def generateThermo(self):
"""
Generate the thermodynamic data for the species and fit it to the
desired heat capacity model (as specified in the `thermoClass`
attribute).
"""
if self.thermoClass.lower() not in ['wilhoit', 'nasa']:
raise Exception('Unknown thermodynamic model "{0}".'.format(
self.thermoClass))
species = self.species
logging.info('Generating {0} thermo model for {1}...'.format(
self.thermoClass, species))
Tlist = numpy.arange(10.0, 3001.0, 10.0, numpy.float64)
Cplist = numpy.zeros_like(Tlist)
H298 = 0.0
S298 = 0.0
conformer = self.species.conformer
for i in range(Tlist.shape[0]):
Cplist[i] += conformer.getHeatCapacity(Tlist[i])
H298 += conformer.getEnthalpy(298.) + conformer.E0.value_si
S298 += conformer.getEntropy(298.)
if not any([
isinstance(mode, (LinearRotor, NonlinearRotor))
for mode in conformer.modes
]):
# Monatomic species
linear = False
Nfreq = 0
Nrotors = 0
Cp0 = 2.5 * constants.R
CpInf = 2.5 * constants.R
else:
# Polyatomic species
linear = True if isinstance(conformer.modes[1],
LinearRotor) else False
Nfreq = len(conformer.modes[2].frequencies.value)
Nrotors = len(conformer.modes[3:])
Cp0 = (3.5 if linear else 4.0) * constants.R
CpInf = Cp0 + (Nfreq + 0.5 * Nrotors) * constants.R
wilhoit = Wilhoit()
if Nfreq == 0 and Nrotors == 0:
wilhoit.Cp0 = (Cplist[0], "J/(mol*K)")
wilhoit.CpInf = (Cplist[0], "J/(mol*K)")
wilhoit.B = (500., "K")
wilhoit.H0 = (0.0, "J/mol")
wilhoit.S0 = (0.0, "J/(mol*K)")
wilhoit.H0 = (H298 - wilhoit.getEnthalpy(298.15), "J/mol")
wilhoit.S0 = (S298 - wilhoit.getEntropy(298.15), "J/(mol*K)")
else:
wilhoit.fitToData(Tlist, Cplist, Cp0, CpInf, H298, S298, B0=500.0)
if self.thermoClass.lower() == 'nasa':
species.thermo = wilhoit.toNASA(Tmin=10.0, Tmax=3000.0, Tint=500.0)
else:
species.thermo = wilhoit
def setUp(self):
"""
A function run before each unit test in this class.
"""
self.Cp0 = 4.0
self.CpInf = 21.5
self.a0 = 0.0977518
self.a1 = -16.3067
self.a2 = 26.2524
self.a3 = -12.6785
self.B = 1068.68
self.H0 = -94088. # -782.292 kJ/mol / constants.R
self.S0 = -118.46 # -984.932 J/mol*K / constants.R
self.Tmin = 300.
self.Tmax = 3000.
self.comment = 'C2H6'
self.wilhoit = Wilhoit(
Cp0 = (self.Cp0*constants.R,"J/(mol*K)"),
CpInf = (self.CpInf*constants.R,"J/(mol*K)"),
a0 = self.a0,
a1 = self.a1,
a2 = self.a2,
a3 = self.a3,
B = (self.B,"K"),
H0 = (self.H0*0.001*constants.R,"kJ/mol"),
S0 = (self.S0*constants.R,"J/(mol*K)"),
Tmin = (self.Tmin,"K"),
Tmax = (self.Tmax,"K"),
comment = self.comment,
)
def test_fit_to_data(self):
"""
Test the Wilhoit.fit_to_data() method.
"""
h298 = self.wilhoit.get_enthalpy(298)
s298 = self.wilhoit.get_entropy(298)
Tdata = np.array([300., 400., 500., 600., 800., 1000., 1500.])
cp_data = np.zeros_like(Tdata)
for i in range(Tdata.shape[0]):
cp_data[i] = self.wilhoit.get_heat_capacity(Tdata[i])
cp_0 = self.Cp0 * constants.R
cp_inf = self.CpInf * constants.R
# Fit the Wilhoit polynomial to the data
wilhoit = Wilhoit().fit_to_data(Tdata, cp_data, cp_0, cp_inf, h298,
s298)
# Check that the fit reproduces the input data
for T in Tdata:
cp_exp = self.wilhoit.get_heat_capacity(T)
cp_act = wilhoit.get_heat_capacity(T)
self.assertAlmostEqual(cp_act, cp_exp, 4)
h_exp = self.wilhoit.get_enthalpy(T)
h_act = wilhoit.get_enthalpy(T)
self.assertAlmostEqual(h_act, h_exp, 3)
s_exp = self.wilhoit.get_entropy(T)
s_act = wilhoit.get_entropy(T)
self.assertAlmostEqual(s_act, s_exp, 4)
# Check that the fit reproduces the input parameters
# Since we're fitting to data generated from a Wilhoit (and since the
# fitting algorithm is linear least-squares), we should get the same
# Wilhoit parameters (with a small allowance for fitting error)
self.assertAlmostEqual(wilhoit.Cp0.value_si, self.wilhoit.Cp0.value_si,
6)
self.assertAlmostEqual(wilhoit.CpInf.value_si,
self.wilhoit.CpInf.value_si, 6)
self.assertAlmostEqual(wilhoit.a0, self.wilhoit.a0, 2)
self.assertAlmostEqual(wilhoit.a1, self.wilhoit.a1, 2)
self.assertAlmostEqual(wilhoit.a2, self.wilhoit.a2, 2)
self.assertAlmostEqual(wilhoit.a3, self.wilhoit.a3, 2)
self.assertAlmostEqual(wilhoit.B.value_si, self.wilhoit.B.value_si, 2)
self.assertAlmostEqual(wilhoit.H0.value_si, self.wilhoit.H0.value_si,
0)
self.assertAlmostEqual(wilhoit.S0.value_si, self.wilhoit.S0.value_si,
2)
def test_fitToData(self):
"""
Test the Wilhoit.fitToData() method.
"""
H298 = self.wilhoit.getEnthalpy(298)
S298 = self.wilhoit.getEntropy(298)
Tdata = numpy.array([300., 400., 500., 600., 800., 1000., 1500.])
Cpdata = numpy.zeros_like(Tdata)
for i in range(Tdata.shape[0]):
Cpdata[i] = self.wilhoit.getHeatCapacity(Tdata[i])
Cp0 = self.Cp0 * constants.R
CpInf = self.CpInf * constants.R
# Fit the Wilhoit polynomial to the data
wilhoit = Wilhoit().fitToData(Tdata, Cpdata, Cp0, CpInf, H298, S298)
# Check that the fit reproduces the input data
for T in Tdata:
Cpexp = self.wilhoit.getHeatCapacity(T)
Cpact = wilhoit.getHeatCapacity(T)
self.assertAlmostEqual(Cpact, Cpexp, 4)
Hexp = self.wilhoit.getEnthalpy(T)
Hact = wilhoit.getEnthalpy(T)
self.assertAlmostEqual(Hact, Hexp, 3)
Sexp = self.wilhoit.getEntropy(T)
Sact = wilhoit.getEntropy(T)
self.assertAlmostEqual(Sact, Sexp, 4)
# Check that the fit reproduces the input parameters
# Since we're fitting to data generated from a Wilhoit (and since the
# fitting algorithm is linear least-squares), we should get the same
# Wilhoit parameters (with a small allowance for fitting error)
self.assertAlmostEqual(wilhoit.Cp0.value_si, self.wilhoit.Cp0.value_si,
6)
self.assertAlmostEqual(wilhoit.CpInf.value_si,
self.wilhoit.CpInf.value_si, 6)
self.assertAlmostEqual(wilhoit.a0, self.wilhoit.a0, 2)
self.assertAlmostEqual(wilhoit.a1, self.wilhoit.a1, 2)
self.assertAlmostEqual(wilhoit.a2, self.wilhoit.a2, 2)
self.assertAlmostEqual(wilhoit.a3, self.wilhoit.a3, 2)
self.assertAlmostEqual(wilhoit.B.value_si, self.wilhoit.B.value_si, 2)
self.assertAlmostEqual(wilhoit.H0.value_si, self.wilhoit.H0.value_si,
0)
self.assertAlmostEqual(wilhoit.S0.value_si, self.wilhoit.S0.value_si,
2)
def generate_thermo(self):
"""
Generate the thermodynamic data for the species and fit it to the
desired heat capacity model (as specified in the `thermo_class`
attribute).
"""
if self.thermo_class.lower() not in ['wilhoit', 'nasa']:
raise InputError('Unknown thermodynamic model "{0}".'.format(
self.thermo_class))
species = self.species
logging.debug('Generating {0} thermo model for {1}...'.format(
self.thermo_class, species))
if species.thermo is not None:
logging.info(
"Thermo already generated for species {}. Skipping thermo generation."
.format(species))
return None
Tlist = np.arange(10.0, 3001.0, 10.0, np.float64)
Cplist = np.zeros_like(Tlist)
H298 = 0.0
S298 = 0.0
conformer = self.species.conformer
for i in range(Tlist.shape[0]):
Cplist[i] += conformer.get_heat_capacity(Tlist[i])
H298 += conformer.get_enthalpy(298.) + conformer.E0.value_si
S298 += conformer.get_entropy(298.)
if not any([
isinstance(mode, (LinearRotor, NonlinearRotor))
for mode in conformer.modes
]):
# Monatomic species
n_freq = 0
n_rotors = 0
Cp0 = 2.5 * constants.R
CpInf = 2.5 * constants.R
else:
# Polyatomic species
linear = True if isinstance(conformer.modes[1],
LinearRotor) else False
n_freq = len(conformer.modes[2].frequencies.value)
n_rotors = len(conformer.modes[3:])
Cp0 = (3.5 if linear else 4.0) * constants.R
CpInf = Cp0 + (n_freq + 0.5 * n_rotors) * constants.R
wilhoit = Wilhoit()
if n_freq == 0 and n_rotors == 0:
wilhoit.Cp0 = (Cplist[0], "J/(mol*K)")
wilhoit.CpInf = (Cplist[0], "J/(mol*K)")
wilhoit.B = (500., "K")
wilhoit.H0 = (0.0, "J/mol")
wilhoit.S0 = (0.0, "J/(mol*K)")
wilhoit.H0 = (H298 - wilhoit.get_enthalpy(298.15), "J/mol")
wilhoit.S0 = (S298 - wilhoit.get_entropy(298.15), "J/(mol*K)")
else:
wilhoit.fit_to_data(Tlist,
Cplist,
Cp0,
CpInf,
H298,
S298,
B0=500.0)
if self.thermo_class.lower() == 'nasa':
species.thermo = wilhoit.to_nasa(Tmin=10.0,
Tmax=3000.0,
Tint=500.0)
else:
species.thermo = wilhoit
elementCounts = {'C': 6, 'H': 5, 'I': 1}
Tlist = numpy.array([300.0,400.0,500.0,600.0,800.0,1000.0])
Cplist = numpy.array([24.2, 31.1, 36.9, 41.4, 48, 52.55])*4.184 #J/mol*K
H298 = 40.5*4184 #J/mol
S298 = 81.35*4.184 #J/mol*K
# Polyatomic species
linear = False
Nfreq = 30
Nrotors = 0
Cp0 = (3.5 if linear else 4.0) * constants.R
CpInf = Cp0 + (Nfreq + 0.5 * Nrotors) * constants.R
wilhoit = Wilhoit()
wilhoit.fitToData(Tlist, Cplist, Cp0, CpInf, H298, S298, B0=500.0)\
NASA_fit = wilhoit.toNASA(Tlist[0], Tlist[-1], Tint=500.0)
string = ''
f = open('chem.inp', 'a')
# Line 1
string += '{0:<16} '.format(SpeciesIdentifier)
if len(elementCounts) <= 4:
# Use the original Chemkin syntax for the element counts
for key, count in elementCounts.iteritems():
if isinstance(key, tuple):
