      1.000000e+01
+      [
+        5.582814e+00, 
+        1.512284e-01, 
+        -3.735343e+00
+      ]
     ], 
-    "type": "exponential"
+    "type": "exppolynomial"
   }, 
   "Tbase": 0.000000e+00, 
   "Tmax": 2.931500e+02, 
@@ -87,7 +89,7 @@
     "type": "notdefined"
   }, 
   "name": "MAM2", 
-  "reference": "Melinder-BOOK-2010, SecCool software", 
+  "reference": "Melinder2010,Skovrup2013", 
   "saturation_pressure": {
     "coeffs": "null", 
     "type": "notdefined"

dev/incompressible_liquids/json/MCA.json

@@ -100,7 +100,7 @@
     "type": "notdefined"
   }, 
   "name": "MCA", 
-  "reference": "Melinder Book", 
+  "reference": "Melinder2010", 
   "saturation_pressure": {
     "coeffs": "null", 
     "type": "notdefined"

dev/incompressible_liquids/json/MCA2.json

@@ -1,11 +1,14 @@
 {
   "T_freeze": {
     "coeffs": [
-      9.308955e+05, 
-      6.000000e+04, 
-      1.000000e+01
+      [
+        5.653313e+00, 
+        -1.078524e+00, 
+        5.849055e+00, 
+        -1.617841e+01
+      ]
     ], 
-    "type": "exponential"
+    "type": "exppolynomial"
   }, 
   "Tbase": 0.000000e+00, 
   "Tmax": 3.031500e+02, 
@@ -95,7 +98,7 @@
     "type": "notdefined"
   }, 
   "name": "MCA2", 
-  "reference": "Melinder-BOOK-2010, SecCool software", 
+  "reference": "Melinder2010,Skovrup2013", 
   "saturation_pressure": {
     "coeffs": "null", 
     "type": "notdefined"

dev/incompressible_liquids/json/MEA.json

@@ -100,7 +100,7 @@
     "type": "notdefined"
   }, 
   "name": "MEA", 
-  "reference": "Melinder Book", 
+  "reference": "Melinder2010", 
   "saturation_pressure": {
     "coeffs": "null", 
     "type": "notdefined"

dev/incompressible_liquids/json/MEA2.json

@@ -80,7 +80,7 @@
         0.000000e+00
       ], 
       [
-        -1.261406e-06, 
+        -1.261407e-06, 
         1.595687e-04, 
         -2.874999e-04, 
         0.000000e+00, 
@@ -100,7 +100,7 @@
     "type": "notdefined"
   }, 
   "name": "MEA2", 
-  "reference": "Melinder-BOOK-2010, SecCool software", 
+  "reference": "Melinder2010,Skovrup2013", 
   "saturation_pressure": {
     "coeffs": "null", 
     "type": "notdefined"

dev/incompressible_liquids/json/MEG.json

@@ -100,7 +100,7 @@
     "type": "notdefined"
   }, 
   "name": "MEG", 
-  "reference": "Melinder Book", 
+  "reference": "Melinder2010", 
   "saturation_pressure": {
     "coeffs": "null", 
     "type": "notdefined"

dev/incompressible_liquids/json/MEG2.json

@@ -100,7 +100,7 @@
     "type": "notdefined"
   }, 
   "name": "MEG2", 
-  "reference": "Melinder-BOOK-2010, SecCool software", 
+  "reference": "Melinder2010,Skovrup2013", 
   "saturation_pressure": {
     "coeffs": "null", 
     "type": "notdefined"

dev/incompressible_liquids/json/MGL.json

@@ -100,7 +100,7 @@
     "type": "notdefined"
   }, 
   "name": "MGL", 
-  "reference": "Melinder Book", 
+  "reference": "Melinder2010", 
   "saturation_pressure": {
     "coeffs": "null", 
     "type": "notdefined"

dev/incompressible_liquids/json/MGL2.json

@@ -1,11 +1,16 @@
 {
   "T_freeze": {
     "coeffs": [
-      9.321636e+05, 
-      6.000000e+04, 
-      1.000000e+01
+      [
+        5.643904e+00, 
+        -5.983132e-01, 
+        3.208226e+00, 
+        -1.038046e+01, 
+        1.531831e+01, 
+        -9.146761e+00
+      ]
     ], 
-    "type": "exponential"
+    "type": "exppolynomial"
   }, 
   "Tbase": 0.000000e+00, 
   "Tmax": 3.131500e+02, 
@@ -95,7 +100,7 @@
     "type": "notdefined"
   }, 
   "name": "MGL2", 
-  "reference": "Melinder-BOOK-2010, SecCool software", 
+  "reference": "Melinder2010,Skovrup2013", 
   "saturation_pressure": {
     "coeffs": "null", 
     "type": "notdefined"

dev/incompressible_liquids/json/MKA.json

@@ -100,7 +100,7 @@
     "type": "notdefined"
   }, 
   "name": "MKA", 
-  "reference": "Melinder Book", 
+  "reference": "Melinder2010", 
   "saturation_pressure": {
     "coeffs": "null", 
     "type": "notdefined"

dev/incompressible_liquids/json/MKA2.json

@@ -1,11 +1,15 @@
 {
   "T_freeze": {
     "coeffs": [
-      9.317439e+05, 
-      6.000000e+04, 
-      1.000000e+01
+      [
+        5.643432e+00, 
+        -8.156028e-01, 
+        4.484583e+00, 
+        -1.364947e+01, 
+        1.090680e+01
+      ]
     ], 
-    "type": "exponential"
+    "type": "exppolynomial"
   }, 
   "Tbase": 0.000000e+00, 
   "Tmax": 3.031500e+02, 
@@ -95,7 +99,7 @@
     "type": "notdefined"
   }, 
   "name": "MKA2", 
-  "reference": "Melinder-BOOK-2010, SecCool software", 
+  "reference": "Melinder2010,Skovrup2013", 
   "saturation_pressure": {
     "coeffs": "null", 
     "type": "notdefined"

dev/incompressible_liquids/json/MKC.json

@@ -100,7 +100,7 @@
     "type": "notdefined"
   }, 
   "name": "MKC", 
-  "reference": "Melinder Book", 
+  "reference": "Melinder2010", 
   "saturation_pressure": {
     "coeffs": "null", 
     "type": "notdefined"

dev/incompressible_liquids/json/MKC2.json

@@ -1,11 +1,16 @@
 {
   "T_freeze": {
     "coeffs": [
-      9.337457e+05, 
-      6.000000e+04, 
-      1.000000e+01
+      [
+        5.610043e+00, 
+        1.018931e-01, 
+        -3.926708e+00, 
+        2.335889e+01, 
+        -6.343317e+01, 
+        5.565994e+01
+      ]
     ], 
-    "type": "exponential"
+    "type": "exppolynomial"
   }, 
   "Tbase": 0.000000e+00, 
   "Tmax": 3.031500e+02, 
@@ -95,7 +100,7 @@
     "type": "notdefined"
   }, 
   "name": "MKC2", 
-  "reference": "Melinder-BOOK-2010, SecCool software", 
+  "reference": "Melinder2010,Skovrup2013", 
   "saturation_pressure": {
     "coeffs": "null", 
     "type": "notdefined"

dev/incompressible_liquids/json/MKF.json

@@ -100,7 +100,7 @@
     "type": "notdefined"
   }, 
   "name": "MKF", 
-  "reference": "Melinder Book", 
+  "reference": "Melinder2010", 
   "saturation_pressure": {
     "coeffs": "null", 
     "type": "notdefined"

dev/incompressible_liquids/json/MLI.json

@@ -100,7 +100,7 @@
     "type": "notdefined"
   }, 
   "name": "MLI", 
-  "reference": "Melinder Book", 
+  "reference": "Melinder2010", 
   "saturation_pressure": {
     "coeffs": "null", 
     "type": "notdefined"

dev/incompressible_liquids/json/MMA.json

@@ -100,7 +100,7 @@
     "type": "notdefined"
   }, 
   "name": "MMA", 
-  "reference": "Melinder Book", 
+  "reference": "Melinder2010", 
   "saturation_pressure": {
     "coeffs": "null", 
     "type": "notdefined"

dev/incompressible_liquids/json/MMA2.json

@@ -1,11 +1,16 @@
 {
   "T_freeze": {
     "coeffs": [
-      9.307502e+05, 
-      6.000000e+04, 
-      1.000000e+01
+      [
+        5.612846e+00, 
+        -2.775615e-01, 
+        1.946035e-01, 
+        -2.014309e+00, 
+        3.148512e+00, 
+        -2.580098e+00
+      ]
     ], 
-    "type": "exponential"
+    "type": "exppolynomial"
   }, 
   "Tbase": 0.000000e+00, 
   "Tmax": 2.931500e+02, 
@@ -95,7 +100,7 @@
     "type": "notdefined"
   }, 
   "name": "MMA2", 
-  "reference": "Melinder-BOOK-2010, SecCool software", 
+  "reference": "Melinder2010,Skovrup2013", 
   "saturation_pressure": {
     "coeffs": "null", 
     "type": "notdefined"

dev/incompressible_liquids/json/MMG.json

@@ -100,7 +100,7 @@
     "type": "notdefined"
   }, 
   "name": "MMG", 
-  "reference": "Melinder Book", 
+  "reference": "Melinder2010", 
   "saturation_pressure": {
     "coeffs": "null", 
     "type": "notdefined"

dev/incompressible_liquids/json/MMG2.json

@@ -1,11 +1,14 @@
 {
   "T_freeze": {
     "coeffs": [
-      9.332731e+05, 
-      6.000000e+04, 
-      1.000000e+01
+      [
+        5.609983e+00, 
+        -1.321822e-01, 
+        -1.458488e+00, 
+        -3.263464e+00
+      ]
     ], 
-    "type": "exponential"
+    "type": "exppolynomial"
   }, 
   "Tbase": 0.000000e+00, 
   "Tmax": 3.031500e+02, 
@@ -38,9 +41,9 @@
         0.000000e+00
       ], 
       [
-        -3.201909e-14, 
-        -8.491526e-14, 
-        1.265223e-12, 
+        -3.116864e-14, 
+        -8.496811e-14, 
+        1.264271e-12, 
         0.000000e+00, 
         0.000000e+00, 
         0.000000e+00
@@ -75,9 +78,9 @@
         0.000000e+00
       ], 
       [
-        1.986411e-11, 
-        -3.429773e-10, 
-        7.354027e-10, 
+        1.984946e-11, 
+        -3.372422e-10, 
+        7.093277e-10, 
         0.000000e+00, 
         0.000000e+00, 
         0.000000e+00
@@ -95,7 +98,7 @@
     "type": "notdefined"
   }, 
   "name": "MMG2", 
-  "reference": "Melinder-BOOK-2010, SecCool software", 
+  "reference": "Melinder2010,Skovrup2013", 
   "saturation_pressure": {
     "coeffs": "null", 
     "type": "notdefined"
@@ -127,9 +130,9 @@
         0.000000e+00
       ], 
       [
-        5.248779e-11, 
-        -7.030447e-10, 
-        1.861376e-09, 
+        5.482548e-11, 
+        -6.893357e-10, 
+        1.956098e-09, 
         0.000000e+00, 
         0.000000e+00, 
         0.000000e+00
@@ -164,9 +167,9 @@
         0.000000e+00
       ], 
       [
-        -1.262879e-15, 
-        -4.101099e-13, 
-        1.456601e-12, 
+        -9.388323e-15, 
+        -4.140820e-13, 
+        1.454213e-12, 
         0.000000e+00, 
         0.000000e+00, 
         0.000000e+00

dev/incompressible_liquids/json/MNA.json

@@ -100,7 +100,7 @@
     "type": "notdefined"
   }, 
   "name": "MNA", 
-  "reference": "Melinder Book", 
+  "reference": "Melinder2010", 
   "saturation_pressure": {
     "coeffs": "null", 
     "type": "notdefined"

dev/incompressible_liquids/json/MNA2.json

@@ -39,8 +39,8 @@
       ], 
       [
         5.963914e-14, 
-        -1.171232e-12, 
-        3.544925e-12, 
+        -1.171287e-12, 
+        3.544939e-12, 
         0.000000e+00, 
         0.000000e+00, 
         0.000000e+00
@@ -75,9 +75,9 @@
         0.000000e+00
       ], 
       [
-        2.382838e-11, 
-        -4.371017e-10, 
-        2.078221e-09, 
+        2.382339e-11, 
+        -4.369353e-10, 
+        2.078946e-09, 
         0.000000e+00, 
         0.000000e+00, 
         0.000000e+00
@@ -95,7 +95,7 @@
     "type": "notdefined"
   }, 
   "name": "MNA2", 
-  "reference": "Melinder-BOOK-2010, SecCool software", 
+  "reference": "Melinder2010,Skovrup2013", 
   "saturation_pressure": {
     "coeffs": "null", 
     "type": "notdefined"
@@ -129,7 +129,7 @@
       [
         -7.407941e-11, 
         1.506720e-09, 
-        -5.254342e-09, 
+        -5.256161e-09, 
         0.000000e+00, 
         0.000000e+00, 
         0.000000e+00
@@ -164,9 +164,9 @@
         0.000000e+00
       ], 
       [
-        1.144571e-14, 
-        -5.037253e-13, 
-        2.031073e-12, 
+        1.141968e-14, 
+        -5.033025e-13, 
+        2.031147e-12, 
         0.000000e+00, 
         0.000000e+00, 
         0.000000e+00

dev/incompressible_liquids/json/MPG.json

@@ -100,7 +100,7 @@
     "type": "notdefined"
   }, 
   "name": "MPG", 
-  "reference": "Melinder Book", 
+  "reference": "Melinder2010", 
   "saturation_pressure": {
     "coeffs": "null", 
     "type": "notdefined"

dev/incompressible_liquids/json/MPG2.json

@@ -1,11 +1,16 @@
 {
   "T_freeze": {
     "coeffs": [
-      9.314963e+05, 
-      6.000000e+04, 
-      1.000000e+01
+      [
+        5.551677e+00, 
+        9.351475e-01, 
+        -6.934728e+00, 
+        2.055368e+01, 
+        -2.940299e+01, 
+        1.483940e+01
+      ]
     ], 
-    "type": "exponential"
+    "type": "exppolynomial"
   }, 
   "Tbase": 0.000000e+00, 
   "Tmax": 3.131500e+02, 
@@ -95,7 +100,7 @@
     "type": "notdefined"
   }, 
   "name": "MPG2", 
-  "reference": "Melinder-BOOK-2010, SecCool software", 
+  "reference": "Melinder2010,Skovrup2013", 
   "saturation_pressure": {
     "coeffs": "null", 
     "type": "notdefined"

dev/incompressible_liquids/json/NBS.json

@@ -51,7 +51,7 @@
     "type": "notdefined"
   }, 
   "name": "NBS", 
-  "reference": "Properties of Water and Steam in SI-Units, 2nd Revised and Updated Printing, Springer 1979, pp. 175 ff., SecCool software", 
+  "reference": "Schmidt1979,Skovrup2013", 
   "saturation_pressure": {
     "coeffs": "null", 
     "type": "notdefined"

dev/incompressible_liquids/json/NaK.json

@@ -29,7 +29,7 @@
     ], 
     "type": "polynomial"
   }, 
-  "description": "NitrateSalt", 
+  "description": "Nitrate salt, heat transfer fluid based on 60% NaNO3 and 40% KNO3", 
   "mass2input": {
     "coeffs": "null", 
     "type": "notdefined"
@@ -39,7 +39,7 @@
     "type": "notdefined"
   }, 
   "name": "NaK", 
-  "reference": "Solar Power Tower Design Basis Document,  Alexis B. Zavoico, Sandia Labs, USA", 
+  "reference": "Zavoico2001", 
   "saturation_pressure": {
     "coeffs": "null", 
     "type": "notdefined"

dev/incompressible_liquids/json/PCL.json

@@ -51,7 +51,7 @@
     "type": "notdefined"
   }, 
   "name": "PCL", 
-  "reference": "Sulzer Chemtech AG, SecCool software", 
+  "reference": "Sulzer1999,Skovrup2013", 
   "saturation_pressure": {
     "coeffs": "null", 
     "type": "notdefined"

dev/incompressible_liquids/json/PCR.json

@@ -0,0 +1,96 @@
+{
+  "T_freeze": {
+    "coeffs": "null", 
+    "type": "notdefined"
+  }, 
+  "Tbase": 0.000000e+00, 
+  "Tmax": 4.931500e+02, 
+  "Tmin": 1.731500e+02, 
+  "TminPsat": 3.581500e+02, 
+  "conductivity": {
+    "coeffs": [
+      [
+        1.631059e-01
+      ], 
+      [
+        -7.723938e-05
+      ], 
+      [
+        -9.279440e-09
+      ], 
+      [
+        9.384425e-12
+      ]
+    ], 
+    "type": "polynomial"
+  }, 
+  "density": {
+    "coeffs": [
+      [
+        1.122801e+03
+      ], 
+      [
+        -9.963599e-01
+      ], 
+      [
+        -4.931404e-05
+      ], 
+      [
+        6.240201e-08
+      ]
+    ], 
+    "type": "polynomial"
+  }, 
+  "description": "Paratherm CR", 
+  "mass2input": {
+    "coeffs": "null", 
+    "type": "notdefined"
+  }, 
+  "mole2input": {
+    "coeffs": "null", 
+    "type": "notdefined"
+  }, 
+  "name": "PCR", 
+  "reference": "Paratherm2013", 
+  "saturation_pressure": {
+    "coeffs": [
+      -3.500387e+02, 
+      -1.263655e+00, 
+      5.633270e+00
+    ], 
+    "type": "logexponential"
+  }, 
+  "specific_heat": {
+    "coeffs": [
+      [
+        7.239249e+02
+      ], 
+      [
+        4.715865e+00
+      ], 
+      [
+        -3.019390e-03
+      ], 
+      [
+        2.864835e-06
+      ]
+    ], 
+    "type": "polynomial"
+  }, 
+  "viscosity": {
+    "coeffs": [
+      5.082283e+02, 
+      -9.138133e+01, 
+      1.654139e+01
+    ], 
+    "type": "exponential"
+  }, 
+  "volume2input": {
+    "coeffs": "null", 
+    "type": "notdefined"
+  }, 
+  "xbase": 0.000000e+00, 
+  "xid": "pure", 
+  "xmax": 1.000000e+00, 
+  "xmin": 0.000000e+00
+}