diff --git a/dev/incompressible_liquids/CPIncomp/BaseObjects.py b/dev/incompressible_liquids/CPIncomp/BaseObjects.py
index aeb779db..8efb11a7 100644
--- a/dev/incompressible_liquids/CPIncomp/BaseObjects.py
+++ b/dev/incompressible_liquids/CPIncomp/BaseObjects.py
@@ -184,15 +184,15 @@ class IncompressibleData(object):
             raise ValueError("Unknown function.")           
         
 
-    def getCoeffs1d(self, x, z, order):
-        if (len(x)<order+1): 
-            raise ValueError("You have only {0} elements and try to fit {1} coefficients, please reduce the order.".format(len(x),order+1))
-        A = np.vander(x,order+1)[:,::-1]
-        #Anew = np.dot(A.T,A)
-        #znew = np.dot(A.T,z)
-        #coeffs = np.linalg.solve(Anew, znew)
-        coeffs, resids, rank, singulars  = np.linalg.lstsq(A, z)
-        return np.reshape(coeffs, (len(x),1))
+#    def getCoeffs1d(self, x, z, order):
+#        if (len(x)<order+1): 
+#            raise ValueError("You have only {0} elements and try to fit {1} coefficients, please reduce the order.".format(len(x),order+1))
+#        A = np.vander(x,order+1)[:,::-1]
+#        #Anew = np.dot(A.T,A)
+#        #znew = np.dot(A.T,z)
+#        #coeffs = np.linalg.solve(Anew, znew)
+#        coeffs, resids, rank, singulars  = np.linalg.lstsq(A, z)
+#        return np.reshape(coeffs, (len(x),1))
             
 
     def getCoeffs2d(self, x_in, y_in, z_in, x_order, y_order):
@@ -316,15 +316,14 @@ class IncompressibleData(object):
                 try:
                     #print func(xData, coeffs_start)
                     # Do the actual fitting
-                    popt, pcov = curve_fit(func, xData, yData, p0=coeffs_start)
-                    #print popt 
-                    #print pcov
+                    popt, pcov = curve_fit(func, xData, yData, p0=coeffs_start)                    
                     if np.any(popt!=coeffs_start):
                         success = True
 #                        print "Fit succeeded for "+fit[counter]+": "
 #                        print "data: {0}, func: {1}".format(yData[ 2],func(xData[ 2], popt))
 #                        print "data: {0}, func: {1}".format(yData[ 6],func(xData[ 6], popt))
 #                        print "data: {0}, func: {1}".format(yData[-1],func(xData[-1], popt))
+                        if self.DEBUG: print("Estimated covariance of parameters: ".format(pcov))
                         return popt
                     else:
                         print("Fit failed for "+fit[counter]+": ")
diff --git a/dev/incompressible_liquids/CPIncomp/MelinderFluids.py b/dev/incompressible_liquids/CPIncomp/MelinderFluids.py
new file mode 100644
index 00000000..2e5179a2
--- /dev/null
+++ b/dev/incompressible_liquids/CPIncomp/MelinderFluids.py
@@ -0,0 +1,60 @@
+import numpy as np
+from CPIncomp.CoefficientObjects import CoefficientData
+
+class MelinderExample(CoefficientData):
+    """ 
+    Methanol-Water mixture according to Melinder book
+    Source: Book
+    """ 
+    def __init__(self):
+        CoefficientData.__init__(self) 
+        self.name = "ExampleMelinder"
+        self.description = "Methanol solution"
+        self.reference = "Melinder-BOOK-2010"
+        self.Tmax =  40 + 273.15
+        self.Tmin = -50 + 273.15
+        self.xmax = 0.6
+        self.xmin = 0.0
+        self.TminPsat =  self.Tmax
+    
+        self.Tbase =   3.5359 + 273.15;
+        self.xbase =  30.5128 / 100.0
+       
+        coeffs = np.array([
+        [-26.29           , 958.1           ,3887           ,   0.4175            ,   1.153           ],
+        [ -0.000002575    ,  -0.4151        ,   7.201       ,   0.0007271         ,  -0.03866         ],
+        [ -0.000006732    ,  -0.002261      ,  -0.08979     ,   0.0000002823      ,   0.0002779       ],
+        [  0.000000163    ,   0.0000002998  ,  -0.000439    ,   0.000000009718    ,  -0.000001543     ],
+        [ -1.187          ,  -1.391         , -18.5         ,  -0.004421          ,   0.005448        ],
+        [ -0.00001609     ,  -0.0151        ,   0.2984      ,  -0.00002952        ,   0.0001008       ],
+        [  0.000000342    ,   0.0001113     ,  -0.001865    ,   0.00000007336     ,  -0.000002809     ],
+        [  0.0000000005687,  -0.0000003264  ,  -0.00001718  ,   0.0000000004328   ,   0.000000009811  ],
+        [ -0.01218        ,  -0.01105       ,  -0.03769     ,   0.00002044        ,  -0.0005552       ],
+        [  0.0000003865   ,   0.0001828     ,  -0.01196     ,   0.0000003413      ,   0.000008384     ],
+        [  0.000000008768 ,  -0.000001641   ,   0.00009801  ,  -0.000000003665    ,  -0.00000003997   ],
+        [ -0.0000000002095,   0.0000000151  ,   0.000000666 ,  -0.00000000002791  ,  -0.0000000003466 ],
+        [ -0.00006823     ,  -0.0001208     ,  -0.003776    ,   0.0000002943      ,   0.000003038     ],
+        [  0.00000002137  ,   0.000002992   ,  -0.00005611  ,  -0.0000000009646   ,  -0.00000007435   ],
+        [ -0.0000000004271,   0.000000001455,  -0.0000007811,   0.00000000003174  ,   0.0000000007442 ],
+        [  0.0000001297   ,   0.000004927   ,  -0.0001504   ,  -0.0000000008666   ,   0.00000006669   ],
+        [ -0.0000000005407,  -0.0000001325  ,   0.000007373 ,  -0.0000000000004573,  -0.0000000009105 ],
+        [  0.00000002363  ,  -0.00000007727 ,   0.000006433 ,  -0.0000000002033   ,  -0.0000000008472 ]
+        ])
+        
+        coeffs = self.convertMelinderMatrix(coeffs).T
+        
+        self.T_freeze.type = self.T_freeze.INCOMPRESSIBLE_POLYNOMIAL
+        self.T_freeze.coeffs = self.convertMelinderArray(coeffs[0])
+        
+        self.density.type = self.density.INCOMPRESSIBLE_POLYNOMIAL
+        self.density.coeffs = self.convertMelinderArray(coeffs[1])
+        
+        self.specific_heat.type = self.specific_heat.INCOMPRESSIBLE_POLYNOMIAL
+        self.specific_heat.coeffs = self.convertMelinderArray(coeffs[2])
+        
+        self.conductivity.type = self.conductivity.INCOMPRESSIBLE_POLYNOMIAL
+        self.conductivity.coeffs = self.convertMelinderArray(coeffs[3])
+        
+        self.viscosity.type = self.viscosity.INCOMPRESSIBLE_POLYNOMIAL
+        self.viscosity.coeffs = self.convertMelinderArray(coeffs[4])   
+
diff --git a/dev/incompressible_liquids/CPIncomp/PureFluids.py b/dev/incompressible_liquids/CPIncomp/PureFluids.py
index 14a77964..26d99312 100644
--- a/dev/incompressible_liquids/CPIncomp/PureFluids.py
+++ b/dev/incompressible_liquids/CPIncomp/PureFluids.py
@@ -9,18 +9,18 @@ class TherminolD12(PureData):
     """
     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.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.Tmin        = np.min(self.temperature.data)
+        self.Tmax        = np.max(self.temperature.data)
+        self.TminPsat    = self.Tmin
+        self.name        = "TD12" 
         self.description = "TherminolD12"
-        self.reference = "Therminol Heat Transfer Reference Disk"
+        self.reference   = "Therminol Heat Transfer Reference Disk"
         self.reshapeAll()
 
 class TherminolVP1(PureData):
@@ -29,18 +29,18 @@ class TherminolVP1(PureData):
     """
     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.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.Tmin        = np.min(self.temperature.data)
+        self.Tmax        = np.max(self.temperature.data)
+        self.TminPsat    = self.Tmin 
+        self.name        = "TVP1" 
         self.description = "TherminolVP1"
-        self.reference = "Therminol Heat Transfer Reference Disk"
+        self.reference   = "Therminol Heat Transfer Reference Disk"
         self.reshapeAll()
     
     
@@ -70,12 +70,12 @@ class Therminol66(PureData):
         # 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.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.reference = "Therminol Heat Transfer Reference Disk"
+        self.reference   = "Therminol Heat Transfer Reference Disk"
         self.reshapeAll()   
     
     
@@ -85,18 +85,18 @@ class Therminol72(PureData):
     """
     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.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.Tmin        = np.min(self.temperature.data)
+        self.Tmax        = np.max(self.temperature.data)
+        self.TminPsat    = self.Tmin 
+        self.name        = "T72" 
         self.description = "Therminol72"
-        self.reference = "Therminol Heat Transfer Reference Disk"
+        self.reference   = "Therminol Heat Transfer Reference Disk"
         self.reshapeAll()
     
     
@@ -107,18 +107,18 @@ class DowthermJ(PureData):
     """
     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.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.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.reference = "Dow Chemicals data sheet"
+        self.reference   = "Dow Chemicals data sheet"
         self.reshapeAll()
     
 class DowthermQ(PureData):
@@ -127,18 +127,18 @@ class DowthermQ(PureData):
     """
     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.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.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.reference = "Dow Chemicals data sheet"
+        self.reference   = "Dow Chemicals data sheet"
         self.reshapeAll()
     
     
@@ -148,18 +148,18 @@ class Texatherm22(PureData):
     """
     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.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.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.reference = "Texaco data sheet"
+        self.reference   = "Texaco data sheet"
         self.reshapeAll()
     
     
@@ -185,12 +185,12 @@ class NitrateSalt(PureData):
         # 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.Tmin        = np.min(self.temperature.data)
+        self.Tmax        = np.max(self.temperature.data)
+        self.TminPsat    = self.Tmax 
+        self.name        = "NaK" 
         self.description = "NitrateSalt"
-        self.reference = "Solar Power Tower Design Basis Document,  Alexis B. Zavoico, Sandia Labs, USA"
+        self.reference   = "Solar Power Tower Design Basis Document,  Alexis B. Zavoico, Sandia Labs, USA"
         self.reshapeAll()
     
 
@@ -200,17 +200,17 @@ class SylthermXLT(PureData):
     """    
     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.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.reference = "Dow Chemicals data sheet"
+        self.reference   = "Dow Chemicals data sheet"
         self.reshapeAll()
     
     
@@ -220,18 +220,18 @@ class HC50(PureData):
     """
     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.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.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.reference = "Dynalene data sheet"
+        self.reference   = "Dynalene data sheet"
         self.reshapeAll()
 
    
@@ -241,18 +241,18 @@ class HC40(PureData):
     """    
     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.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.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.reference = "Dynalene data sheet"
+        self.reference   = "Dynalene data sheet"
         self.reshapeAll()
 
     
@@ -262,18 +262,18 @@ class HC30(PureData):
     """    
     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.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.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.reference = "Dynalene data sheet"
+        self.reference   = "Dynalene data sheet"
         self.reshapeAll()
     
     
@@ -283,18 +283,18 @@ class HC20(PureData):
     """    
     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.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.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.reference = "Dynalene data sheet"
+        self.reference   = "Dynalene data sheet"
         self.reshapeAll()
     
 
@@ -310,12 +310,12 @@ class HC10(PureData):
         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.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.reference = "Dynalene data sheet"
+        self.reference   = "Dynalene data sheet"
         self.reshapeAll()
 
 
@@ -326,17 +326,17 @@ class AS10(PureData):
     """ 
     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.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.reference = "SecCool Software"
+        self.reference   = "SecCool Software"
         self.reshapeAll()
 
 
@@ -346,17 +346,17 @@ class AS20(PureData):
     """    
     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.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.reference = "SecCool Software"
+        self.reference   = "SecCool Software"
         self.reshapeAll()
     
 
@@ -366,17 +366,17 @@ class AS30(PureData):
     """
     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.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.reference = "SecCool Software"
+        self.reference   = "SecCool Software"
         self.reshapeAll()
 
 
@@ -386,17 +386,17 @@ class AS40(PureData):
     """   
     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.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.reference = "SecCool Software"
+        self.reference   = "SecCool Software"
         self.reshapeAll()
 
 
@@ -406,17 +406,17 @@ class AS55(PureData):
     """ 
     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.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.reference = "SecCool Software"
+        self.reference   = "SecCool Software"
         self.reshapeAll()
 
 
@@ -426,17 +426,17 @@ class ZS10(PureData):
     """ 
     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.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.reference = "SecCool Software"
+        self.reference   = "SecCool Software"
         self.reshapeAll()
 
 
@@ -446,17 +446,17 @@ class ZS25(PureData):
     """ 
     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.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.reference = "SecCool Software"
+        self.reference   = "SecCool Software"
         self.reshapeAll()
 
     
@@ -466,17 +466,17 @@ class ZS40(PureData):
     """ 
     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.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.reference = "SecCool Software"
+        self.reference   = "SecCool Software"
         self.reshapeAll()
 
 
@@ -486,17 +486,17 @@ class ZS45(PureData):
     """ 
     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.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.reference = "SecCool Software"
+        self.reference   = "SecCool Software"
         self.reshapeAll()
 
 
@@ -506,17 +506,17 @@ class ZS55(PureData):
     """ 
     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.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.reference = "SecCool Software"
+        self.reference   = "SecCool Software"
         self.reshapeAll()
 
 
diff --git a/dev/incompressible_liquids/all_incompressibles.py b/dev/incompressible_liquids/all_incompressibles.py
index d5d410df..ad3559d4 100644
--- a/dev/incompressible_liquids/all_incompressibles.py
+++ b/dev/incompressible_liquids/all_incompressibles.py
@@ -5,8 +5,11 @@ import itertools,scipy.interpolate
 
 import CoolProp.CoolProp as CP
 
-import CPIncomp.PureFluids
 import CPIncomp.DataObjects
+import CPIncomp.CoefficientObjects
+
+import CPIncomp.PureFluids
+import CPIncomp.MelinderFluids
 
 from CPIncomp.WriterObjects import SolutionDataWriter
 from CPIncomp.DataObjects import PureExample, SolutionExample
@@ -25,6 +28,8 @@ def getBaseClassNames():
     ignList = []
     for i in inspect.getmembers(CPIncomp.DataObjects):
         ignList.append(i[0])
+    for i in inspect.getmembers(CPIncomp.CoefficientObjects):
+        ignList.append(i[0])
     return ignList
 
 def getPureDataObjects():
@@ -55,7 +60,12 @@ def getCoefficientObjects():
     classes = []
     ignList = getBaseClassNames()
 
-    for name, obj in inspect.getmembers(CPIncomp.CoefficientFluids):
+    for name, obj in inspect.getmembers(CPIncomp.MelinderFluids):
+        if inspect.isclass(obj):
+            #print(name)
+            if not name in ignList: # Ignore the base classes
+                classes += [obj()]
+    for name, obj in inspect.getmembers(CPIncomp.SecCoolFluids):
         if inspect.isclass(obj):
             #print(name)
             if not name in ignList: # Ignore the base classes
diff --git a/include/IncompressibleFluid.h b/include/IncompressibleFluid.h
index 0cccd669..7446a84e 100644
--- a/include/IncompressibleFluid.h
+++ b/include/IncompressibleFluid.h
@@ -183,23 +183,23 @@ public:
 	 * be necessary, but gives a clearer structure.
 	 */
 	/// Density as a function of temperature, pressure and composition.
-	double rho (double T, double p, double x=0.0);
+	double rho (double T, double p, double x);
 	/// Heat capacities as a function of temperature, pressure and composition.
-	double c   (double T, double p, double x=0.0);
-	double cp  (double T, double p, double x=0.0){return c(T,p,x);};
-	double cv  (double T, double p, double x=0.0){return c(T,p,x);};
+	double c   (double T, double p, double x);
+	double cp  (double T, double p, double x){return c(T,p,x);};
+	double cv  (double T, double p, double x){return c(T,p,x);};
 	/// Entropy as a function of temperature, pressure and composition.
-	double s   (double T, double p, double x=0.0);
+	double s   (double T, double p, double x);
 	/// Internal energy as a function of temperature, pressure and composition.
-	double u   (double T, double p, double x=0.0);
+	double u   (double T, double p, double x);
 	/// Enthalpy as a function of temperature, pressure and composition.
-	double h   (double T, double p, double x=0.0);
+	double h   (double T, double p, double x);
 	/// Viscosity as a function of temperature, pressure and composition.
-	double visc(double T, double p, double x=0.0);
+	double visc(double T, double p, double x);
 	/// Thermal conductivity as a function of temperature, pressure and composition.
-	double cond(double T, double p, double x=0.0);
+	double cond(double T, double p, double x);
 	/// Saturation pressure as a function of temperature and composition.
-	double psat(double T,           double x=0.0);
+	double psat(double T,           double x);
 	/// Freezing temperature as a function of pressure and composition.
 	double Tfreeze(       double p, double x);
 	/// Conversion from volume-based to mass-based composition.
@@ -213,21 +213,21 @@ public:
 	 * done here, but in the backend, T(h,p) for example.
 	 */
 	/// Temperature as a function of density, pressure and composition.
-	double T_rho (double Dmass, double p, double x=0.0);
+	double T_rho (double Dmass, double p, double x);
 	/// Temperature as a function of heat capacities as a function of temperature, pressure and composition.
-	double T_c   (double Cmass, double p, double x=0.0);
+	double T_c   (double Cmass, double p, double x);
 	/// Temperature as a function of entropy as a function of temperature, pressure and composition.
-	double T_s   (double Smass, double p, double x=0.0);
+	double T_s   (double Smass, double p, double x);
 	/// Temperature as a function of internal energy as a function of temperature, pressure and composition.
-	double T_u   (double Umass, double p, double x=0.0);
+	double T_u   (double Umass, double p, double x);
 	/// Temperature as a function of enthalpy, pressure and composition.
-	double T_h   (double Hmass, double p, double x=0.0){throw NotImplementedError(format("%s (%d): T from enthalpy is not implemented in the fluid, use the backend.",__FILE__,__LINE__));}
+	double T_h   (double Hmass, double p, double x){throw NotImplementedError(format("%s (%d): T from enthalpy is not implemented in the fluid, use the backend.",__FILE__,__LINE__));}
 	/// Viscosity as a function of temperature, pressure and composition.
-	double T_visc(double  visc, double p, double x=0.0){throw NotImplementedError(format("%s (%d): T from viscosity is not implemented.",__FILE__,__LINE__));}
+	double T_visc(double  visc, double p, double x){throw NotImplementedError(format("%s (%d): T from viscosity is not implemented.",__FILE__,__LINE__));}
 	/// Thermal conductivity as a function of temperature, pressure and composition.
-	double T_cond(double  cond, double p, double x=0.0){throw NotImplementedError(format("%s (%d): T from conductivity is not implemented.",__FILE__,__LINE__));}
+	double T_cond(double  cond, double p, double x){throw NotImplementedError(format("%s (%d): T from conductivity is not implemented.",__FILE__,__LINE__));}
 	/// Saturation pressure as a function of temperature and composition.
-	double T_psat(double  psat,           double x=0.0){throw NotImplementedError(format("%s (%d): T from psat is not implemented.",__FILE__,__LINE__));}
+	double T_psat(double  psat,           double x){throw NotImplementedError(format("%s (%d): T from psat is not implemented.",__FILE__,__LINE__));}
 	/// Composition as a function of freezing temperature and pressure.
 	double x_Tfreeze(       double Tfreeze, double p){throw NotImplementedError(format("%s (%d): x from T_freeze is not implemented.",__FILE__,__LINE__));}
 
@@ -242,7 +242,7 @@ protected:
 	/** Calculate enthalpy as a function of temperature and
 	 *  pressure employing functions for internal energy and
 	 *  density. Provides consistent formulations. */
-	double h_u(double T, double p, double x=0.0) {
+	double h_u(double T, double p, double x) {
 		return u(T,p,x)+p/rho(T,p,x)-href;
 	};
 
@@ -250,7 +250,7 @@ protected:
 	/** Calculate internal energy as a function of temperature
 	 *  and pressure employing functions for enthalpy and
 	 *  density. Provides consistent formulations. */
-	double u_h(double T, double p, double x=0.0) {
+	double u_h(double T, double p, double x) {
 		return h(T,p,x)-p/rho(T,p,x)+href;
 	};
 
@@ -265,7 +265,7 @@ protected:
 	/** Compares the given temperature T to the result of a
 	 *  freezing point calculation. This is not necessarily
 	 *  defined for all fluids, default values do not cause errors. */
-	bool checkT(double T, double p, double x=0.0);
+	bool checkT(double T, double p, double x);
 
 	/// Check validity of pressure input.
 	/** Compares the given pressure p to the saturation pressure at
@@ -274,16 +274,16 @@ protected:
 	 *  The default value for psat is -1 yielding true if psat
 	 *  is not redefined in the subclass.
 	 *  */
-	bool checkP(double T, double p, double x=0.0);
+	bool checkP(double T, double p, double x);
 
 	/// Check validity of composition input.
 	/** Compares the given composition x to a stored minimum and
 	 *  maximum value. Enforces the redefinition of xmin and
 	 *  xmax since the default values cause an error. */
-	bool checkX(double x=0.0);
+	bool checkX(double x);
 
 	/// Check validity of temperature, pressure and composition input.
-	bool checkTPX(double T, double p, double x=0.0){
+	bool checkTPX(double T, double p, double x){
 		return (checkT(T,p,x) && checkP(T,p,x) && checkX(x));
 	};
 };
diff --git a/src/Backends/Incompressible/IncompressibleBackend.cpp b/src/Backends/Incompressible/IncompressibleBackend.cpp
index bf9a4577..67f51ba2 100644
--- a/src/Backends/Incompressible/IncompressibleBackend.cpp
+++ b/src/Backends/Incompressible/IncompressibleBackend.cpp
@@ -434,6 +434,111 @@ TEST_CASE("Internal consistency checks and example use cases for the incompressi
 		}
 
 
+		fluid = std::string("INCOMP::ExampleSecCool");
+		T   = -5  + 273.15;
+		p   = 10e5;
+		x   = 0.4;
+		std::vector<double> x_vec = std::vector<double>(1,x);
+
+		// Compare d
+		val = 9.4844e+02;
+		res = CoolProp::PropsSI("D","T",T,"P",p,fluid,x_vec);
+		{
+		CAPTURE(T);
+		CAPTURE(p);
+		CAPTURE(x);
+		CAPTURE(val);
+		CAPTURE(res);
+		CHECK( check_abs(val,res,acc) );
+		}
+
+		// Compare cp
+		val = 3.6304e+03;
+		res = CoolProp::PropsSI("C","T",T,"P",p,fluid,x_vec);
+		{
+		CAPTURE(T);
+		CAPTURE(p);
+		CAPTURE(x);
+		CAPTURE(val);
+		CAPTURE(res);
+		CHECK( check_abs(val,res,acc) );
+		}
+
+		fluid = std::string("INCOMP::ExamplePure");
+		T   = +55  + 273.15;
+		p   = 10e5;
+
+		// Compare d
+		val = 7.3646e+02;
+		res = CoolProp::PropsSI("D","T",T,"P",p,fluid);
+		{
+		CAPTURE(T);
+		CAPTURE(p);
+		CAPTURE(x);
+		CAPTURE(val);
+		CAPTURE(res);
+		CHECK( check_abs(val,res,acc) );
+		}
+
+		// Compare cp
+		val = 2.2580e+03;
+		res = CoolProp::PropsSI("C","T",T,"P",p,fluid);
+		{
+		CAPTURE(T);
+		CAPTURE(p);
+		CAPTURE(x);
+		CAPTURE(val);
+		CAPTURE(res);
+		CHECK( check_abs(val,res,acc) );
+		}
+	}
+
+
+	SECTION("Tests for the hardcoded fluids") {
+
+		// Prepare the results and compare them to the calculated values
+		std::string fluid("INCOMP::LiBr");
+		double acc = 0.0001;
+		double T   =  50 + 273.15;
+		double p   = 10e5;
+		double x   = 0.3;
+		double val = 0;
+		double res = 0;
+
+		// Compare different inputs
+		// ... as vector
+		val = 9.6212e+02;
+		res = CoolProp::PropsSI("D","T",T,"P",p,fluid,std::vector<double>(1,x));
+		{
+		CAPTURE(T);
+		CAPTURE(p);
+		CAPTURE(x);
+		CAPTURE(val);
+		CAPTURE(res);
+		CHECK( check_abs(val,res,acc) );
+		}
+		// ... as %
+		res = CoolProp::PropsSI("D","T",T,"P",p,fluid+format("-%f%s",x*100.0,"%"));
+		{
+		CAPTURE(T);
+		CAPTURE(p);
+		CAPTURE(x);
+		CAPTURE(val);
+		CAPTURE(res);
+		CHECK( check_abs(val,res,acc) );
+		}
+		// ... as mass fraction
+		res = CoolProp::PropsSI("D","T",T,"P",p,fluid+format("[%f]",x));
+		{
+		CAPTURE(T);
+		CAPTURE(p);
+		CAPTURE(x);
+		CAPTURE(val);
+		CAPTURE(res);
+		CHECK( check_abs(val,res,acc) );
+		}
+
+
 		fluid = std::string("INCOMP::ExampleSecCool");
 		T   = -5  + 273.15;
 		p   = 10e5;
diff --git a/src/Backends/Incompressible/IncompressibleFluid.cpp b/src/Backends/Incompressible/IncompressibleFluid.cpp
index 4ea25566..0d6b3ee7 100644
--- a/src/Backends/Incompressible/IncompressibleFluid.cpp
+++ b/src/Backends/Incompressible/IncompressibleFluid.cpp
@@ -583,12 +583,13 @@ TEST_CASE("Internal consistency checks and example use cases for the incompressi
 		double acc = 0.0001;
 		double T = 273.15+50;
 		double p = 10e5;
+		double x = xref;
 		double val = 0;
 		double res = 0;
 
 		// Compare density
 		val = 824.4615702148608;
-		res = XLT.rho(T,p);
+		res = XLT.rho(T,p,x);
 		{
 		CAPTURE(T);
 		CAPTURE(val);
@@ -598,7 +599,7 @@ TEST_CASE("Internal consistency checks and example use cases for the incompressi
 
 		// Compare cp
 		val = 1834.7455527670554;
-		res = XLT.c(T,p);
+		res = XLT.c(T,p,x);
 		{
 		CAPTURE(T);
 		CAPTURE(val);
@@ -608,7 +609,7 @@ TEST_CASE("Internal consistency checks and example use cases for the incompressi
 
 		// Compare s
 		val = 145.59157247249246;
-		res = XLT.s(T,p);
+		res = XLT.s(T,p,x);
 		{
 		CAPTURE(T);
 		CAPTURE(val);
@@ -617,7 +618,7 @@ TEST_CASE("Internal consistency checks and example use cases for the incompressi
 		}
 
 		val = 0.0;
-		res = XLT.s(Tref,pref);
+		res = XLT.s(Tref,pref,xref);
 		{
 		CAPTURE(T);
 		CAPTURE(val);
@@ -627,7 +628,7 @@ TEST_CASE("Internal consistency checks and example use cases for the incompressi
 
 		// Compare u
 		val = 45212.407309106304;
-		res = XLT.u(T,p);
+		res = XLT.u(T,p,x);
 		{
 		CAPTURE(T);
 		CAPTURE(val);
@@ -635,8 +636,8 @@ TEST_CASE("Internal consistency checks and example use cases for the incompressi
 		CHECK( check_abs(val,res,acc) );
 		}
 
-		val = href - pref/XLT.rho(Tref,pref);
-		res = XLT.u(Tref,pref);
+		val = href - pref/XLT.rho(Tref,pref,xref);
+		res = XLT.u(Tref,pref,xref);
 		{
 		CAPTURE(T);
 		CAPTURE(val);
@@ -646,7 +647,7 @@ TEST_CASE("Internal consistency checks and example use cases for the incompressi
 
 		// Compare h
 		val = 46425.32011926845;
-		res = XLT.h(T,p);
+		res = XLT.h(T,p,x);
 		{
 		CAPTURE(T);
 		CAPTURE(val);
@@ -655,7 +656,7 @@ TEST_CASE("Internal consistency checks and example use cases for the incompressi
 		}
 
 		val = 0.0;
-		res = XLT.h(Tref,pref);
+		res = XLT.h(Tref,pref,xref);
 		{
 		CAPTURE(T);
 		CAPTURE(val);
@@ -665,7 +666,7 @@ TEST_CASE("Internal consistency checks and example use cases for the incompressi
 
 		// Compare v
 		val = 0.0008931435169681835;
-		res = XLT.visc(T,p);
+		res = XLT.visc(T,p,x);
 		{
 		CAPTURE(T);
 		CAPTURE(val);
@@ -675,7 +676,7 @@ TEST_CASE("Internal consistency checks and example use cases for the incompressi
 
 		// Compare l
 		val = 0.10410086156049088;
-		res = XLT.cond(T,p);
+		res = XLT.cond(T,p,x);
 		{
 		CAPTURE(T);
 		CAPTURE(val);
diff --git a/src/Backends/Incompressible/IncompressibleLibrary.cpp b/src/Backends/Incompressible/IncompressibleLibrary.cpp
index 1fa4ed50..1b30e20d 100644
--- a/src/Backends/Incompressible/IncompressibleLibrary.cpp
+++ b/src/Backends/Incompressible/IncompressibleLibrary.cpp
@@ -5,6 +5,331 @@
 
 namespace CoolProp{
 
+/// Class to access Lithium-Bromide solutions
+/** Employs some basic wrapper-like functionality
+ *  to bridge the gap between the solution functions
+ *  used in the paper by Pátek and Klomfar:
+ *  http://dx.doi.org/10.1016/j.ijrefrig.2005.10.007
+ *
+ *  We owe gratitude to the authors for providing
+ *  both access to the paper as well as the equations
+ *  in the form of C source code. */
+
+double const LiBrSolution::M_H2O  = 0.018015268; /* kg/mol, molar mass of H2O */
+double const LiBrSolution::M_LiBr = 0.08685; /* kg/mol, molar mass of LiBr */
+double const LiBrSolution::T0     = 221; /* K, constant */
+
+/* Critical point of H2O */
+double const LiBrSolution::Tc_H2O   = 647.096; /* K, temperature  */
+double const LiBrSolution::pc_H2O   = 22.064; /* MPa, pressure */
+double const LiBrSolution::rhoc_H2O = 17873; /* mol/m^3 (322 kg/m^3), molar density */
+double const LiBrSolution::hc_H2O   = 37548.5; /* J/mol, molar enthalpy */
+double const LiBrSolution::sc_H2O   = 79.3933; /* J/(mol.K) molar entropy*/
+
+/*Triple point of H2O */
+double const LiBrSolution::Tt_H2O   = 273.16; /* K, temperature */
+double const LiBrSolution::cpt_H2O  = 76.0226; /* J/(mol.K), molar isobaric heat capacity*/
+
+double LiBrSolution::ps_mix(double T, double x)
+/* Equation (1) */
+{
+	static double m[8] = { 3.0, 4.0, 4.0, 8.0, 1.0, 1.0, 4.0, 6.0 };
+	static double n[8] = { 0.0, 5.0, 6.0, 3.0, 0.0, 2.0, 6.0, 0.0 };
+	static double t[8] = { 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0 };
+	static double a[8] = { -2.41303e2, 1.91750e7, -1.75521e8, 3.25430e7,
+			3.92571e2, -2.12626e3, 1.85127e8, 1.91216e3 };
+	double tau, suma = 0.0;
+	int i;
+
+	tau = T / Tc_H2O;
+	for (i = 0; i <= 7; i++)
+		suma += a[i] * pow(x, m[i]) * pow(0.4 - x, n[i]) * pow(tau, t[i]);
+	return (ps_H2O(T - suma));
+
+} /* end function ps_mix */
+
+double LiBrSolution::rho_mix(double T, double x)
+/* Equation (2) */
+{
+	static double m[2] = { 1.0, 1.0 };
+	static double n[2] = { 0.0, 6.0 };
+	static double a[2] = { 1.746, 4.709 };
+
+	double tau, suma = 0.0;
+	int i;
+
+	tau = T / Tc_H2O;
+	for (i = 0; i <= 1; i++)
+		suma += a[i] * pow(x, m[i]) * pow(tau, n[i]);
+
+	return ((1.0 - x) * rho_H2O(T) + rhoc_H2O * suma);
+
+} /* end function rho_mix */
+
+double LiBrSolution::cp_mix(double T, double x)
+/* Equation (3) */
+{
+	static double m[8] = { 2.0, 3.0, 3.0, 3.0, 3.0, 2.0, 1.0, 1.0 };
+	static double n[8] = { 0.0, 0.0, 1.0, 2.0, 3.0, 0.0, 3.0, 2.0 };
+	static double t[8] = { 0.0, 0.0, 0.0, 0.0, 0.0, 2.0, 3.0, 4.0 };
+	static double a[8] = { -1.42094e1, 4.04943e1, 1.11135e2, 2.29980e2,
+			1.34526e3, -1.41010e-2, 1.24977e-2, -6.83209e-4 };
+
+	double tau, suma = 0.0;
+	int i;
+
+	tau = Tc_H2O / (T - T0);
+	for (i = 0; i <= 7; i++)
+		suma += a[i] * pow(x, m[i]) * pow(0.4 - x, n[i]) * pow(tau, t[i]);
+
+	return ((1.0 - x) * cp_H2O(T) + cpt_H2O * suma);
+
+} /* end function cp_mix */
+
+double LiBrSolution::h_mix(double T, double x)
+/* Equation (4) */
+{
+	static double m[30] = { 1.0, 1.0, 2.0, 3.0, 6.0, 1.0, 3.0, 5.0, 4.0,
+			5.0, 5.0, 6.0, 6.0, 1.0, 2.0, 2.0, 2.0, 5.0, 6.0, 7.0, 1.0, 1.0,
+			2.0, 2.0, 2.0, 3.0, 1.0, 1.0, 1.0, 1.0 };
+
+	static double n[30] = { 0.0, 1.0, 6.0, 6.0, 2.0, 0.0, 0.0, 4.0, 0.0,
+			4.0, 5.0, 5.0, 6.0, 0.0, 3.0, 5.0, 7.0, 0.0, 3.0, 1.0, 0.0, 4.0,
+			2.0, 6.0, 7.0, 0.0, 0.0, 1.0, 2.0, 3.0 };
+
+	static double t[30] = { 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 2.0,
+			2.0, 2.0, 2.0, 2.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 4.0, 4.0,
+			4.0, 4.0, 4.0, 4.0, 5.0, 5.0, 5.0, 5.0 };
+
+	static double a[30] = { 2.27431, -7.99511, 3.85239e2, -1.63940e4,
+			-4.22562e2, 1.13314e-1, -8.33474, -1.73833e4, 6.49763,
+			3.24552e3, -1.34643e4, 3.99322e4, -2.58877e5, -1.93046e-3,
+			2.80616, -4.04479e1, 1.45342e2, -2.74873, -4.49743e2,
+			-1.21794e1, -5.83739e-3, 2.33910e-1, 3.41888e-1, 8.85259,
+			-1.78731e1, 7.35179e-2, -1.79430e-4, 1.84261e-3, -6.24282e-3,
+			6.84765e-3 };
+
+	double tau, suma = 0.0;
+	int i;
+
+	tau = Tc_H2O / (T - T0);
+	for (i = 0; i <= 29; i++)
+		suma += a[i] * pow(x, m[i]) * pow(0.4 - x, n[i]) * pow(tau, t[i]);
+
+	return ((1.0 - x) * h_H2O(T) + hc_H2O * suma);
+
+} /* end function h_mix */
+
+double LiBrSolution::s_mix(double T, double x)
+/* Equation (5) */
+{
+	static double m[29] = { 1.0, 1.0, 2.0, 3.0, 6.0, 1.0, 3.0, 5.0, 1.0,
+			2.0, 2.0, 4.0, 5.0, 5.0, 6.0, 6.0, 1.0, 3.0, 5.0, 7.0, 1.0, 1.0,
+			1.0, 2.0, 3.0, 1.0, 1.0, 1.0, 1.0 };
+
+	static double n[29] = { 0.0, 1.0, 6.0, 6.0, 2.0, 0.0, 0.0, 4.0, 0.0,
+			0.0, 4.0, 0.0, 4.0, 5.0, 2.0, 5.0, 0.0, 4.0, 0.0, 1.0, 0.0, 2.0,
+			4.0, 7.0, 1.0, 0.0, 1.0, 2.0, 3.0 };
+
+	static double t[29] = { 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 2.0,
+			2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 3.0, 3.0, 3.0, 3.0, 4.0, 4.0,
+			4.0, 4.0, 4.0, 5.0, 5.0, 5.0, 5.0 };
+
+	static double a[29] = { 1.53091, -4.52564, 6.98302e+2, -2.16664e+4,
+			-1.47533e+3, 8.47012e-2, -6.59523, -2.95331e+4, 9.56314e-3,
+			-1.88679e-1, 9.31752, 5.78104, 1.38931e+4, -1.71762e+4,
+			4.15108e+2, -5.55647e+4, -4.23409e-3, 3.05242e+1, -1.67620,
+			1.48283e+1, 3.03055e-3, -4.01810e-2, 1.49252e-1, 2.59240,
+			-1.77421e-1, -6.99650e-5, 6.05007e-4, -1.65228e-3, 1.22966e-3 };
+
+	double tau, suma = 0.0;
+	int i;
+
+	tau = Tc_H2O / (T - T0);
+	for (i = 0; i <= 28; i++)
+		suma += a[i] * pow(x, m[i]) * pow(0.4 - x, n[i]) * pow(tau, t[i]);
+
+	return ((1.0 - x) * s_H2O(T) + sc_H2O * suma);
+
+} /* end function s_mix */
+
+double LiBrSolution::ps_H2O(double T)
+/* Equation (28) */
+{
+	static double a[7] = { 0.0, -7.85951783, 1.84408259, -11.7866497,
+			22.6807411, -15.9618719, 1.80122502 };
+
+	double tau, ps;
+
+	tau = 1 - T / Tc_H2O;
+
+	ps = pc_H2O
+			* exp(
+					Tc_H2O / T
+							* (a[1] * tau + a[2] * pow(tau, 1.5)
+									+ a[3] * pow(tau, 3.0)
+									+ a[4] * pow(tau, 3.5)
+									+ a[5] * pow(tau, 4.0)
+									+ a[6] * pow(tau, 7.5)));
+
+	return (ps * 1.0e6);
+
+} /* end function ps_H2O */
+
+double LiBrSolution::rho_H2O(double T)
+/* Equation (29) */
+{
+	static double b[7] = { 0.0, 1.99274064, 1.09965342, -0.510839303,
+			-1.75493479, -45.5170352, -6.7469445e5 };
+	double theta, rho;
+
+	theta = 1.0 - T / Tc_H2O;
+
+	rho = rhoc_H2O
+			* (1.0 + b[1] * pow(theta, 1.0 / 3.0)
+					+ b[2] * pow(theta, 2.0 / 3.0)
+					+ b[3] * pow(theta, 5.0 / 3.0)
+					+ b[4] * pow(theta, 16.0 / 3.0)
+					+ b[5] * pow(theta, 43.0 / 3.0)
+					+ b[6] * pow(theta, 110.0 / 3.0));
+
+	return (rho);
+} /* end function rho_H2O */
+
+double LiBrSolution::cp_H2O(double T)
+/* Equation (30) */
+{
+	static double a[5] =
+			{ 1.38801, -2.95318, 3.18721, -0.645473, 9.18946e5 };
+	static double b[5] = { 0.0, 2.0, 3.0, 6.0, 34.0 };
+	static double c[5] = { 0.0, 2.0, 3.0, 5.0, 0.0 };
+
+	double suma = 0.0;
+	int i;
+
+	for (i = 0; i <= 4; i++)
+		suma += a[i] * exp(b[i] * log(1.0 - T / Tc_H2O))
+				* exp(c[i] * log(T / Tt_H2O));
+
+	return (cpt_H2O * suma);
+
+} /* end function cp_H2O */
+
+double LiBrSolution::h_H2O(double T)
+/* Equation (31) */
+{
+	static double a[4] = { -4.37196e-1, 3.03440e-1, -1.29582, -1.76410e-1 };
+	static double alpha[4] = { 1.0 / 3.0, 2.0 / 3.0, 5.0 / 6.0, 21.0 / 6.0 };
+
+	double suma = 0.0;
+	int i;
+
+	for (i = 0; i <= 3; i++)
+		suma += a[i] * exp(alpha[i] * log(1.0 - T / Tc_H2O));
+
+	return (hc_H2O * (1.0 + suma));
+
+} /* end function h_H2O */
+
+double LiBrSolution::s_H2O(double T)
+/* Equation (32)  */
+{
+	static double a[4] = { -3.34112e-1, -8.47987e-1, -9.11980e-1, -1.64046 };
+	static double alpha[4] = { 1.0 / 3.0, 3.0 / 3.0, 8.0 / 3.0, 24.0 / 3.0 };
+
+	double suma = 0.0;
+	int i;
+
+	for (i = 0; i <= 3; i++)
+		suma += a[i] * exp(alpha[i] * log(1.0 - T / Tc_H2O));
+
+	return (sc_H2O * (1.0 + suma));
+
+} /* end function s_H2O */
+
+
+/** Finished with the code from the paper. Now we need to
+ *  convert the molar values to mass-based units. */
+double LiBrSolution::massToMole(double w)
+/* Equation (7)  */
+{
+	return (w/M_LiBr)/(w/M_LiBr+(1.-w)/M_H2O);
+	//return (w*M_LiBr)/(w*M_LiBr+(1.-w)*M_H2O);
+}
+
+double LiBrSolution::molarToSpecific(double w, double value)
+/* Equation (7,8)  */
+{
+	double x = massToMole(w);
+	//return w/(x*M_LiBr) * value;
+	return 1. / ( x*M_LiBr + (1.-x)*M_H2O ) * value;
+}
+
+bool const LiBrSolution::debug = false;
+
+
+
+LiBrSolution::LiBrSolution(){
+	name = std::string("LiBr");
+	description = std::string("Lithium-Bromide solution from Patek2006");
+	reference = std::string("Patek2006");
+
+	Tmin     = 273.00;
+	Tmax     = 500.00;
+	TminPsat = Tmin;
+
+	xmin     = 0.0;
+	xmax     = 1.0;
+
+	xref   = 0.0;
+	Tref   = 0.0;
+	pref   = 0.0;
+	href   = 0.0;
+	sref   = 0.0;
+	uref   = 0.0;
+	rhoref = 0.0;
+	xbase  = 0.0;
+	Tbase  = 0.0;
+
+};
+
+double LiBrSolution::rho(double T_K, double p, double x){
+	checkTPX(T_K, p, x);
+	return 1./molarToSpecific(x, 1./rho_mix(T_K,massToMole(x)));
+}
+double LiBrSolution::c(double T_K, double p, double x){
+	checkTPX(T_K, p, x);
+	return molarToSpecific(x, cp_mix(T_K,massToMole(x)));
+}
+//double h(double T_K, double p, double x){
+//	return h_u(T_K,p,x);
+//}
+double LiBrSolution::s(double T_K, double p, double x){
+	checkTPX(T_K, p, x);
+	return molarToSpecific(x, s_mix(T_K,massToMole(x)));
+}
+double LiBrSolution::visc(double T_K, double p, double x){
+	throw ValueError("Viscosity is not defined for LiBr-solutions.");
+}
+double LiBrSolution::cond(double T_K, double p, double x){
+	throw ValueError("Thermal conductivity is not defined for LiBr-solutions.");
+}
+double LiBrSolution::u(double T_K, double p, double x){
+	checkTPX(T_K, p, x);
+	return molarToSpecific(x, h_mix(T_K,massToMole(x)));
+}
+double LiBrSolution::psat(double T_K, double x){
+	//checkT(T_K,p,x);
+	if (debug) throw ValueError(format("Your concentration is %f in kg/kg and %f in mol/mol.",x,massToMole(x)));
+	return ps_mix(T_K,massToMole(x));
+};
+double LiBrSolution::Tfreeze(double p, double x){
+	if (debug) throw ValueError(format("No freezing point data available for Lithium-Bromide: p=%f, x=%f",p,x));
+	return Tmin;
+}
+
+
+
 /// A general function to parse the json files that hold the coefficient matrices
 IncompressibleData JSONIncompressibleLibrary::parse_coefficients(rapidjson::Value &obj, std::string id, bool vital){
 	IncompressibleData fluidData;
@@ -145,6 +470,25 @@ void JSONIncompressibleLibrary::add_one(rapidjson::Value &fluid_json) {
 
 };
 
+void JSONIncompressibleLibrary::add_obj(IncompressibleFluid fluid_obj) {
+	_is_empty = false;
+
+	// Get the next index for this fluid
+	std::size_t index = fluid_map.size();
+
+	// Add index->fluid mapping
+	fluid_map[index] = IncompressibleFluid(fluid_obj);
+
+	// Create an instance of the fluid
+	IncompressibleFluid &fluid = fluid_map[index];
+
+    /// A function to check coefficients and equation types.
+    fluid.validate();
+
+    // Add name->index mapping
+    string_to_index_map[fluid.getName()] = index;
+}
+
 /// Get an IncompressibleFluid instance stored in this library
 /**
  @param name Name of the fluid
@@ -219,6 +563,7 @@ void load_incompressible_library()
     } else{
         try{library.add_many(dd);}catch(std::exception &e){std::cout << e.what() << std::endl;}
     }
+	library.add_obj(LiBrSolution());
 }
 
 JSONIncompressibleLibrary & get_incompressible_library(void){
diff --git a/src/Backends/Incompressible/IncompressibleLibrary.h b/src/Backends/Incompressible/IncompressibleLibrary.h
index 8d0f2087..75727f27 100644
--- a/src/Backends/Incompressible/IncompressibleLibrary.h
+++ b/src/Backends/Incompressible/IncompressibleLibrary.h
@@ -13,6 +13,139 @@ namespace CoolProp{
 
 // Forward declaration of the necessary debug function to avoid including the whole header
 extern int get_debug_level();
+
+
+/// Class to access Lithium-Bromide solutions
+/** Employs some basic wrapper-like functionality
+ *  to bridge the gap between the solution functions
+ *  used in the paper by Pátek and Klomfar:
+ *  http://dx.doi.org/10.1016/j.ijrefrig.2005.10.007
+ *
+ *  We owe gratitude to the authors for providing
+ *  both access to the paper as well as the equations
+ *  in the form of C source code. */
+class LiBrSolution : public IncompressibleFluid{
+
+protected:
+	static double const M_H2O; /* kg/mol, molar mass of H2O */
+	static double const M_LiBr; /* kg/mol, molar mass of LiBr */
+	static double const T0; /* K, constant */
+
+	/* Critical point of H2O */
+	static double const Tc_H2O; /* K, temperature  */
+	static double const pc_H2O; /* MPa, pressure */
+	static double const rhoc_H2O; /* mol/m^3 (322 kg/m^3), molar density */
+	static double const hc_H2O; /* J/mol, molar enthalpy */
+	static double const sc_H2O; /* J/(mol.K) molar entropy*/
+
+	/*Triple point of H2O */
+	static double const Tt_H2O; /* K, temperature */
+	static double const cpt_H2O; /* J/(mol.K), molar isobaric heat capacity*/
+
+	double ps_mix(double T, double x);
+	double rho_mix(double T, double x);
+	double cp_mix(double T, double x);
+	double h_mix(double T, double x);
+	double s_mix(double T, double x);
+	double ps_H2O(double T);
+	double rho_H2O(double T);
+	double cp_H2O(double T);
+	double h_H2O(double T);
+	double s_H2O(double T);
+
+	/** Finished with the code from the paper. Now we need to
+	 *  convert the molar values to mass-based units. */
+	double massToMole(double w);
+	double molarToSpecific(double w, double value);
+
+	static const bool debug;
+
+public:
+
+	LiBrSolution();
+
+	double rho(double T, double p, double x);
+	double c(double T, double p, double x);
+	//double h(double T_K, double p, double x);
+	double s(double T, double p, double x);
+	double visc(double T, double p, double x);
+	double cond(double T, double p, double x);
+	double u(double T, double p, double x);
+	double psat(double T, double x);
+	double Tfreeze(double p, double x);
+
+	/* Some functions can be inverted directly, those are listed
+	 * here. It is also possible to solve for other quantities, but
+	 * that involves some more sophisticated processing and is not
+	 * done here, but in the backend, T(h,p) for example.
+	 */
+	/// Temperature as a function of density, pressure and composition.
+	double T_rho (double Dmass, double p, double x){throw NotImplementedError(format("%s (%d): T from density is not implemented for LiBr.",__FILE__,__LINE__));}
+	/// Temperature as a function of heat capacities as a function of temperature, pressure and composition.
+	double T_c   (double Cmass, double p, double x){throw NotImplementedError(format("%s (%d): T from heat capacity is not implemented for LiBr.",__FILE__,__LINE__));}
+	/// Temperature as a function of entropy as a function of temperature, pressure and composition.
+	double T_s   (double Smass, double p, double x){throw NotImplementedError(format("%s (%d): T from entropy is not implemented for LiBr.",__FILE__,__LINE__));}
+	/// Temperature as a function of internal energy as a function of temperature, pressure and composition.
+	double T_u   (double Umass, double p, double x){throw NotImplementedError(format("%s (%d): T from internal energy is not implemented for LiBr.",__FILE__,__LINE__));}
+	/// Temperature as a function of enthalpy, pressure and composition.
+	//double T_h   (double Hmass, double p, double x){throw NotImplementedError(format("%s (%d): T from enthalpy is not implemented in the fluid, use the backend.",__FILE__,__LINE__));}
+	/// Viscosity as a function of temperature, pressure and composition.
+	double T_visc(double  visc, double p, double x){throw NotImplementedError(format("%s (%d): T from viscosity is not implemented.",__FILE__,__LINE__));}
+	/// Thermal conductivity as a function of temperature, pressure and composition.
+	double T_cond(double  cond, double p, double x){throw NotImplementedError(format("%s (%d): T from conductivity is not implemented.",__FILE__,__LINE__));}
+	/// Saturation pressure as a function of temperature and composition.
+	double T_psat(double  psat,           double x){throw NotImplementedError(format("%s (%d): T from psat is not implemented.",__FILE__,__LINE__));}
+	/// Composition as a function of freezing temperature and pressure.
+	double x_Tfreeze(       double Tfreeze, double p){throw NotImplementedError(format("%s (%d): x from T_freeze is not implemented.",__FILE__,__LINE__));}
+
+
+	/// Overwrite some standard functions that cannot be used with LiBr
+	void setName(std::string name){throw ValueError(format("%s (%d): Cannot change property of LiBr class",__FILE__,__LINE__));}
+	void setDescription(std::string description){throw ValueError(format("%s (%d): Cannot change property of LiBr class",__FILE__,__LINE__));}
+	void setReference(std::string reference){throw ValueError(format("%s (%d): Cannot change property of LiBr class",__FILE__,__LINE__));}
+	void setTmax(double Tmax){throw ValueError(format("%s (%d): Cannot change property of LiBr class",__FILE__,__LINE__));}
+	void setTmin(double Tmin){throw ValueError(format("%s (%d): Cannot change property of LiBr class",__FILE__,__LINE__));}
+	void setxmax(double xmax){throw ValueError(format("%s (%d): Cannot change property of LiBr class",__FILE__,__LINE__));}
+	void setxmin(double xmin){throw ValueError(format("%s (%d): Cannot change property of LiBr class",__FILE__,__LINE__));}
+	void setTminPsat(double TminPsat){throw ValueError(format("%s (%d): Cannot change property of LiBr class",__FILE__,__LINE__));}
+
+	void setTbase(double Tbase){throw ValueError(format("%s (%d): Cannot change property of LiBr class",__FILE__,__LINE__));}
+	void setxbase(double xbase){throw ValueError(format("%s (%d): Cannot change property of LiBr class",__FILE__,__LINE__));}
+
+	void setDensity(IncompressibleData density){throw ValueError(format("%s (%d): Cannot change property of LiBr class",__FILE__,__LINE__));}
+	void setSpecificHeat(IncompressibleData specific_heat){throw ValueError(format("%s (%d): Cannot change property of LiBr class",__FILE__,__LINE__));}
+	void setViscosity(IncompressibleData viscosity){throw ValueError(format("%s (%d): Cannot change property of LiBr class",__FILE__,__LINE__));}
+	void setConductivity(IncompressibleData conductivity){throw ValueError(format("%s (%d): Cannot change property of LiBr class",__FILE__,__LINE__));}
+	void setPsat(IncompressibleData p_sat){throw ValueError(format("%s (%d): Cannot change property of LiBr class",__FILE__,__LINE__));}
+	void setTfreeze(IncompressibleData T_freeze){throw ValueError(format("%s (%d): Cannot change property of LiBr class",__FILE__,__LINE__));}
+	void setVolToMass(IncompressibleData volToMass){throw ValueError(format("%s (%d): Cannot change property of LiBr class",__FILE__,__LINE__));}
+	void setMassToMole(IncompressibleData massToMole){throw ValueError(format("%s (%d): Cannot change property of LiBr class",__FILE__,__LINE__));}
+
+	bool is_pure() {return false;};
+
+};
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
 
 /// A container for the fluid parameters for the incompressible fluids
 /**
@@ -45,6 +178,7 @@ public:
     /// Add all the fluid entries in the rapidjson::Value instance passed in
     void add_many(rapidjson::Value &listing);
     void add_one(rapidjson::Value &fluid_json);
+    void add_obj(IncompressibleFluid fluid_obj);
 
     /// Get an IncompressibleFluid instance stored in this library
     /**