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Fitting works, some tests do not pass.

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jowr
2014-07-17 11:52:12 +02:00
parent e946d84dec
commit 2bb3e182e5
9 changed files with 1101 additions and 435 deletions
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12
dev/incompressible_liquids/CPIncomp/BaseObjects.py
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@@ -27,7 +27,7 @@ class IncompressibleData(object):
self.maxLog = np.log(np.finfo(np.float64).max-1)
self.minLog = -self.maxLog
self.DEBUG = True
self.DEBUG = False
### Base functions that handle the custom data type, just a place holder to show the structure.
@@ -113,12 +113,12 @@ class IncompressibleData(object):
return (r,c,np.reshape(array,(r,c)))
def fit(self, x=0.0, y=0.0, xbase=0.0, ybase=0.0):
def fitCoeffs(self, x=0.0, y=0.0, xbase=0.0, ybase=0.0):
"""
A function that selects the correct equations and
fits coefficients. Some functions require a start
guess for the coefficients to work properly.
"""
"""
dr,dc,_ = self.shapeArray(self.data)
xr,xc,x = self.shapeArray(x)
yr,yc,y = self.shapeArray(y, axs=1)
@@ -126,6 +126,12 @@ class IncompressibleData(object):
if self.DEBUG: print("Data : ({0},{1})".format(dr,dc))
if self.DEBUG: print("x-axis : ({0},{1})".format(xr,xc))
if self.DEBUG: print("y-axis : ({0},{1})".format(yr,yc))
if dr==1 and dc==1: #
if self.DEBUG: print("Data no set, we cannot fit the coefficients")
self.coeffs = None
self.type = self.INCOMPRESSIBLE_NOT_SET
return
if (xc!=1): raise ValueError("The first input has to be a 2D array with one column.")
if (yr!=1): raise ValueError("The second input has to be a 2D array with one row.")

2
dev/incompressible_liquids/CPIncomp/CoefficientObjects.py
View File

@@ -243,7 +243,7 @@ class SecCoolExample(CoefficientData):
-22.973221700,
-1.1040507200*100.0,
-0.0120762281*100.0*100.0,
-9.343458E-05*100.0*100.0*100.0])
-9.343458E-05*100.0*100.0*100.0])
class MelinderExample(CoefficientData):

124
dev/incompressible_liquids/CPIncomp/DataObjects.py
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@@ -39,6 +39,7 @@ class SolutionData(object):
#self.viscosity.coeffs = np.array([+7e+2, -6e+1, +1e+1])
#self.saturation_pressure.type = self.saturation_pressure.INCOMPRESSIBLE_EXPONENTIAL
#self.saturation_pressure.coeffs = np.array([-5e+3, +3e+1, -1e+1])
self.xref = 0.0
self.Tref = 0.0
@@ -47,6 +48,38 @@ class SolutionData(object):
self.sref = 0.0
self.uref = 0.0
self.rhoref = 0.0
# def getDataObjects(self):
# objList = {}
# objList["temperature"] = self.temperature
# objList["concentration"] = self.concentration
# objList["density"] = self.density
# objList["specific_heat"] = self.specific_heat
# objList["viscosity"] = self.viscosity
# objList["conductivity"] = self.conductivity
# objList["saturation_pressure"] = self.saturation_pressure
# objList["T_freeze"] = self.T_freeze
# objList["volume2mass"] = self.volume2mass
# objList["mass2mole"] = self.mass2mole
# return objList
def getPolyObjects(self):
objList = {}
objList["density"] = self.density
objList["specific heat"] = self.specific_heat
# objList["viscosity"] = self.viscosity
objList["conductivity"] = self.conductivity
# objList["saturation_pressure"] = self.saturation_pressure
# objList["T_freeze"] = self.T_freeze
# objList["volume2mass"] = self.volume2mass
# objList["mass2mole"] = self.mass2mole
return objList
def getExpPolyObjects(self):
objList = {}
objList["viscosity"] = self.viscosity
objList["saturation pressure"] = self.saturation_pressure
return objList
def rho (self, T, p=0.0, x=0.0, c=None):
@@ -87,7 +120,7 @@ class SolutionData(object):
def cond(self, T, p=0.0, x=0.0, c=None):
return self.conductivity.baseFunction(T, x, self.Tbase, self.xbase, c=c)
def psat(self, T, x=0.0, c=None):
def psat(self, T, x=0.0, c=None):
return self.saturation_pressure.baseFunction(T, x, self.Tbase, self.xbase, c=c)
def Tfreeze(self, p=0.0, x=0.0, c=None):
@@ -112,11 +145,44 @@ class SolutionData(object):
self.sref = s0
self.href = self.h(T0,p0,x0)
self.sref = self.s(T0,p0,x0)
class PureExample(SolutionData):
class PureData(SolutionData):
"""
An extension of the solution data that makes it
easier to gather data for pure fluids.
"""
def __init__(self):
SolutionData.__init__(self)
SolutionData.__init__(self)
self.concentration.data = np.array([ 0 ]) # mass fraction
def reshapeData(self, dataArray, length):
"""
Makes any array 1-dimensional and implicitly
checks the length.
"""
if dataArray!=None:
return np.reshape(dataArray, (length,1))
else:
return None
def reshapeAll(self):
len_T = len(self.temperature.data)
#len_x = len(self.concentration.data)
self.density.data = self.reshapeData(self.density.data, len_T)
self.specific_heat.data = self.reshapeData(self.specific_heat.data, len_T)
self.viscosity.data = self.reshapeData(self.viscosity.data, len_T)
self.conductivity.data = self.reshapeData(self.conductivity.data, len_T)
self.saturation_pressure.data = self.reshapeData(self.saturation_pressure.data, len_T)
#self.T_freeze.data = self.reshapeData(self.T_freeze.data, len_T)
#self.volume2mass.data = self.reshapeData(self.volume2mass.data, len_T)
#self.mass2mole.data = self.reshapeData(self.mass2mole.data, len_T)
class PureExample(PureData):
def __init__(self):
PureData.__init__(self)
self.name = "ExamplePure"
self.description = "Heat transfer fluid TherminolD12 by Solutia"
self.reference = "Solutia data sheet"
@@ -124,13 +190,13 @@ class PureExample(SolutionData):
self.Tmin = 50 + 273.15
self.TminPsat = self.Tmax
self.temperature.data = np.array([ 50 , 60 , 70 , 80 , 90 , 100 , 110 , 120 , 130 , 140 , 150 ])+273.15 # Kelvin
self.concentration.data = np.array([ 0 ])/100.0 # mass fraction
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
self.viscosity.data = np.array([[0.804],[ 0.704],[ 0.623],[ 0.556],[ 0.498],[ 0.451],[ 0.410],[ 0.374],[ 0.346],[ 0.317],[ 0.289]]) # Pa-s
self.conductivity.data = np.array([[0.105],[ 0.104],[ 0.102],[ 0.100],[ 0.098],[ 0.096],[ 0.095],[ 0.093],[ 0.091],[ 0.089],[ 0.087]]) # W/m-K
self.saturation_pressure.data = np.array([[ 0.5],[ 0.9],[ 1.4],[ 2.3],[ 3.9],[ 6.0],[ 8.7],[ 12.4],[ 17.6],[ 24.4],[ 33.2]]) # Pa
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
self.viscosity.data = np.array([0.804, 0.704, 0.623, 0.556, 0.498, 0.451, 0.410, 0.374, 0.346, 0.317, 0.289]) # Pa-s
self.conductivity.data = np.array([0.105, 0.104, 0.102, 0.100, 0.098, 0.096, 0.095, 0.093, 0.091, 0.089, 0.087]) # W/m-K
self.saturation_pressure.data = np.array([ 0.5, 0.9, 1.4, 2.3, 3.9, 6.0, 8.7, 12.4, 17.6, 24.4, 33.2]) # Pa
self.reshapeAll()
class SolutionExample(SolutionData):
@@ -167,26 +233,40 @@ class SolutionExample(SolutionData):
# [np.nan, np.nan, 1009.2, np.nan, np.nan, np.nan, np.nan]]) # kg/m3
if __name__ == '__main__':
# obj = PureExample()
# obj.density.type = obj.density.INCOMPRESSIBLE_POLYNOMIAL
# obj.density.coeffs = np.ones((3,))
# print(obj.density.coeffs)
# obj.density.fit(obj.temperature.data)
# obj.density.fitCoeffs(obj.temperature.data)
# print(obj.density.coeffs)
# print(obj.density.data[2][0],obj.rho(obj.temperature.data[2]))
#
# obj = PureExample()
# print(obj.saturation_pressure.coeffs)
# obj.saturation_pressure.fit(obj.temperature.data)
# print(obj.saturation_pressure.coeffs)
obj = SolutionExample()
obj = PureExample()
obj.density.type = obj.density.INCOMPRESSIBLE_POLYNOMIAL
obj.density.coeffs = np.ones((3,5))
obj.density.coeffs = np.zeros((4,6))
print(obj.density.coeffs)
obj.density.fit(obj.temperature.data,obj.concentration.data)
obj.density.fitCoeffs(obj.temperature.data)
print(obj.density.coeffs)
obj.saturation_pressure.type = obj.density.INCOMPRESSIBLE_EXPPOLYNOMIAL
obj.saturation_pressure.coeffs = np.zeros((4,6))
print(obj.saturation_pressure.coeffs)
obj.saturation_pressure.fitCoeffs(obj.temperature.data)
print(obj.saturation_pressure.coeffs)
print(obj.density.data[2][0],obj.rho(obj.temperature.data[2],10e5,obj.concentration.data[0]))
print(obj.density.data[2][2],obj.rho(obj.temperature.data[2],10e5,obj.concentration.data[2]))
print(obj.density.data[5][0],obj.rho(obj.temperature.data[5],10e5,obj.concentration.data[0]))
print(obj.saturation_pressure.data[2][0],obj.psat(obj.temperature.data[2],obj.concentration.data[0]))
print(obj.saturation_pressure.data[5][0],obj.psat(obj.temperature.data[5],obj.concentration.data[0]))
# obj = SolutionExample()
# obj.density.type = obj.density.INCOMPRESSIBLE_POLYNOMIAL
# obj.density.coeffs = np.ones((3,5))
# print(obj.density.coeffs)
# obj.density.fitCoeffs(obj.temperature.data,obj.concentration.data)
# print(obj.density.coeffs)
# print(obj.density.data[2][0],obj.rho(obj.temperature.data[2],10e5,obj.concentration.data[0]))
# print(obj.density.data[2][2],obj.rho(obj.temperature.data[2],10e5,obj.concentration.data[2]))

523
dev/incompressible_liquids/CPIncomp/PureFluids.py Normal file
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@@ -0,0 +1,523 @@
import numpy as np
from CPIncomp.DataObjects import PureData
class TherminolD12(PureData):
"""
Heat transfer fluid Therminol D12 by Solutia
Source: Therminol Heat Transfer Reference Disk
"""
def __init__(self):
PureData.__init__(self)
self.temperature.data = np.array([-8.50000E+1, -8.00000E+1, -7.50000E+1, -7.00000E+1, -6.50000E+1, -6.00000E+1, -5.50000E+1, -5.00000E+1, -4.50000E+1, -4.00000E+1, -3.50000E+1, -3.00000E+1, -2.50000E+1, -2.00000E+1, -1.50000E+1, -1.00000E+1, -5.00000E+0, +0.00000E+0, +5.00000E+0, +1.00000E+1, +1.50000E+1, +2.00000E+1, +2.50000E+1, +3.00000E+1, +3.50000E+1, +4.00000E+1, +4.50000E+1, +5.00000E+1, +5.50000E+1, +6.00000E+1, +6.50000E+1, +7.00000E+1, +7.50000E+1, +8.00000E+1, +8.50000E+1, +9.00000E+1, +9.50000E+1, +1.00000E+2, +1.05000E+2, +1.10000E+2, +1.15000E+2, +1.20000E+2, +1.25000E+2, +1.30000E+2, +1.35000E+2, +1.40000E+2, +1.45000E+2, +1.50000E+2, +1.55000E+2, +1.60000E+2, +1.65000E+2, +1.70000E+2, +1.75000E+2, +1.80000E+2, +1.85000E+2, +1.90000E+2, +1.95000E+2, +2.00000E+2, +2.05000E+2, +2.10000E+2, +2.15000E+2, +2.20000E+2, +2.25000E+2, +2.30000E+2])+273.15 # Kelvin
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.Tmin = np.min(self.temperature.data)
self.Tmax = np.max(self.temperature.data)
self.TminPsat = self.Tmin
self.name = "TD12"
self.description = "TherminolD12"
self.reshapeAll()
class TherminolVP1(PureData):
"""
Heat transfer fluid Therminol VP-1 by Solutia
Source: Therminol Heat Transfer Reference Disk
"""
def __init__(self):
PureData.__init__(self)
self.temperature.data = np.array([+1.20000E+1, +1.70000E+1, +2.20000E+1, +2.70000E+1, +3.20000E+1, +3.70000E+1, +4.20000E+1, +4.70000E+1, +5.20000E+1, +5.70000E+1, +6.20000E+1, +6.70000E+1, +7.20000E+1, +7.70000E+1, +8.20000E+1, +8.70000E+1, +9.20000E+1, +9.70000E+1, +1.02000E+2, +1.07000E+2, +1.12000E+2, +1.17000E+2, +1.22000E+2, +1.27000E+2, +1.32000E+2, +1.37000E+2, +1.42000E+2, +1.47000E+2, +1.52000E+2, +1.57000E+2, +1.62000E+2, +1.67000E+2, +1.72000E+2, +1.77000E+2, +1.82000E+2, +1.87000E+2, +1.92000E+2, +1.97000E+2, +2.02000E+2, +2.07000E+2, +2.12000E+2, +2.17000E+2, +2.22000E+2, +2.27000E+2, +2.32000E+2, +2.37000E+2, +2.42000E+2, +2.47000E+2, +2.52000E+2, +2.57000E+2, +2.62000E+2, +2.67000E+2, +2.72000E+2, +2.77000E+2, +2.82000E+2, +2.87000E+2, +2.92000E+2, +2.97000E+2, +3.02000E+2, +3.07000E+2, +3.12000E+2, +3.17000E+2, +3.22000E+2, +3.27000E+2, +3.32000E+2, +3.37000E+2, +3.42000E+2, +3.47000E+2, +3.52000E+2, +3.57000E+2, +3.62000E+2, +3.67000E+2, +3.72000E+2, +3.77000E+2, +3.82000E+2, +3.87000E+2, +3.92000E+2, +3.97000E+2])+273.15 # Kelvin
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.Tmin = np.min(self.temperature.data)
self.Tmax = np.max(self.temperature.data)
self.TminPsat = self.Tmin
self.name = "TVP1"
self.description = "TherminolVP1"
self.reshapeAll()
class Therminol66(PureData):
"""
Heat transfer fluid Therminol 66 by Solutia
Source: Therminol Heat Transfer Reference Disk
"""
def __init__(self):
PureData.__init__(self)
temp = np.linspace(0, 380, 30) # Celsius temperaure
def f_rho( T_C):
return -0.614254 * T_C - 0.000321 * T_C**2 + 1020.62
def f_cp( T_C):
return 0.003313 * T_C + 0.0000008970785 * T_C**2 + 1.496005
def f_mu( T_C):
return np.exp(586.375/(T_C+62.5) -2.2809 )
def f_lam( T_C):
return -0.000033 * T_C - 0.00000015 * T_C**2 + 0.118294
def f_psa( T_C):
return np.exp(-9094.51/(T_C+340) + 17.6371 )
self.temperature.data = temp + 273.15 # Kelvin
self.density.data = f_rho(temp) # kg/m3
self.specific_heat.data = f_cp(temp)*1e3 # J/kg-K
self.conductivity.data = f_lam(temp) # W/m-K
# Viscosity: Pa-s (dynamic = kinematic * rho)
# mm2/s /1e6 -> m2/s * kg/m3 = kg/s/m = Pa s
self.viscosity.data = f_mu(temp)/1e6 * self.density.data
self.saturation_pressure.data = f_psa(temp) * 1e3 # Pa
self.Tmin = np.min(self.temperature.data)
self.Tmax = np.max(self.temperature.data)
self.TminPsat = 70+273.15
self.name = "T66"
self.description = "Therminol66"
self.reshapeAll()
class Therminol72(PureData):
"""
Heat transfer fluid Therminol 72 by Solutia
Source: Therminol Heat Transfer Reference Disk
"""
def __init__(self):
PureData.__init__(self)
self.temperature.data = np.array([-1.00000E+1, -5.00000E+0, +0.00000E+0, +5.00000E+0, +1.00000E+1, +1.50000E+1, +2.00000E+1, +2.50000E+1, +3.00000E+1, +3.50000E+1, +4.00000E+1, +4.50000E+1, +5.00000E+1, +5.50000E+1, +6.00000E+1, +6.50000E+1, +7.00000E+1, +7.50000E+1, +8.00000E+1, +8.50000E+1, +9.00000E+1, +9.50000E+1, +1.00000E+2, +1.05000E+2, +1.10000E+2, +1.15000E+2, +1.20000E+2, +1.25000E+2, +1.30000E+2, +1.35000E+2, +1.40000E+2, +1.45000E+2, +1.50000E+2, +1.55000E+2, +1.60000E+2, +1.65000E+2, +1.70000E+2, +1.75000E+2, +1.80000E+2, +1.85000E+2, +1.90000E+2, +1.95000E+2, +2.00000E+2, +2.05000E+2, +2.10000E+2, +2.15000E+2, +2.20000E+2, +2.25000E+2, +2.30000E+2, +2.35000E+2, +2.40000E+2, +2.45000E+2, +2.50000E+2, +2.55000E+2, +2.60000E+2, +2.65000E+2, +2.70000E+2, +2.75000E+2, +2.80000E+2, +2.85000E+2, +2.90000E+2, +2.95000E+2, +3.00000E+2, +3.05000E+2, +3.10000E+2, +3.15000E+2, +3.20000E+2, +3.25000E+2, +3.30000E+2, +3.35000E+2, +3.40000E+2, +3.45000E+2, +3.50000E+2, +3.55000E+2, +3.60000E+2, +3.65000E+2, +3.70000E+2, +3.75000E+2, +3.80000E+2])+273.15 # Kelvin
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.Tmin = np.min(self.temperature.data)
self.Tmax = np.max(self.temperature.data)
self.TminPsat = self.Tmin
self.name = "T72"
self.description = "Therminol72"
self.reshapeAll()
class DowthermJ(PureData):
"""
Heat transfer fluid Dowtherm J by Dow Chemicals
Source: Dow Chemicals data sheet
"""
def __init__(self):
PureData.__init__(self)
self.temperature.data = np.array([-8.00000E+1, -7.00000E+1, -6.00000E+1, -5.00000E+1, -4.00000E+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, +1.81300E+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, +3.45000E+2])+273.15 # Kelvin
self.density.data = np.array([+9.31300E+2, +9.27900E+2, +9.21000E+2, +9.14100E+2, +9.07100E+2, +9.00000E+2, +8.92900E+2, +8.85700E+2, +8.78500E+2, +8.71100E+2, +8.63700E+2, +8.56200E+2, +8.48700E+2, +8.41000E+2, +8.33200E+2, +8.25400E+2, +8.17400E+2, +8.09400E+2, +8.01200E+2, +7.92900E+2, +7.84400E+2, +7.75900E+2, +7.67100E+2, +7.58300E+2, +7.49200E+2, +7.40000E+2, +7.30600E+2, +7.29300E+2, +7.20900E+2, +7.11000E+2, +7.00900E+2, +6.90500E+2, +6.79800E+2, +6.68800E+2, +6.57300E+2, +6.45500E+2, +6.33100E+2, +6.20200E+2, +6.06600E+2, +5.92200E+2, +5.76900E+2, +5.60400E+2, +5.42400E+2, +5.22400E+2, +5.11400E+2]) # kg/m3
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.Tmin = np.min(self.temperature.data)
self.Tmax = np.max(self.temperature.data)
self.TminPsat = 50 + 273.15
self.name = "DowJ"
self.description = "DowthermJ"
self.reshapeAll()
class DowthermQ(PureData):
"""
Heat transfer fluid Dowtherm Q by Dow Chemicals
Source: Dow Chemicals data sheet
"""
def __init__(self):
PureData.__init__(self)
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.Tmin = np.min(self.temperature.data)
self.Tmax = np.max(self.temperature.data)
self.TminPsat = 120 + 273.15
self.name = "DowQ"
self.description = "DowthermQ"
self.reshapeAll()
class Texatherm22(PureData):
"""
Heat transfer fluid Texatherm 22 by Texaco
Source: Texaco data sheet
"""
def __init__(self):
PureData.__init__(self)
self.temperature.data = np.array([+0.00000E+0, +4.00000E+1, +5.00000E+1, +1.00000E+2, +1.50000E+2, +2.00000E+2, +2.50000E+2, +3.00000E+2, +3.50000E+2])+273.15 # Kelvin
self.density.data = np.array([+8.74500E+2, +8.47300E+2, +8.42500E+2, +8.10500E+2, +7.76300E+2, +7.41600E+2, +7.03200E+2, +6.68000E+2, +6.21500E+2]) # kg/m3
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.Tmin = np.min(self.temperature.data)
self.Tmax = np.max(self.temperature.data)
self.TminPsat = 40 + 273.15
self.name = "TX22"
self.description = "Texatherm22"
self.reshapeAll()
class NitrateSalt(PureData):
"""
Heat transfer fluid based on 60% NaNO3 and 40% KNO3
Source: Solar Power Tower Design Basis Document, Alexis B. Zavoico, Sandia Labs, USA
"""
def __init__(self):
PureData.__init__(self)
temp = np.linspace(300, 600, 25) # Celsius temperaure
def f_rho( T_C):
return 2090 - 0.636 * T_C
def f_cp( T_C):
return 1443 + 0.172 * T_C
def f_mu( T_C):
return 22.714 - 0.120 * T_C + 2.281 * 1e-4 * T_C*T_C - 1.474 * 1e-7 * T_C*T_C*T_C
def f_lam( T_C):
return 0.443 + 1.9e-4 * T_C
self.temperature.data = temp + 273.15 # Kelvin
self.density.data = f_rho(temp) # kg/m3
self.specific_heat.data = f_cp(temp)*1e3 # J/kg-K
self.conductivity.data = f_lam(temp) # W/m-K
# Viscosity: Pa-s (dynamic = kinematic * rho)
# mm2/s /1e6 -> m2/s * kg/m3 = kg/s/m = Pa s
self.viscosity.data = f_mu(temp)/1e3
self.Tmin = np.min(self.temperature.data)
self.Tmax = np.max(self.temperature.data)
self.TminPsat = self.Tmax
self.name = "NaK"
self.description = "NitrateSalt"
self.reshapeAll()
class SylthermXLT(PureData):
"""
Heat transfer fluid Syltherm XLT by Dow Chemicals
Source: Dow Chemicals data sheet
"""
def __init__(self):
PureData.__init__(self)
self.temperature.data = np.array([-1.00000E+2, -9.50000E+1, -9.00000E+1, -8.50000E+1, -8.00000E+1, -7.50000E+1, -7.00000E+1, -6.50000E+1, -6.00000E+1, -5.50000E+1, -5.00000E+1, -4.50000E+1, -4.00000E+1, -3.50000E+1, -3.00000E+1, -2.50000E+1, -2.00000E+1, -1.50000E+1, -1.00000E+1, -5.00000E+0, +0.00000E+0, +5.00000E+0, +1.00000E+1, +1.50000E+1, +2.00000E+1, +2.50000E+1, +3.00000E+1, +3.50000E+1, +4.00000E+1, +4.50000E+1, +5.00000E+1, +5.50000E+1, +6.00000E+1, +6.50000E+1, +7.00000E+1, +7.50000E+1, +8.00000E+1, +8.50000E+1, +9.00000E+1, +9.50000E+1, +1.00000E+2, +1.05000E+2, +1.10000E+2, +1.15000E+2, +1.20000E+2, +1.25000E+2, +1.30000E+2, +1.35000E+2, +1.40000E+2, +1.45000E+2, +1.50000E+2, +1.55000E+2, +1.60000E+2, +1.65000E+2, +1.70000E+2, +1.75000E+2, +1.80000E+2, +1.85000E+2, +1.90000E+2, +1.95000E+2, +2.00000E+2, +2.05000E+2, +2.10000E+2, +2.15000E+2, +2.20000E+2, +2.25000E+2, +2.30000E+2, +2.35000E+2, +2.40000E+2, +2.45000E+2, +2.50000E+2, +2.55000E+2, +2.60000E+2])+273.15 # Kelvin
self.density.data = np.array([+9.78500E+2, +9.73400E+2, +9.68300E+2, +9.63100E+2, +9.58000E+2, +9.52900E+2, +9.47700E+2, +9.42600E+2, +9.37500E+2, +9.32300E+2, +9.27200E+2, +9.22000E+2, +9.16900E+2, +9.11800E+2, +9.06600E+2, +9.01500E+2, +8.96400E+2, +8.91200E+2, +8.86100E+2, +8.81000E+2, +8.75800E+2, +8.70700E+2, +8.65500E+2, +8.60400E+2, +8.55300E+2, +8.50100E+2, +8.45000E+2, +8.39900E+2, +8.34700E+2, +8.29600E+2, +8.24500E+2, +8.19300E+2, +8.14200E+2, +8.09100E+2, +8.03900E+2, +7.98800E+2, +7.93600E+2, +7.88500E+2, +7.83400E+2, +7.78200E+2, +7.73100E+2, +7.68000E+2, +7.62800E+2, +7.57700E+2, +7.52600E+2, +7.47400E+2, +7.42300E+2, +7.37200E+2, +7.32000E+2, +7.26900E+2, +7.21700E+2, +7.16600E+2, +7.11500E+2, +7.06300E+2, +7.01200E+2, +6.96100E+2, +6.90900E+2, +6.85800E+2, +6.80700E+2, +6.75500E+2, +6.70400E+2, +6.65300E+2, +6.60100E+2, +6.55000E+2, +6.49800E+2, +6.44700E+2, +6.39600E+2, +6.34400E+2, +6.29300E+2, +6.24200E+2, +6.19000E+2, +6.13900E+2, +6.08800E+2]) # kg/m3
self.specific_heat.data = np.array([+1.52000E+0, +1.53000E+0, +1.54100E+0, +1.55100E+0, +1.56200E+0, +1.57200E+0, +1.58300E+0, +1.59300E+0, +1.60400E+0, +1.61400E+0, +1.62500E+0, +1.63500E+0, +1.64600E+0, +1.65600E+0, +1.66700E+0, +1.67700E+0, +1.68800E+0, +1.69800E+0, +1.70900E+0, +1.71900E+0, +1.73000E+0, +1.74000E+0, +1.75100E+0, +1.76100E+0, +1.77200E+0, +1.78200E+0, +1.79300E+0, +1.80300E+0, +1.81400E+0, +1.82400E+0, +1.83500E+0, +1.84500E+0, +1.85600E+0, +1.86600E+0, +1.87700E+0, +1.88700E+0, +1.89800E+0, +1.90800E+0, +1.91900E+0, +1.92900E+0, +1.94000E+0, +1.95000E+0, +1.96100E+0, +1.97100E+0, +1.98200E+0, +1.99200E+0, +2.00300E+0, +2.01300E+0, +2.02400E+0, +2.03400E+0, +2.04500E+0, +2.05500E+0, +2.06600E+0, +2.07600E+0, +2.08700E+0, +2.09700E+0, +2.10800E+0, +2.11800E+0, +2.12900E+0, +2.13900E+0, +2.15000E+0, +2.16000E+0, +2.17100E+0, +2.18100E+0, +2.19200E+0, +2.20200E+0, +2.21300E+0, +2.22300E+0, +2.23400E+0, +2.24400E+0, +2.25500E+0, +2.26500E+0, +2.27600E+0])*1e3 # J/kg-K
self.conductivity.data = np.array([+1.34100E-1, +1.33200E-1, +1.32400E-1, +1.31500E-1, +1.30600E-1, +1.29700E-1, +1.28800E-1, +1.27900E-1, +1.26900E-1, +1.26000E-1, +1.25000E-1, +1.24100E-1, +1.23100E-1, +1.22100E-1, +1.21100E-1, +1.20100E-1, +1.19100E-1, +1.18100E-1, +1.17100E-1, +1.16100E-1, +1.15000E-1, +1.14000E-1, +1.12900E-1, +1.11900E-1, +1.10800E-1, +1.09700E-1, +1.08600E-1, +1.07500E-1, +1.06400E-1, +1.05300E-1, +1.04200E-1, +1.03000E-1, +1.01900E-1, +1.00800E-1, +9.96000E-2, +9.84400E-2, +9.72800E-2, +9.61000E-2, +9.49200E-2, +9.37300E-2, +9.25300E-2, +9.13300E-2, +9.01200E-2, +8.89100E-2, +8.76800E-2, +8.64500E-2, +8.52200E-2, +8.39800E-2, +8.27300E-2, +8.14700E-2, +8.02100E-2, +7.89500E-2, +7.76700E-2, +7.64000E-2, +7.51100E-2, +7.38200E-2, +7.25300E-2, +7.12300E-2, +6.99200E-2, +6.86100E-2, +6.72900E-2, +6.59700E-2, +6.46500E-2, +6.33100E-2, +6.19800E-2, +6.06400E-2, +5.92900E-2, +5.79400E-2, +5.65800E-2, +5.52300E-2, +5.38600E-2, +5.24900E-2, +5.11200E-2]) # W/m-K
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.description = "SylthermXLT"
self.reshapeAll()
class HC50(PureData):
"""
Heat transfer fluid Dynalene HC-50
Source: Dynalene
"""
def __init__(self):
PureData.__init__(self)
self.temperature.data = np.array([+2.23150E+2,+2.33150E+2,+2.43150E+2,+2.53150E+2,+2.63150E+2,+2.73150E+2,+2.83150E+2,+2.93150E+2,+3.03150E+2,+3.13150E+2,+3.23150E+2,+3.33150E+2,+3.43150E+2,+3.53150E+2,+3.63150E+2,+3.73150E+2,+3.83150E+2,+3.93150E+2,+4.03150E+2,+4.13150E+2,+4.23150E+2,+4.33150E+2,+4.43150E+2,+4.53150E+2,+4.63150E+2,+4.73150E+2,+4.83150E+2]) # Kelvin
self.density.data = np.array([+1.37800E+3,+1.37300E+3,+1.36700E+3,+1.36200E+3,+1.35600E+3,+1.35100E+3,+1.34500E+3,+1.34000E+3,+1.33400E+3,+1.32800E+3,+1.32300E+3,+1.31700E+3,+1.31200E+3,+1.30600E+3,+1.30100E+3,+1.29500E+3,+1.29000E+3,+1.28400E+3,+1.27900E+3,+1.27300E+3,+1.26700E+3,+1.26200E+3,+1.25600E+3,+1.25100E+3,+1.24500E+3,+1.24000E+3,+1.23400E+3]) # kg/m3
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,+2.40000E-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.Tmin = np.min(self.temperature.data)
self.Tmax = np.max(self.temperature.data)
self.TminPsat = 20+273.15
self.name = "HC50"
self.description = "Dynalene "+ self.name
self.reshapeAll()
class HC40(PureData):
"""
Heat transfer fluid Dynalene HC-40
Source: Dynalene
"""
def __init__(self):
PureData.__init__(self)
self.temperature.data = np.array([+2.33150E+2,+2.43150E+2,+2.53150E+2,+2.63150E+2,+2.73150E+2,+2.83150E+2,+2.93150E+2,+3.03150E+2,+3.13150E+2,+3.23150E+2,+3.33150E+2,+3.43150E+2,+3.53150E+2,+3.63150E+2,+3.73150E+2,+3.83150E+2,+3.93150E+2,+4.03150E+2,+4.13150E+2,+4.23150E+2,+4.33150E+2,+4.38150E+2,+4.43150E+2,+4.53150E+2,+4.63150E+2,+4.73150E+2]) # Kelvin
self.density.data = np.array([+1.34800E+3,+1.34300E+3,+1.33700E+3,+1.33200E+3,+1.32600E+3,+1.32100E+3,+1.31500E+3,+1.30900E+3,+1.30400E+3,+1.29800E+3,+1.29300E+3,+1.28700E+3,+1.28100E+3,+1.27600E+3,+1.27000E+3,+1.26500E+3,+1.25900E+3,+1.25300E+3,+1.24800E+3,+1.24200E+3,+1.23700E+3,+1.23400E+3,+1.23100E+3,+1.22500E+3,+1.22000E+3,+1.21400E+3]) # kg/m3
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.Tmin = np.min(self.temperature.data)
self.Tmax = np.max(self.temperature.data)
self.TminPsat = 20+273.15
self.name = "HC40"
self.description = "Dynalene "+ self.name
self.reshapeAll()
class HC30(PureData):
"""
Heat transfer fluid Dynalene HC-30
Source: Dynalene
"""
def __init__(self):
PureData.__init__(self)
self.temperature.data = np.array([+2.43150E+2,+2.53150E+2,+2.63150E+2,+2.73150E+2,+2.83150E+2,+2.93150E+2,+3.03150E+2,+3.13150E+2,+3.23150E+2,+3.33150E+2,+3.43150E+2,+3.53150E+2,+3.63150E+2,+3.73150E+2,+3.83150E+2,+3.93150E+2,+4.03150E+2,+4.13150E+2,+4.23150E+2,+4.33150E+2,+4.43150E+2,+4.53150E+2,+4.63150E+2,+4.73150E+2,+4.83150E+2]) # Kelvin
self.density.data = np.array([+1.30000E+3,+1.29500E+3,+1.29000E+3,+1.28500E+3,+1.28000E+3,+1.27500E+3,+1.27000E+3,+1.26500E+3,+1.26000E+3,+1.25500E+3,+1.25000E+3,+1.24400E+3,+1.23900E+3,+1.23400E+3,+1.22900E+3,+1.22400E+3,+1.21900E+3,+1.21400E+3,+1.20900E+3,+1.20400E+3,+1.19900E+3,+1.19300E+3,+1.18800E+3,+1.18300E+3,+1.17800E+3]) # kg/m3
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.Tmin = np.min(self.temperature.data)
self.Tmax = np.max(self.temperature.data)
self.TminPsat = 20+273.15
self.name = "HC30"
self.description = "Dynalene "+ self.name
self.reshapeAll()
class HC20(PureData):
"""
Heat transfer fluid Dynalene HC-20
Source: Dynalene
"""
def __init__(self):
PureData.__init__(self)
self.temperature.data = np.array([+2.53150E+2,+2.63150E+2,+2.73150E+2,+2.83150E+2,+2.93150E+2,+3.03150E+2,+3.13150E+2,+3.23150E+2,+3.33150E+2,+3.43150E+2,+3.53150E+2,+3.63150E+2,+3.73150E+2,+3.83150E+2,+3.93150E+2,+4.03150E+2,+4.13150E+2,+4.23150E+2,+4.33150E+2,+4.43150E+2,+4.53150E+2,+4.63150E+2,+4.73150E+2,+4.83150E+2]) # Kelvin
self.density.data = np.array([+1.25800E+3,+1.25300E+3,+1.24800E+3,+1.24200E+3,+1.23700E+3,+1.23200E+3,+1.22700E+3,+1.22200E+3,+1.21600E+3,+1.21100E+3,+1.20600E+3,+1.20100E+3,+1.19600E+3,+1.19100E+3,+1.18500E+3,+1.18000E+3,+1.17500E+3,+1.17000E+3,+1.16500E+3,+1.15900E+3,+1.15400E+3,+1.14900E+3,+1.14400E+3,+1.13900E+3]) # kg/m3
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.Tmin = np.min(self.temperature.data)
self.Tmax = np.max(self.temperature.data)
self.TminPsat = 20+273.15
self.name = "HC20"
self.description = "Dynalene "+ self.name
self.reshapeAll()
class HC10(PureData):
"""
Heat transfer fluid Dynalene HC-10
Source: Dynalene
"""
def __init__(self):
PureData.__init__(self)
self.temperature.data = np.array([+2.63150E+2,+2.73150E+2,+2.83150E+2,+2.93150E+2,+3.03150E+2,+3.13150E+2,+3.23150E+2,+3.33150E+2,+3.43150E+2,+3.53150E+2,+3.63150E+2,+3.73150E+2,+3.83150E+2,+3.93150E+2,+4.03150E+2,+4.13150E+2,+4.23150E+2,+4.33150E+2,+4.43150E+2,+4.53150E+2,+4.63150E+2,+4.73150E+2,+4.83150E+2,+4.91150E+2]) # Kelvin
self.density.data = np.array([+1.20400E+3,+1.19900E+3,+1.19500E+3,+1.19000E+3,+1.18600E+3,+1.18100E+3,+1.17700E+3,+1.17200E+3,+1.16700E+3,+1.16300E+3,+1.15800E+3,+1.15400E+3,+1.14900E+3,+1.14500E+3,+1.14000E+3,+1.13600E+3,+1.13100E+3,+1.12700E+3,+1.12200E+3,+1.11800E+3,+1.11300E+3,+1.10900E+3,+1.10400E+3,+1.10100E+3]) # kg/m3
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.Tmin = np.min(self.temperature.data)
self.Tmax = np.max(self.temperature.data)
self.TminPsat = 20+273.15
self.name = "HC10"
self.description = "Dynalene "+ self.name
self.reshapeAll()
class AS10(PureData):
"""
Heat transfer fluid Aspen Temper -10 by Aspen Petroleum
Source: SecCool Software
"""
def __init__(self):
PureData.__init__(self)
self.temperature.data = np.array([2.65000E+02, 2.70000E+02, 2.75000E+02, 2.80000E+02, 2.85000E+02, 2.90000E+02, 2.95000E+02, 3.00000E+02]) # Kelvin
self.density.data = np.array([1.09160E+03, 1.09060E+03, 1.08960E+03, 1.08860E+03, 1.08760E+03, 1.08660E+03, 1.08560E+03, 1.08460E+03]) # kg/m3
self.specific_heat.data = np.array([3.52460E+03, 3.53540E+03, 3.54550E+03, 3.55500E+03, 3.56380E+03, 3.57190E+03, 3.57940E+03, 3.58620E+03]) # J/kg-K
self.conductivity.data = np.array([5.02200E-01, 5.09600E-01, 5.17000E-01, 5.24400E-01, 5.31800E-01, 5.39200E-01, 5.46700E-01, 5.54100E-01]) # W/m-K
self.viscosity.data = np.array([3.83600E-03, 3.16000E-03, 2.61300E-03, 2.17700E-03, 1.83700E-03, 1.57700E-03, 1.38200E-03, 1.23500E-03]) # Pa-s
self.Tmin = np.min(self.temperature.data)
self.Tmax = np.max(self.temperature.data)
self.TminPsat = self.Tmax
self.name = "AS10"
self.description = "Aspen Temper -10"
self.reshapeAll()
class AS20(PureData):
"""
Heat transfer fluid Aspen Temper -20 by Aspen Petroleum
Source: SecCool Software
"""
def __init__(self):
PureData.__init__(self)
self.temperature.data = np.array([2.55000E+02, 2.60000E+02, 2.65000E+02, 2.70000E+02, 2.75000E+02, 2.80000E+02, 2.85000E+02, 2.90000E+02, 2.95000E+02, 3.00000E+02]) # Kelvin
self.density.data = np.array([1.15050E+03, 1.14970E+03, 1.14870E+03, 1.14770E+03, 1.14660E+03, 1.14540E+03, 1.14420E+03, 1.14290E+03, 1.14150E+03, 1.14000E+03]) # kg/m3
self.specific_heat.data = np.array([3.20660E+03, 3.22280E+03, 3.23840E+03, 3.25340E+03, 3.26780E+03, 3.28150E+03, 3.29470E+03, 3.30720E+03, 3.31920E+03, 3.33050E+03]) # J/kg-K
self.conductivity.data = np.array([4.56400E-01, 4.63100E-01, 4.69800E-01, 4.76500E-01, 4.83200E-01, 4.90000E-01, 4.96700E-01, 5.03400E-01, 5.10100E-01, 5.16800E-01]) # W/m-K
self.viscosity.data = np.array([7.43800E-03, 5.91400E-03, 4.74900E-03, 3.85900E-03, 3.17900E-03, 2.65900E-03, 2.26100E-03, 1.95800E-03, 1.72500E-03, 1.54800E-03]) # Pa-s
self.Tmin = np.min(self.temperature.data)
self.Tmax = np.max(self.temperature.data)
self.TminPsat = self.Tmax
self.name = "AS20"
self.description = "Aspen Temper -20"
self.reshapeAll()
class AS30(PureData):
"""
Heat transfer fluid Aspen Temper -30 by Aspen Petroleum
Source: SecCool Software
"""
def __init__(self):
PureData.__init__(self)
self.temperature.data = np.array([2.45000E+02, 2.50000E+02, 2.55000E+02, 2.60000E+02, 2.65000E+02, 2.70000E+02, 2.75000E+02, 2.80000E+02, 2.85000E+02, 2.90000E+02, 2.95000E+02, 3.00000E+02]) # Kelvin
self.density.data = np.array([1.19140E+03, 1.19030E+03, 1.18910E+03, 1.18770E+03, 1.18630E+03, 1.18480E+03, 1.18330E+03, 1.18170E+03, 1.18010E+03, 1.17840E+03, 1.17680E+03, 1.17510E+03]) # kg/m3
self.specific_heat.data = np.array([2.96950E+03, 2.99130E+03, 3.01190E+03, 3.03100E+03, 3.04890E+03, 3.06540E+03, 3.08050E+03, 3.09430E+03, 3.10670E+03, 3.11770E+03, 3.12750E+03, 3.13580E+03]) # J/kg-K
self.conductivity.data = np.array([4.25000E-01, 4.31300E-01, 4.37600E-01, 4.43900E-01, 4.50200E-01, 4.56400E-01, 4.62700E-01, 4.69000E-01, 4.75300E-01, 4.81600E-01, 4.87800E-01, 4.94100E-01]) # W/m-K
self.viscosity.data = np.array([1.56400E-02, 1.19300E-02, 9.17800E-03, 7.14000E-03, 5.62900E-03, 4.50900E-03, 3.67900E-03, 3.06400E-03, 2.60800E-03, 2.27000E-03, 2.01900E-03, 1.83400E-03]) # Pa-s
self.Tmin = np.min(self.temperature.data)
self.Tmax = np.max(self.temperature.data)
self.TminPsat = self.Tmax
self.name = "AS30"
self.description = "Aspen Temper -30"
self.reshapeAll()
class AS40(PureData):
"""
Heat transfer fluid Aspen Temper -40 by Aspen Petroleum
Source: SecCool Software
"""
def __init__(self):
PureData.__init__(self)
self.temperature.data = np.array([2.35000E+02, 2.40000E+02, 2.45000E+02, 2.50000E+02, 2.55000E+02, 2.60000E+02, 2.65000E+02, 2.70000E+02, 2.75000E+02, 2.80000E+02, 2.85000E+02, 2.90000E+02, 2.95000E+02, 3.00000E+02]) # Kelvin
self.density.data = np.array([1.22670E+03, 1.22560E+03, 1.22420E+03, 1.22280E+03, 1.22120E+03, 1.21960E+03, 1.21780E+03, 1.21600E+03, 1.21410E+03, 1.21220E+03, 1.21020E+03, 1.20820E+03, 1.20620E+03, 1.20420E+03]) # kg/m3
self.specific_heat.data = np.array([2.83450E+03, 2.85970E+03, 2.88300E+03, 2.90430E+03, 2.92370E+03, 2.94120E+03, 2.95670E+03, 2.97030E+03, 2.98200E+03, 2.99170E+03, 2.99950E+03, 3.00530E+03, 3.00920E+03, 3.01120E+03]) # J/kg-K
self.conductivity.data = np.array([4.01400E-01, 4.06900E-01, 4.12400E-01, 4.17900E-01, 4.23400E-01, 4.28900E-01, 4.34400E-01, 4.39900E-01, 4.45400E-01, 4.50900E-01, 4.56400E-01, 4.61800E-01, 4.67300E-01, 4.72800E-01]) # W/m-K
self.viscosity.data = np.array([4.43400E-02, 3.01000E-02, 2.10800E-02, 1.52600E-02, 1.14300E-02, 8.84100E-03, 7.03900E-03, 5.74200E-03, 4.77600E-03, 4.03200E-03, 3.44300E-03, 2.96300E-03, 2.56600E-03, 2.23100E-03]) # Pa-s
self.Tmin = np.min(self.temperature.data)
self.Tmax = np.max(self.temperature.data)
self.TminPsat = self.Tmax
self.name = "AS40"
self.description = "Aspen Temper -40"
self.reshapeAll()
class AS55(PureData):
"""
Heat transfer fluid Aspen Temper -55 by Aspen Petroleum
Source: SecCool Software
"""
def __init__(self):
PureData.__init__(self)
self.temperature.data = np.array([2.20000E+02, 2.25000E+02, 2.30000E+02, 2.35000E+02, 2.40000E+02, 2.45000E+02, 2.50000E+02, 2.55000E+02, 2.60000E+02, 2.65000E+02, 2.70000E+02, 2.75000E+02, 2.80000E+02, 2.85000E+02, 2.90000E+02, 2.95000E+02, 3.00000E+02]) # Kelvin
self.density.data = np.array([1.26880E+03, 1.26780E+03, 1.26650E+03, 1.26510E+03, 1.26350E+03, 1.26180E+03, 1.25990E+03, 1.25790E+03, 1.25580E+03, 1.25350E+03, 1.25120E+03, 1.24890E+03, 1.24640E+03, 1.24400E+03, 1.24150E+03, 1.23900E+03, 1.23650E+03]) # kg/m3
self.specific_heat.data = np.array([2.64790E+03, 2.67190E+03, 2.69470E+03, 2.71630E+03, 2.73660E+03, 2.75570E+03, 2.77350E+03, 2.79010E+03, 2.80540E+03, 2.81950E+03, 2.83240E+03, 2.84400E+03, 2.85440E+03, 2.86350E+03, 2.87140E+03, 2.87800E+03, 2.88340E+03]) # J/kg-K
self.conductivity.data = np.array([3.82400E-01, 3.85900E-01, 3.89600E-01, 3.93300E-01, 3.97200E-01, 4.01200E-01, 4.05300E-01, 4.09500E-01, 4.13900E-01, 4.18300E-01, 4.22900E-01, 4.27500E-01, 4.32300E-01, 4.37200E-01, 4.42300E-01, 4.47400E-01, 4.52600E-01]) # W/m-K
self.viscosity.data = np.array([2.93600E-01, 1.62700E-01, 9.44200E-02, 5.79500E-02, 3.78300E-02, 2.62200E-02, 1.91500E-02, 1.45600E-02, 1.14000E-02, 9.10600E-03, 7.36900E-03, 6.01200E-03, 4.93000E-03, 4.05600E-03, 3.34300E-03, 2.75900E-03, 2.27800E-03]) # Pa-s
self.Tmin = np.min(self.temperature.data)
self.Tmax = np.max(self.temperature.data)
self.TminPsat = self.Tmax
self.name = "AS55"
self.description = "Aspen Temper -55"
self.reshapeAll()
class ZS10(PureData):
"""
Heat transfer fluid Zitrec S -10 by Arteco
Source: SecCool Software
"""
def __init__(self):
PureData.__init__(self)
self.temperature.data = np.array([2.65000E+02, 2.70000E+02, 2.75000E+02, 2.80000E+02, 2.85000E+02, 2.90000E+02, 2.95000E+02, 3.00000E+02, 3.05000E+02, 3.10000E+02, 3.15000E+02, 3.20000E+02, 3.25000E+02, 3.30000E+02, 3.35000E+02, 3.40000E+02, 3.45000E+02, 3.50000E+02, 3.55000E+02, 3.60000E+02]) # Kelvin
self.density.data = np.array([1.10250E+03, 1.10020E+03, 1.09790E+03, 1.09550E+03, 1.09320E+03, 1.09090E+03, 1.08860E+03, 1.08630E+03, 1.08390E+03, 1.08160E+03, 1.07930E+03, 1.07700E+03, 1.07470E+03, 1.07230E+03, 1.07000E+03, 1.06770E+03, 1.06540E+03, 1.06300E+03, 1.06070E+03, 1.05840E+03]) # kg/m3
self.specific_heat.data = np.array([3.54260E+03, 3.55520E+03, 3.56720E+03, 3.57880E+03, 3.59000E+03, 3.60070E+03, 3.61090E+03, 3.62060E+03, 3.62990E+03, 3.63870E+03, 3.64710E+03, 3.65500E+03, 3.66240E+03, 3.66940E+03, 3.67590E+03, 3.68190E+03, 3.68750E+03, 3.69260E+03, 3.69720E+03, 3.70140E+03]) # J/kg-K
self.conductivity.data = np.array([4.99700E-01, 5.06300E-01, 5.13000E-01, 5.19600E-01, 5.26200E-01, 5.32800E-01, 5.39400E-01, 5.45900E-01, 5.52500E-01, 5.59000E-01, 5.65500E-01, 5.72000E-01, 5.78500E-01, 5.84900E-01, 5.91400E-01, 5.97800E-01, 6.04300E-01, 6.10700E-01, 6.17100E-01, 6.23400E-01]) # W/m-K
self.viscosity.data = np.array([4.51900E-03, 3.75000E-03, 3.14500E-03, 2.66500E-03, 2.28200E-03, 1.97200E-03, 1.72000E-03, 1.51300E-03, 1.34200E-03, 1.20000E-03, 1.08100E-03, 9.80000E-04, 8.94000E-04, 8.21000E-04, 7.58000E-04, 7.03000E-04, 6.56000E-04, 6.14000E-04, 5.77000E-04, 5.44000E-04]) # Pa-s
self.Tmin = np.min(self.temperature.data)
self.Tmax = np.max(self.temperature.data)
self.TminPsat = self.Tmax
self.name = "ZS10"
self.description = "Zitrec S -10"
self.reshapeAll()
class ZS25(PureData):
"""
Heat transfer fluid Zitrec S -25 by Arteco
Source: SecCool Software
"""
def __init__(self):
PureData.__init__(self)
self.temperature.data = np.array([2.50000E+02, 2.55000E+02, 2.60000E+02, 2.65000E+02, 2.70000E+02, 2.75000E+02, 2.80000E+02, 2.85000E+02, 2.90000E+02, 2.95000E+02, 3.00000E+02, 3.05000E+02, 3.10000E+02, 3.15000E+02, 3.20000E+02, 3.25000E+02, 3.30000E+02, 3.35000E+02, 3.40000E+02, 3.45000E+02, 3.50000E+02, 3.55000E+02, 3.60000E+02]) # Kelvin
self.density.data = np.array([1.20620E+03, 1.20360E+03, 1.20090E+03, 1.19820E+03, 1.19560E+03, 1.19290E+03, 1.19030E+03, 1.18760E+03, 1.18490E+03, 1.18230E+03, 1.17960E+03, 1.17690E+03, 1.17430E+03, 1.17160E+03, 1.16890E+03, 1.16630E+03, 1.16360E+03, 1.16100E+03, 1.15830E+03, 1.15560E+03, 1.15300E+03, 1.15030E+03, 1.14760E+03]) # kg/m3
self.specific_heat.data = np.array([3.17680E+03, 3.17880E+03, 3.18090E+03, 3.18290E+03, 3.18500E+03, 3.18710E+03, 3.18920E+03, 3.19130E+03, 3.19340E+03, 3.19550E+03, 3.19760E+03, 3.19980E+03, 3.20200E+03, 3.20410E+03, 3.20630E+03, 3.20850E+03, 3.21070E+03, 3.21290E+03, 3.21520E+03, 3.21740E+03, 3.21970E+03, 3.22200E+03, 3.22420E+03]) # J/kg-K
self.conductivity.data = np.array([4.43000E-01, 4.49600E-01, 4.56200E-01, 4.62700E-01, 4.69200E-01, 4.75600E-01, 4.81900E-01, 4.88200E-01, 4.94400E-01, 5.00600E-01, 5.06700E-01, 5.12700E-01, 5.18700E-01, 5.24600E-01, 5.30400E-01, 5.36200E-01, 5.42000E-01, 5.47700E-01, 5.53300E-01, 5.58800E-01, 5.64300E-01, 5.69800E-01, 5.75200E-01]) # W/m-K
self.viscosity.data = np.array([1.06800E-02, 8.37400E-03, 6.68600E-03, 5.42800E-03, 4.47800E-03, 3.74900E-03, 3.18300E-03, 2.73800E-03, 2.38400E-03, 2.10000E-03, 1.86800E-03, 1.67800E-03, 1.52000E-03, 1.38800E-03, 1.27500E-03, 1.17900E-03, 1.09500E-03, 1.02100E-03, 9.55000E-04, 8.95000E-04, 8.40000E-04, 7.89000E-04, 7.40000E-04]) # Pa-s
self.Tmin = np.min(self.temperature.data)
self.Tmax = np.max(self.temperature.data)
self.TminPsat = self.Tmax
self.name = "ZS25"
self.description = "Zitrec S -25"
self.reshapeAll()
class ZS40(PureData):
"""
Heat transfer fluid Zitrec S -40 by Arteco
Source: SecCool Software
"""
def __init__(self):
PureData.__init__(self)
self.temperature.data = np.array([2.35000E+02, 2.40000E+02, 2.45000E+02, 2.50000E+02, 2.55000E+02, 2.60000E+02, 2.65000E+02, 2.70000E+02, 2.75000E+02, 2.80000E+02, 2.85000E+02, 2.90000E+02, 2.95000E+02, 3.00000E+02, 3.05000E+02, 3.10000E+02, 3.15000E+02, 3.20000E+02, 3.25000E+02, 3.30000E+02, 3.35000E+02, 3.40000E+02, 3.45000E+02, 3.50000E+02, 3.55000E+02, 3.60000E+02]) # Kelvin
self.density.data = np.array([1.28360E+03, 1.28080E+03, 1.27800E+03, 1.27510E+03, 1.27230E+03, 1.26940E+03, 1.26660E+03, 1.26380E+03, 1.26090E+03, 1.25810E+03, 1.25530E+03, 1.25240E+03, 1.24960E+03, 1.24680E+03, 1.24390E+03, 1.24110E+03, 1.23820E+03, 1.23540E+03, 1.23260E+03, 1.22970E+03, 1.22690E+03, 1.22410E+03, 1.22120E+03, 1.21840E+03, 1.21550E+03, 1.21270E+03]) # kg/m3
self.specific_heat.data = np.array([2.69640E+03, 2.70500E+03, 2.71320E+03, 2.72100E+03, 2.72850E+03, 2.73570E+03, 2.74260E+03, 2.74940E+03, 2.75600E+03, 2.76250E+03, 2.76900E+03, 2.77540E+03, 2.78190E+03, 2.78850E+03, 2.79530E+03, 2.80220E+03, 2.80930E+03, 2.81670E+03, 2.82440E+03, 2.83250E+03, 2.84100E+03, 2.85000E+03, 2.85950E+03, 2.86950E+03, 2.88010E+03, 2.89140E+03]) # J/kg-K
self.conductivity.data = np.array([4.15100E-01, 4.20500E-01, 4.25800E-01, 4.31200E-01, 4.36500E-01, 4.41800E-01, 4.47200E-01, 4.52500E-01, 4.57800E-01, 4.63100E-01, 4.68400E-01, 4.73600E-01, 4.78900E-01, 4.84200E-01, 4.89400E-01, 4.94700E-01, 4.99900E-01, 5.05200E-01, 5.10400E-01, 5.15600E-01, 5.20800E-01, 5.26000E-01, 5.31200E-01, 5.36400E-01, 5.41600E-01, 5.46800E-01]) # W/m-K
self.viscosity.data = np.array([3.10200E-02, 2.28600E-02, 1.72100E-02, 1.32300E-02, 1.03600E-02, 8.26100E-03, 6.70400E-03, 5.53000E-03, 4.63200E-03, 3.93600E-03, 3.38900E-03, 2.95500E-03, 2.60700E-03, 2.32300E-03, 2.09100E-03, 1.89800E-03, 1.73500E-03, 1.59700E-03, 1.47900E-03, 1.37500E-03, 1.28400E-03, 1.20200E-03, 1.12700E-03, 1.05800E-03, 9.93000E-04, 9.30000E-04]) # Pa-s
self.Tmin = np.min(self.temperature.data)
self.Tmax = np.max(self.temperature.data)
self.TminPsat = self.Tmax
self.name = "ZS40"
self.description = "Zitrec S -40"
self.reshapeAll()
class ZS45(PureData):
"""
Heat transfer fluid Zitrec S -45 by Arteco
Source: SecCool Software
"""
def __init__(self):
PureData.__init__(self)
self.temperature.data = np.array([2.30000E+02, 2.35000E+02, 2.40000E+02, 2.45000E+02, 2.50000E+02, 2.55000E+02, 2.60000E+02, 2.65000E+02, 2.70000E+02, 2.75000E+02, 2.80000E+02, 2.85000E+02, 2.90000E+02, 2.95000E+02, 3.00000E+02, 3.05000E+02, 3.10000E+02, 3.15000E+02, 3.20000E+02, 3.25000E+02, 3.30000E+02, 3.35000E+02, 3.40000E+02, 3.45000E+02, 3.50000E+02, 3.55000E+02, 3.60000E+02]) # Kelvin
self.density.data = np.array([1.30590E+03, 1.30320E+03, 1.30040E+03, 1.29760E+03, 1.29490E+03, 1.29210E+03, 1.28940E+03, 1.28660E+03, 1.28380E+03, 1.28110E+03, 1.27830E+03, 1.27550E+03, 1.27280E+03, 1.27000E+03, 1.26730E+03, 1.26450E+03, 1.26170E+03, 1.25900E+03, 1.25620E+03, 1.25340E+03, 1.25070E+03, 1.24790E+03, 1.24520E+03, 1.24240E+03, 1.23960E+03, 1.23690E+03, 1.23410E+03]) # kg/m3
self.specific_heat.data = np.array([2.55240E+03, 2.56350E+03, 2.57450E+03, 2.58550E+03, 2.59650E+03, 2.60760E+03, 2.61860E+03, 2.62960E+03, 2.64070E+03, 2.65170E+03, 2.66270E+03, 2.67370E+03, 2.68480E+03, 2.69580E+03, 2.70680E+03, 2.71790E+03, 2.72890E+03, 2.73990E+03, 2.75090E+03, 2.76200E+03, 2.77300E+03, 2.78400E+03, 2.79510E+03, 2.80610E+03, 2.81710E+03, 2.82810E+03, 2.83920E+03]) # J/kg-K
self.conductivity.data = np.array([4.06200E-01, 4.11100E-01, 4.15900E-01, 4.20900E-01, 4.25800E-01, 4.30700E-01, 4.35700E-01, 4.40600E-01, 4.45600E-01, 4.50600E-01, 4.55700E-01, 4.60700E-01, 4.65800E-01, 4.70900E-01, 4.76000E-01, 4.81100E-01, 4.86200E-01, 4.91400E-01, 4.96600E-01, 5.01700E-01, 5.07000E-01, 5.12200E-01, 5.17400E-01, 5.22700E-01, 5.28000E-01, 5.33300E-01, 5.38600E-01]) # W/m-K
self.viscosity.data = np.array([4.97400E-02, 3.53200E-02, 2.57000E-02, 1.91400E-02, 1.45700E-02, 1.13300E-02, 8.99200E-03, 7.27000E-03, 5.98200E-03, 5.00500E-03, 4.25200E-03, 3.66500E-03, 3.20000E-03, 2.82800E-03, 2.52700E-03, 2.28000E-03, 2.07500E-03, 1.90300E-03, 1.75600E-03, 1.62900E-03, 1.51800E-03, 1.41800E-03, 1.32800E-03, 1.24400E-03, 1.16500E-03, 1.08900E-03, 1.01600E-03]) # Pa-s
self.Tmin = np.min(self.temperature.data)
self.Tmax = np.max(self.temperature.data)
self.TminPsat = self.Tmax
self.name = "ZS45"
self.description = "Zitrec S -45"
self.reshapeAll()
class ZS55(PureData):
"""
Heat transfer fluid Zitrec S -55 by Arteco
Source: SecCool Software
"""
def __init__(self):
PureData.__init__(self)
self.temperature.data = np.array([2.20000E+02, 2.25000E+02, 2.30000E+02, 2.35000E+02, 2.40000E+02, 2.45000E+02, 2.50000E+02, 2.55000E+02, 2.60000E+02, 2.65000E+02, 2.70000E+02, 2.75000E+02, 2.80000E+02, 2.85000E+02, 2.90000E+02, 2.95000E+02, 3.00000E+02, 3.05000E+02, 3.10000E+02, 3.15000E+02, 3.20000E+02, 3.25000E+02, 3.30000E+02, 3.35000E+02, 3.40000E+02, 3.45000E+02, 3.50000E+02, 3.55000E+02, 3.60000E+02]) # Kelvin
self.density.data = np.array([1.35580E+03, 1.35280E+03, 1.34980E+03, 1.34680E+03, 1.34380E+03, 1.34070E+03, 1.33770E+03, 1.33470E+03, 1.33170E+03, 1.32870E+03, 1.32560E+03, 1.32260E+03, 1.31960E+03, 1.31660E+03, 1.31350E+03, 1.31050E+03, 1.30750E+03, 1.30450E+03, 1.30150E+03, 1.29840E+03, 1.29540E+03, 1.29240E+03, 1.28940E+03, 1.28630E+03, 1.28330E+03, 1.28030E+03, 1.27730E+03, 1.27430E+03, 1.27120E+03]) # kg/m3
self.specific_heat.data = np.array([2.43970E+03, 2.44650E+03, 2.45350E+03, 2.46070E+03, 2.46810E+03, 2.47580E+03, 2.48380E+03, 2.49190E+03, 2.50030E+03, 2.50900E+03, 2.51780E+03, 2.52700E+03, 2.53630E+03, 2.54590E+03, 2.55570E+03, 2.56580E+03, 2.57610E+03, 2.58660E+03, 2.59740E+03, 2.60840E+03, 2.61970E+03, 2.63120E+03, 2.64290E+03, 2.65480E+03, 2.66700E+03, 2.67950E+03, 2.69210E+03, 2.70500E+03, 2.71820E+03]) # J/kg-K
self.conductivity.data = np.array([3.93100E-01, 3.97000E-01, 4.01000E-01, 4.05100E-01, 4.09100E-01, 4.13200E-01, 4.17300E-01, 4.21400E-01, 4.25600E-01, 4.29700E-01, 4.33900E-01, 4.38200E-01, 4.42400E-01, 4.46700E-01, 4.51000E-01, 4.55400E-01, 4.59700E-01, 4.64100E-01, 4.68500E-01, 4.73000E-01, 4.77500E-01, 4.82000E-01, 4.86500E-01, 4.91000E-01, 4.95600E-01, 5.00200E-01, 5.04800E-01, 5.09500E-01, 5.14200E-01]) # W/m-K
self.viscosity.data = np.array([1.44300E-01, 9.52000E-02, 6.46500E-02, 4.51300E-02, 3.23400E-02, 2.37700E-02, 1.78900E-02, 1.37800E-02, 1.08400E-02, 8.69800E-03, 7.11600E-03, 5.92500E-03, 5.01500E-03, 4.31100E-03, 3.75700E-03, 3.31700E-03, 2.96200E-03, 2.67300E-03, 2.43300E-03, 2.23300E-03, 2.06300E-03, 1.91500E-03, 1.78600E-03, 1.67000E-03, 1.56500E-03, 1.46600E-03, 1.37300E-03, 1.28300E-03, 1.19400E-03]) # Pa-s
self.Tmin = np.min(self.temperature.data)
self.Tmax = np.max(self.temperature.data)
self.TminPsat = self.Tmax
self.name = "ZS55"
self.description = "Zitrec S -55"
self.reshapeAll()

222
dev/incompressible_liquids/CPIncomp/WriterObjects.py
View File

@@ -1,6 +1,12 @@
from __future__ import division, print_function
import numpy as np
from CPIncomp.BaseObjects import IncompressibleData
import CPIncomp.DataObjects as DO
import CPIncomp.CoefficientObjects as CO
from CoolProp.CoolProp import FluidsList
import CoolProp.CoolProp as CP
class SolutionDataWriter(object):
"""
@@ -15,82 +21,111 @@ class SolutionDataWriter(object):
def fitAll(self, data):
T = data.temperature.data
x = data.concentration.data
fluid = data.name
#if data.Tbase==0.0:
# data.Tbase = (np.min(T) + np.max(T)) / 2.0
#if data.xbase==0.0:
# data.xbase = (np.min(x) + np.max(x)) / 2.0
if data.Tbase==0.0:
data.Tbase = (np.min(T) + np.max(T)) / 2.0
if data.xbase==0.0:
data.xbase = (np.min(x) + np.max(x)) / 2.0
# Set the standard order for polynomials
std_xorder = 3
std_yorder = 5
std_xorder = 3+1
std_yorder = 5+1
std_coeffs = np.zeros((std_xorder,std_yorder))
errList = (ValueError, AttributeError, TypeError, RuntimeError)
try:
data.density.coeffs = np.copy(std_coeffs)
data.density.type = data.density.INCOMPRESSIBLE_POLYNOMIAL
data.density.fit(T,x,data.Tbase,data.xbase)
except errList as ve:
if self.verbose: print("Could not fit density coefficients:", ve)
pass
try:
data.specific_heat.coeffs = np.copy(std_coeffs)
data.specific_heat.type = data.specific_heat.INCOMPRESSIBLE_POLYNOMIAL
data.specific_heat.fit(T,x,data.Tbase,data.xbase)
except errList as ve:
if self.verbose: print("Could not fit specific heat coefficients:", ve)
pass
try:
data.viscosity.coeffs = np.copy(std_coeffs)
data.viscosity.type = data.viscosity.INCOMPRESSIBLE_EXPPOLYNOMIAL
data.viscosity.fit(T,x,data.Tbase,data.xbase)
except errList as ve:
if self.verbose: print("Could not fit viscosity coefficients:", ve)
pass
try:
data.conductivity.coeffs = np.copy(std_coeffs)
data.conductivity.type = data.conductivity.INCOMPRESSIBLE_POLYNOMIAL
data.conductivity.fit(T,x,data.Tbase,data.xbase)
except errList as ve:
if self.verbose: print("Could not fit conductivity coefficients:", ve)
pass
try:
data.saturation_pressure.coeffs = np.copy(std_coeffs)
data.saturation_pressure.type = data.saturation_pressure.INCOMPRESSIBLE_EXPPOLYNOMIAL
data.saturation_pressure.fit(T,x,data.Tbase,data.xbase)
except errList as ve:
if self.verbose: print("Could not fit saturation pressure coefficients:", ve)
pass
for name,entry in data.getPolyObjects().iteritems():
try:
entry.coeffs = np.copy(std_coeffs)
entry.type = entry.INCOMPRESSIBLE_POLYNOMIAL
entry.fitCoeffs(T,x,data.Tbase,data.xbase)
except errList as ve:
if self.verbose: print("{0}: Could not fit {1} coefficients: {2}".format(fluid,name,ve))
pass
for name,entry in data.getExpPolyObjects().iteritems():
try:
entry.coeffs = np.copy(std_coeffs)
entry.type = entry.INCOMPRESSIBLE_EXPPOLYNOMIAL
entry.fitCoeffs(T,x,data.Tbase,data.xbase)
except errList as ve:
if self.verbose: print("{0}: Could not fit {1} coefficients: {2}".format(fluid,name,ve))
pass
try:
data.T_freeze.coeffs = np.copy(std_coeffs)
data.T_freeze.type = data.T_freeze.INCOMPRESSIBLE_POLYNOMIAL
data.T_freeze.fit(0.0,x,0.0,data.xbase)
data.T_freeze.fitCoeffs(x,0.0,data.xbase,0.0)
except errList as ve:
if self.verbose: print("Could not fit TFreeze coefficients:", ve)
if self.verbose: print("{0}: Could not fit {1} coefficients: {2}".format(fluid,"T_freeze",ve))
pass
try:
data.volume2mass.coeffs = np.copy(std_coeffs)
data.volume2mass.type = data.volume2mass.INCOMPRESSIBLE_POLYNOMIAL
data.volume2mass.fit(T,x,data.Tbase,data.xbase)
except errList as ve:
if self.verbose: print("Could not fit V2M coefficients:", ve)
pass
try:
data.mass2mole.coeffs = np.copy(std_coeffs)
data.mass2mole.type = data.mass2mole.INCOMPRESSIBLE_POLYNOMIAL
data.mass2mole.fit(T,x,data.Tbase,data.xbase)
except errList as ve:
if self.verbose: print("Could not fit M2M coefficients:", ve)
pass
# try:
# data.density.coeffs = np.copy(std_coeffs)
# data.density.type = data.density.INCOMPRESSIBLE_POLYNOMIAL
# data.density.fit(T,x,data.Tbase,data.xbase)
# except errList as ve:
# if self.verbose: print(name, ": Could not fit density coefficients: ", ve)
# pass
#
# try:
# data.specific_heat.coeffs = np.copy(std_coeffs)
# data.specific_heat.type = data.specific_heat.INCOMPRESSIBLE_POLYNOMIAL
# data.specific_heat.fit(T,x,data.Tbase,data.xbase)
# except errList as ve:
# if self.verbose: print(name, ": Could not fit specific heat coefficients: ", ve)
# pass
#
# try:
# data.viscosity.coeffs = np.copy(std_coeffs)
# data.viscosity.type = data.viscosity.INCOMPRESSIBLE_EXPPOLYNOMIAL
# data.viscosity.fit(T,x,data.Tbase,data.xbase)
# except errList as ve:
# if self.verbose: print(name, ": Could not fit viscosity coefficients: ", ve)
# pass
#
# try:
# data.conductivity.coeffs = np.copy(std_coeffs)
# data.conductivity.type = data.conductivity.INCOMPRESSIBLE_POLYNOMIAL
# data.conductivity.fit(T,x,data.Tbase,data.xbase)
# except errList as ve:
# if self.verbose: print(name, ": Could not fit conductivity coefficients: ", ve)
# pass
#
# try:
# data.saturation_pressure.coeffs = np.copy(std_coeffs)
# data.saturation_pressure.type = data.saturation_pressure.INCOMPRESSIBLE_EXPPOLYNOMIAL
# data.saturation_pressure.fit(T,x,data.Tbase,data.xbase)
# except errList as ve:
# if self.verbose: print(name, ": Could not fit saturation pressure coefficients: ", ve)
# pass
#
# try:
# data.T_freeze.coeffs = np.copy(std_coeffs)
# data.T_freeze.type = data.T_freeze.INCOMPRESSIBLE_POLYNOMIAL
# data.T_freeze.fit(0.0,x,0.0,data.xbase)
# except errList as ve:
# if self.verbose: print(name, ": Could not fit TFreeze coefficients: ", ve)
# pass
# try:
# data.volume2mass.coeffs = np.copy(std_coeffs)
# data.volume2mass.type = data.volume2mass.INCOMPRESSIBLE_POLYNOMIAL
# data.volume2mass.fit(T,x,data.Tbase,data.xbase)
# except errList as ve:
# if self.verbose: print(name, ": Could not fit V2M coefficients: ", ve)
# pass
#
# try:
# data.mass2mole.coeffs = np.copy(std_coeffs)
# data.mass2mole.type = data.mass2mole.INCOMPRESSIBLE_POLYNOMIAL
# data.mass2mole.fit(T,x,data.Tbase,data.xbase)
# except errList as ve:
# if self.verbose: print(name, ": Could not fit M2M coefficients: ", ve)
# pass
def toJSON(self,data):
@@ -132,4 +167,67 @@ class SolutionDataWriter(object):
fp = open(jobj['name']+'.json', 'w')
fp.write(dump)
fp.close()
fp.close()
#class FitGraphWriter(object):
# """
# A base class that defines all the variables needed
# in order to make a proper fit. You can copy this code
# put in your data and add some documentation for where the
# information came from.
# """
# def __init__(self):
# self.verbose = True
#
# def inCoolProp(self,name):
# #print FluidsList()
# result = name in FluidsList()
# if not result:
# try:
# CP.PropsU('Tmin','T',0,'P',0,name,"SI")
# return True
# except ValueError as e:
# print e
# return False
#
# def getFluidList(self):
# containerList = []
## containerList += [TherminolD12()]
## containerList += [TherminolVP1(), Therminol66(), Therminol72()]
## containerList += [DowthermJ(), DowthermQ()]
## containerList += [Texatherm22(), NitrateSalt(), SylthermXLT()]
## containerList += [HC50(), HC40(), HC30(), HC20(), HC10()]
## containerList += [AS10(), AS20(), AS30(), AS40(), AS55()]
## containerList += [ZS10(), ZS25(), ZS40(), ZS45(), ZS55()]
# return containerList
#
# def relError(A=[],B=[],PCT=False):
# """
# Returns the relative Error from (A-B)/B, if PCT is True, it returns percent.
# """
# result = (np.array(A)-np.array(B))/np.array(B);
# if PCT:
# return result * 100.
# else:
# return result
#
#
# def makePlots(self, fluid):
# # row and column sharing for test plots
# #matplotlib.pyplot.subplots_adjust(top=0.85)
# f, ((ax1, ax2), (ax3, ax4), (ax5, ax6)) = matplotlib.pyplot.subplots(3, 2, sharex='col')
# f.set_size_inches(matplotlib.pyplot.figaspect(1.2)*1.5)
# #f.suptitle("Fit for "+str(data.Desc), fontsize=14)
#
## ### This is the actual fitting
# tData = data.T
# tDat1 = numpy.linspace(numpy.min(tData)+1, numpy.max(tData)-1, 10)
# Pin = 1e20 # Dummy pressure
# inCP =liqObj.inCoolProp(data.Name)
# print "Fluid in CoolProp: "+str(inCP)
# print

107
dev/incompressible_liquids/ExamplePure.json
View File

@@ -1,26 +1,25 @@
{
"T_freeze": {
"coeffs": [
[
0.000000000000000e+00
]
],
"type": "polynomial"
"coeffs": "null",
"type": "notdefined"
},
"Tbase": 0.000000000000000e+00,
"Tbase": 3.731500000000000e+02,
"Tmax": 4.231500000000000e+02,
"Tmin": 3.231500000000000e+02,
"TminPsat": 4.231500000000000e+02,
"conductivity": {
"coeffs": [
[
1.480970952797216e-01
9.648018648018647e-02
],
[
-9.483682983683419e-05
-1.800893550893292e-04
],
[
-1.165501165501257e-07
-1.165501165501155e-07
],
[
-9.712509712375223e-10
]
],
"type": "polynomial"
@@ -28,56 +27,40 @@
"density": {
"coeffs": [
[
9.188509110139944e+02
7.024452214452213e+02
],
[
-3.798857808858156e-01
-7.703185703184596e-01
],
[
-5.361305361305231e-04
-5.361305361305524e-04
],
[
-5.439005438915205e-06
]
],
"type": "polynomial"
},
"description": "Heat transfer fluid TherminolD12 by Solutia",
"mass2mole": {
"coeffs": [
[
0.000000000000000e+00,
0.000000000000000e+00,
0.000000000000000e+00,
0.000000000000000e+00,
0.000000000000000e+00
],
[
0.000000000000000e+00,
0.000000000000000e+00,
0.000000000000000e+00,
0.000000000000000e+00,
0.000000000000000e+00
],
[
0.000000000000000e+00,
0.000000000000000e+00,
0.000000000000000e+00,
0.000000000000000e+00,
0.000000000000000e+00
]
],
"type": "polynomial"
"coeffs": "null",
"type": "notdefined"
},
"name": "ExamplePure",
"reference": "Solutia data sheet",
"saturation_pressure": {
"coeffs": [
[
-3.373625698981662e+01
1.758406480398250e+00
],
[
1.484757141205827e-01
4.163045637749924e-02
],
[
-1.429827445582900e-04
-1.429827445583037e-04
],
[
7.709858382251525e-08
]
],
"type": "exppolynomial"
@@ -85,13 +68,16 @@
"specific_heat": {
"coeffs": [
[
6.291029323425564e+02
2.446529137529137e+03
],
[
5.644631701632086e+00
4.096017871017279e+00
],
[
-2.074592074592690e-03
-2.074592074591486e-03
],
[
1.942501940163629e-07
]
],
"type": "polynomial"
@@ -99,42 +85,23 @@
"viscosity": {
"coeffs": [
[
6.734078202541530e+00
-7.952555049439570e-01
],
[
-3.030175157911949e-02
-9.663911227114340e-03
],
[
2.713113951297678e-05
2.713113951297377e-05
],
[
-2.190286143086986e-07
]
],
"type": "exppolynomial"
},
"volume2mass": {
"coeffs": [
[
0.000000000000000e+00,
0.000000000000000e+00,
0.000000000000000e+00,
0.000000000000000e+00,
0.000000000000000e+00
],
[
0.000000000000000e+00,
0.000000000000000e+00,
0.000000000000000e+00,
0.000000000000000e+00,
0.000000000000000e+00
],
[
0.000000000000000e+00,
0.000000000000000e+00,
0.000000000000000e+00,
0.000000000000000e+00,
0.000000000000000e+00
]
],
"type": "polynomial"
"coeffs": "null",
"type": "notdefined"
},
"xbase": 0.000000000000000e+00,
"xmax": 1.000000000000000e+00,

221
dev/incompressible_liquids/ExampleSolution.json
View File

@@ -1,80 +1,47 @@
{
"T_freeze": {
"coeffs": [
[
0.000000000000000e+00,
0.000000000000000e+00,
0.000000000000000e+00,
0.000000000000000e+00,
0.000000000000000e+00
],
[
0.000000000000000e+00,
0.000000000000000e+00,
0.000000000000000e+00,
0.000000000000000e+00,
0.000000000000000e+00
],
[
0.000000000000000e+00,
0.000000000000000e+00,
0.000000000000000e+00,
0.000000000000000e+00,
0.000000000000000e+00
]
],
"type": "polynomial"
"coeffs": "null",
"type": "notdefined"
},
"Tbase": 0.000000000000000e+00,
"Tbase": 2.456500000000000e+02,
"Tmax": 2.631500000000000e+02,
"Tmin": 2.281500000000000e+02,
"TminPsat": 2.631500000000000e+02,
"conductivity": {
"coeffs": [
[
0.000000000000000e+00,
0.000000000000000e+00,
0.000000000000000e+00,
0.000000000000000e+00,
0.000000000000000e+00
],
[
0.000000000000000e+00,
0.000000000000000e+00,
0.000000000000000e+00,
0.000000000000000e+00,
0.000000000000000e+00
],
[
0.000000000000000e+00,
0.000000000000000e+00,
0.000000000000000e+00,
0.000000000000000e+00,
0.000000000000000e+00
]
],
"type": "polynomial"
"coeffs": "null",
"type": "notdefined"
},
"density": {
"coeffs": [
[
-2.399430982040804e+02,
1.250815901394215e+03,
4.235073668359494e+01,
-1.816072631001761e+02,
3.787878787862606e+01
1.026080708874463e+03,
-1.590170634919809e+02,
2.363230519487098e+01,
-5.555555556031459e+01,
3.787878787435842e+01,
1.500000000001633e+03
],
[
1.185772755113077e+01,
-1.350134561596795e+01,
7.888548752001263e-01,
5.820105820110935e-01,
-8.565823197961835e-01,
1.584394540610013e+00,
-4.885161135173223e-01,
5.820105820079813e-01,
-1.298701298673707e+00,
0.000000000000000e+00
],
[
-2.674489795948098e-02,
3.140022675718737e-02,
-3.287981859254933e-03,
-2.059637188207991e-02,
2.962207105057909e-02,
-3.287981859486457e-03,
2.645502645515989e-02,
0.000000000000000e+00,
0.000000000000000e+00
],
[
-2.976430976448491e-04,
3.477633480047034e-04,
1.827801827859139e-04,
0.000000000000000e+00,
0.000000000000000e+00,
0.000000000000000e+00
]
@@ -83,138 +50,28 @@
},
"description": "Ethanol ice slurry",
"mass2mole": {
"coeffs": [
[
0.000000000000000e+00,
0.000000000000000e+00,
0.000000000000000e+00,
0.000000000000000e+00,
0.000000000000000e+00
],
[
0.000000000000000e+00,
0.000000000000000e+00,
0.000000000000000e+00,
0.000000000000000e+00,
0.000000000000000e+00
],
[
0.000000000000000e+00,
0.000000000000000e+00,
0.000000000000000e+00,
0.000000000000000e+00,
0.000000000000000e+00
]
],
"type": "polynomial"
"coeffs": "null",
"type": "notdefined"
},
"name": "ExampleSolution",
"reference": "SecCool software",
"saturation_pressure": {
"coeffs": [
[
0.000000000000000e+00,
0.000000000000000e+00,
0.000000000000000e+00,
0.000000000000000e+00,
0.000000000000000e+00
],
[
0.000000000000000e+00,
0.000000000000000e+00,
0.000000000000000e+00,
0.000000000000000e+00,
0.000000000000000e+00
],
[
0.000000000000000e+00,
0.000000000000000e+00,
0.000000000000000e+00,
0.000000000000000e+00,
0.000000000000000e+00
]
],
"type": "exppolynomial"
"coeffs": "null",
"type": "notdefined"
},
"specific_heat": {
"coeffs": [
[
0.000000000000000e+00,
0.000000000000000e+00,
0.000000000000000e+00,
0.000000000000000e+00,
0.000000000000000e+00
],
[
0.000000000000000e+00,
0.000000000000000e+00,
0.000000000000000e+00,
0.000000000000000e+00,
0.000000000000000e+00
],
[
0.000000000000000e+00,
0.000000000000000e+00,
0.000000000000000e+00,
0.000000000000000e+00,
0.000000000000000e+00
]
],
"type": "polynomial"
"coeffs": "null",
"type": "notdefined"
},
"viscosity": {
"coeffs": [
[
0.000000000000000e+00,
0.000000000000000e+00,
0.000000000000000e+00,
0.000000000000000e+00,
0.000000000000000e+00
],
[
0.000000000000000e+00,
0.000000000000000e+00,
0.000000000000000e+00,
0.000000000000000e+00,
0.000000000000000e+00
],
[
0.000000000000000e+00,
0.000000000000000e+00,
0.000000000000000e+00,
0.000000000000000e+00,
0.000000000000000e+00
]
],
"type": "exppolynomial"
"coeffs": "null",
"type": "notdefined"
},
"volume2mass": {
"coeffs": [
[
0.000000000000000e+00,
0.000000000000000e+00,
0.000000000000000e+00,
0.000000000000000e+00,
0.000000000000000e+00
],
[
0.000000000000000e+00,
0.000000000000000e+00,
0.000000000000000e+00,
0.000000000000000e+00,
0.000000000000000e+00
],
[
0.000000000000000e+00,
0.000000000000000e+00,
0.000000000000000e+00,
0.000000000000000e+00,
0.000000000000000e+00
]
],
"type": "polynomial"
"coeffs": "null",
"type": "notdefined"
},
"xbase": 0.000000000000000e+00,
"xbase": 2.000000000000000e-01,
"xmax": 1.000000000000000e+00,
"xmin": 0.000000000000000e+00
}

285
dev/incompressible_liquids/all_incompressibles.py
View File

@@ -1,17 +1,112 @@
from __future__ import division, absolute_import, print_function
import inspect
import numpy as np
import itertools,scipy.interpolate
import CoolProp.CoolProp as CP
import CPIncomp.PureFluids
import CPIncomp.DataObjects
from CPIncomp.WriterObjects import SolutionDataWriter
from CPIncomp.DataObjects import PureExample, SolutionExample
from CPIncomp.CoefficientObjects import SecCoolExample, MelinderExample
def getExampleData():
return [PureExample(), SolutionExample()]
if __name__ == '__main__':
def getExampleCoef():
return [SecCoolExample(), MelinderExample()]
def getExampleObjects():
return getExampleData() + getExampleCoef()
def getBaseClassNames():
ignList = []
for i in inspect.getmembers(CPIncomp.DataObjects):
ignList.append(i[0])
return ignList
def getPureDataObjects():
classes = []
ignList = getBaseClassNames()
for name, obj in inspect.getmembers(CPIncomp.PureFluids):
if inspect.isclass(obj):
#print(name)
if not name in ignList: # Ignore the base classes
classes += [obj()]
return classes
def getSolutionDataObjects():
return []
classes = []
ignList = getBaseClassNames()
for name, obj in inspect.getmembers(CPIncomp.SolutionFluids):
if inspect.isclass(obj):
#print(name)
if not name in ignList: # Ignore the base classes
classes += [obj()]
return classes
def getCoefficientObjects():
return []
classes = []
ignList = getBaseClassNames()
for name, obj in inspect.getmembers(CPIncomp.CoefficientFluids):
if inspect.isclass(obj):
#print(name)
if not name in ignList: # Ignore the base classes
classes += [obj()]
return classes
def fitFluidList(fluidObjs):
for obj in fluidObjs:
if obj==fluidObjs[0]:
print(" {0}".format(obj.name), end="")
elif obj==fluidObjs[-1]:
print(", {0}".format(obj.name), end="")
else:
print(", {0}".format(obj.name), end="")
try:
writer.fitAll(obj)
except (TypeError, ValueError) as e:
print("An error occurred for fluid: {0}".format(obj.name))
print(obj)
print(e)
pass
return
if __name__ == '__main__':
writer = SolutionDataWriter()
dataObjs = getExampleData()
for obj in dataObjs:
writer.fitAll(obj)
dataObjs += getExampleCoef()
for obj in dataObjs:
writer.toJSON(obj)
# If the examples did not cause any errors,
# we can proceed to the real data.
dataObjs = getPureDataObjects()
print("Fitting pure fluids:".format(obj.name), end="")
fitFluidList(dataObjs)
for obj in dataObjs:
writer.toJSON(obj)
dataObjs = getPureDataObjects()
print("Fitting solutions:".format(obj.name), end="")
fitFluidList(dataObjs)
for obj in dataObjs:
writer.toJSON(obj)
# data = SecCoolExample()
# writer.toJSON(data)
@@ -40,99 +135,99 @@ if __name__ == '__main__':
# print data.density.data[1][1]
# print np.polynomial.polynomial.polyval2d(data.temperature.data[1], data.concentration.data[1], data.density.coeffs)
test = True
#if test: import CoolProp.CoolProp as CP
if test: from scipy import interpolate
if test: p = 10e5
def printInfo(data):
print("{0:s} : {1:.4e}, {2:.4e}".format(data.name, data.Tbase, data.xbase))
def printValue(data, T, p, x, fluid='', f=None, dataFunc=None, dataLetter=''):
if f!=None:
try:
print("{0:s} : {7:s} : {1:.4e}, {2:.4e}, {3:.4e}, inputs: {4:.4e}, {5:.4e}, {6:.4e} ".format(data.name, dataFunc(T, p, x), CP.PropsSI(dataLetter,'T',T,'P',p,fluid), float(f(T,x)), T, p, x, dataLetter))
except:
print("{0:s} : {7:s} : {1:.4e}, {2:.4e}, {3:.4e}, inputs: {4:.4e}, {5:.4e}, {6:.4e} ".format(data.name, dataFunc(T, p, x), CP.PropsSI(dataLetter,'T',T,'P',p,fluid), float(f(T)), T, p, x, dataLetter))
else:
print ("{0:s} : {7:s} : {1:.4e}, {2:.4e}, {3:.4e}, inputs: {4:.4e}, {5:.4e}, {6:.4e} ".format(data.name, dataFunc(T, p, x), CP.PropsSI(dataLetter,'T',T,'P',p,fluid), 0.0, T, p, x, dataLetter))
def printDens(data, T, p, x, fluid='', f=None):
printValue(data, T, p, x, fluid=fluid, f=f, dataFunc=data.rho, dataLetter='D')
def printHeat(data, T, p, x, fluid='', f=None):
printValue(data, T, p, x, fluid=fluid, f=f, dataFunc=data.c, dataLetter='C')
def printEnergy(data, T, p, x, fluid='', f=None):
printValue(data, T, p, x, fluid=fluid, f=f, dataFunc=data.u, dataLetter='U')
def printEnthalpy(data, T, p, x, fluid='', f=None):
printValue(data, T, p, x, fluid=fluid, f=f, dataFunc=data.h, dataLetter='H')
def printVisc(data, T, p, x, fluid='', f=None):
printValue(data, T, p, x, fluid=fluid, f=f, dataFunc=data.visc, dataLetter='V')
def printCond(data, T, p, x, fluid='', f=None):
printValue(data, T, p, x, fluid=fluid, f=f, dataFunc=data.cond, dataLetter='L')
def printPsat(data, T, p, x, fluid='', f=None):
printValue(data, T, p, x, fluid=fluid, f=f, dataFunc=data.psat, dataLetter='Psat')
def printTfreeze(data, T, p, x, fluid='', f=None):
printValue(data, T, p, x, fluid=fluid, f=f, dataFunc=data.Tfreeze, dataLetter='Tfreeze')
def printAll(data, T, p, x, fluid=''):
printDens(data, T, p, x, fluid, None)
printHeat(data, T, p, x, fluid, None)
printEnergy(data, T, p, x, fluid, None)
#printEnthalpy(data, T, p, x, fluid, None)
printVisc(data, T, p, x, fluid, None)
printCond(data, T, p, x, fluid, None)
#printPsat(data, T, p, x, fluid, None)
#printTfreeze(data, T, p, x, fluid, None)
data = PureExample()
writer.fitAll(data)
writer.toJSON(data)
#printInfo(data)
if test: T = 55+273.15
if test: x = 0.0
#if test: f = interpolate.interp1d(data.temperature.data, data.density.data.T[0])
#if test: printDens(data, T, p, x, fluid='TD12', f=f)
#if test: f = interpolate.interp1d(data.temperature.data, data.specific_heat.data.T[0])
#if test: printHeat(data, T, p, x, fluid='TD12', f=f)
if test: printAll(data, T, p, x, fluid='TD12')
data = SolutionExample()
writer.fitAll(data)
writer.toJSON(data)
#printInfo(data)
if test: T = -15+273.15
if test: x = 0.10
#if test: f = interpolate.interp2d(data.temperature.data, data.concentration.data, data.density.data.T)
#if test: printDens(data, T, p, x, fluid='IceEA-{0:.4f}%'.format(x*100.0), f=f)
#if test: f = None
if test: printAll(data, T, p, x, fluid='IceEA-{0:.4f}%'.format(x*100.0))
data = SecCoolExample()
writer.toJSON(data)
#printInfo(data)
if test: T = -5+273.15
if test: x = 0.40
if test: f = None #interpolate.interp2d(data.temperature.data, data.concentration.data, data.density.data.T)
if test: printAll(data, T, p, x, fluid='SecCoolSolution-{0:.4f}%'.format(x*100.0))
data = MelinderExample()
writer.toJSON(data)
#printInfo(data)
if test: T = -5+273.15
if test: x = 0.3
if test: f = None #interpolate.interp2d(data.temperature.data, data.concentration.data, data.density.data.T)
if test: printAll(data, T, p, x, fluid='MMA-{0:.4f}%'.format(x*100.0))
# test = True
# #if test: import CoolProp.CoolProp as CP
# if test: from scipy import interpolate
# if test: p = 10e5
#
# def printInfo(data):
# print("{0:s} : {1:.4e}, {2:.4e}".format(data.name, data.Tbase, data.xbase))
#
# def printValue(data, T, p, x, fluid='', f=None, dataFunc=None, dataLetter=''):
# if f!=None:
# try:
# print("{0:s} : {7:s} : {1:.4e}, {2:.4e}, {3:.4e}, inputs: {4:.4e}, {5:.4e}, {6:.4e} ".format(data.name, dataFunc(T, p, x), CP.PropsSI(dataLetter,'T',T,'P',p,fluid), float(f(T,x)), T, p, x, dataLetter))
# except:
# print("{0:s} : {7:s} : {1:.4e}, {2:.4e}, {3:.4e}, inputs: {4:.4e}, {5:.4e}, {6:.4e} ".format(data.name, dataFunc(T, p, x), CP.PropsSI(dataLetter,'T',T,'P',p,fluid), float(f(T)), T, p, x, dataLetter))
# else:
# print ("{0:s} : {7:s} : {1:.4e}, {2:.4e}, {3:.4e}, inputs: {4:.4e}, {5:.4e}, {6:.4e} ".format(data.name, dataFunc(T, p, x), CP.PropsSI(dataLetter,'T',T,'P',p,fluid), 0.0, T, p, x, dataLetter))
#
# def printDens(data, T, p, x, fluid='', f=None):
# printValue(data, T, p, x, fluid=fluid, f=f, dataFunc=data.rho, dataLetter='D')
#
# def printHeat(data, T, p, x, fluid='', f=None):
# printValue(data, T, p, x, fluid=fluid, f=f, dataFunc=data.c, dataLetter='C')
#
# def printEnergy(data, T, p, x, fluid='', f=None):
# printValue(data, T, p, x, fluid=fluid, f=f, dataFunc=data.u, dataLetter='U')
#
# def printEnthalpy(data, T, p, x, fluid='', f=None):
# printValue(data, T, p, x, fluid=fluid, f=f, dataFunc=data.h, dataLetter='H')
#
# def printVisc(data, T, p, x, fluid='', f=None):
# printValue(data, T, p, x, fluid=fluid, f=f, dataFunc=data.visc, dataLetter='V')
#
# def printCond(data, T, p, x, fluid='', f=None):
# printValue(data, T, p, x, fluid=fluid, f=f, dataFunc=data.cond, dataLetter='L')
#
# def printPsat(data, T, p, x, fluid='', f=None):
# printValue(data, T, p, x, fluid=fluid, f=f, dataFunc=data.psat, dataLetter='Psat')
#
# def printTfreeze(data, T, p, x, fluid='', f=None):
# printValue(data, T, p, x, fluid=fluid, f=f, dataFunc=data.Tfreeze, dataLetter='Tfreeze')
#
# def printAll(data, T, p, x, fluid=''):
# printDens(data, T, p, x, fluid, None)
# printHeat(data, T, p, x, fluid, None)
# printEnergy(data, T, p, x, fluid, None)
# #printEnthalpy(data, T, p, x, fluid, None)
# printVisc(data, T, p, x, fluid, None)
# printCond(data, T, p, x, fluid, None)
# #printPsat(data, T, p, x, fluid, None)
# #printTfreeze(data, T, p, x, fluid, None)
#
#
#
#
# data = PureExample()
# writer.fitAll(data)
# writer.toJSON(data)
# #printInfo(data)
# if test: T = 55+273.15
# if test: x = 0.0
# #if test: f = interpolate.interp1d(data.temperature.data, data.density.data.T[0])
# #if test: printDens(data, T, p, x, fluid='TD12', f=f)
# #if test: f = interpolate.interp1d(data.temperature.data, data.specific_heat.data.T[0])
# #if test: printHeat(data, T, p, x, fluid='TD12', f=f)
# if test: printAll(data, T, p, x, fluid='TD12')
#
#
# data = SolutionExample()
# writer.fitAll(data)
# writer.toJSON(data)
# #printInfo(data)
# if test: T = -15+273.15
# if test: x = 0.10
# #if test: f = interpolate.interp2d(data.temperature.data, data.concentration.data, data.density.data.T)
# #if test: printDens(data, T, p, x, fluid='IceEA-{0:.4f}%'.format(x*100.0), f=f)
# #if test: f = None
# if test: printAll(data, T, p, x, fluid='IceEA-{0:.4f}%'.format(x*100.0))
#
#
# data = SecCoolExample()
# writer.toJSON(data)
# #printInfo(data)
# if test: T = -5+273.15
# if test: x = 0.40
# if test: f = None #interpolate.interp2d(data.temperature.data, data.concentration.data, data.density.data.T)
# if test: printAll(data, T, p, x, fluid='SecCoolSolution-{0:.4f}%'.format(x*100.0))
#
# data = MelinderExample()
# writer.toJSON(data)
# #printInfo(data)
# if test: T = -5+273.15
# if test: x = 0.3
# if test: f = None #interpolate.interp2d(data.temperature.data, data.concentration.data, data.density.data.T)
# if test: printAll(data, T, p, x, fluid='MMA-{0:.4f}%'.format(x*100.0))
#

40
include/IncompressibleFluid.h
View File

@@ -64,13 +64,53 @@ protected:
double uref, rhoref;
double xbase, Tbase;
/// These are the objects that hold the coefficients
/** Note that all polynomials require a 2-dimensional array
* of coefficients. This array may have only one row or
* column, but the structure should be 2D. This behaviour is
* hard-coded in the JSON file reader that resides inside
* the IncompressibleLibrary.cpp
* All other functions, also polyoffset, can only handle 1D
* input and throw an error if you feed them other coefficients.
*/
/// Density coefficients
/** If 2D, the rows are temperature and the columns are concentration.
* If 1D, should be a column vector of temperature coefficients
*/
IncompressibleData density;
/// Specific heat coefficients
/** If 2D, the rows are temperature and the columns are concentration.
* If 1D, should be a column vector of temperature coefficients
* Fails for all other forms than polynomial due to the automatic
* integration for internal energy and entropy.
*/
IncompressibleData specific_heat;
/// Viscosity coefficients
/** If 2D, the rows are temperature and the columns are concentration.
* If 1D, should be a column vector of temperature coefficients
*/
IncompressibleData viscosity;
/// Conductivity coefficients
/** If 2D, the rows are temperature and the columns are concentration.
* If 1D, should be a column vector of temperature coefficients
*/
IncompressibleData conductivity;
/// Saturation pressure coefficients
/** If 2D, the rows are temperature and the columns are concentration.
* If 1D, should be a column vector of temperature coefficients
*/
IncompressibleData p_sat;
/// Freezing temperature coefficients
/** If 2D, the rows are concentration and the columns are pressure.
* If 1D, should be a column vector of concentration coefficients
*/
IncompressibleData T_freeze;
/// Volume to mass fraction coefficients
/** Not implemented, yet */
IncompressibleData volToMass;
/// Mass to mole fraction coefficients
/** Not implemented, yet */
IncompressibleData massToMole;
Polynomial2DFrac poly;