get_H_kwargs={'units': 'kcal/mol'},
get_S_kwargs={'units': 'cal/mol/K'},
get_G_kwargs={'units': 'kcal/mol'})
f2.set_size_inches(6, 6)
f2.set_dpi(200)
plt.show()
# <a id='section_8_2'></a>
# ## 8.2. Fitting an empirical object to a StatMech object
# Empirical objects can be made directly using ``StatMech`` objects and the ``from_model`` method.
# In[10]:
H2_nasa = Nasa.from_model(name='H2',
T_low=200.,
T_high=3500.,
model=H2_statmech)
# Compare the statistical mechanical model to the empirical model
f3, ax3 = H2_nasa.plot_statmech_and_empirical(Cp_units='J/mol/K',
H_units='kJ/mol',
S_units='J/mol/K',
G_units='kJ/mol')
f3.set_size_inches(6, 8)
f3.set_dpi(200)
plt.show()
# <a id='section_9'></a>
# # 9. Input/Output
# pMuTT has more IO functionality than below. See this page for [supported IO functions](https://vlachosgroup.github.io/pMuTT/io.html).
def setUp(self):
unittest.TestCase.setUp(self)
self.Nasa_direct = Nasa(name='H2O',
elements={
'H': 2,
'O': 1
},
phase='g',
a_low=np.array([
4.04618796E+00, -6.87238823E-04,
2.79722240E-06, -1.42318006E-09,
2.34551159E-13, -3.02826236E+04,
-2.50036531E-01
]),
a_high=np.array([
2.41854323E+00, 3.35448922E-03,
-9.66398101E-07, 1.34441829E-10,
-7.18940063E-15, -2.97582484E+04,
8.37839787E+00
]),
T_low=100.,
T_mid=1610.97,
T_high=5000.)
self.Nasa_direct_dict = {
'class':
"<class 'pmutt.empirical.nasa.Nasa'>",
'name':
'H2O',
'elements': {
'H': 2,
'O': 1
},
'phase':
'g',
'a_low': [
4.04618796E+00, -6.87238823E-04, 2.79722240E-06,
-1.42318006E-09, 2.34551159E-13, -3.02826236E+04,
-2.50036531E-01
],
'a_high': [
2.41854323E+00, 3.35448922E-03, -9.66398101E-07,
1.34441829E-10, -7.18940063E-15, -2.97582484E+04,
8.37839787E+00
],
'T_low':
100.,
'T_mid':
1610.97,
'T_high':
5000.,
'notes':
None,
'model':
None,
'misc_models': [{
'class': "<class 'pmutt.empirical.GasPressureAdj'>"
}],
'cat_site':
None,
'n_sites':
None,
'smiles':
None,
'type':
'nasa'
}
self.Nasa_data = Nasa.from_data(
name='H2O',
elements={
'H': 2,
'O': 1
},
phase='g',
T=np.array([
500., 600., 700., 800., 900., 1000., 1100., 1200., 1300.,
1400., 1500., 1600., 1700., 1800., 1900., 2000., 2100., 2200
]),
CpoR=np.array([
4.238636088, 4.363835667, 4.503924733, 4.654023202,
4.809813915, 4.967542636, 5.124018051, 5.276611768,
5.423258319, 5.56245516, 5.693262665, 5.815304137, 5.928750505,
6.034087273, 6.131819121, 6.222433488, 6.306400563, 6.384173277
]),
T_ref=500.,
HoRT_ref=-56.49930957,
SoR_ref=24.84583501)
self.Nasa_statmech = Nasa.from_model(name='H2O',
elements={
'H': 2,
'O': 1
},
phase='g',
model=StatMech,
trans_model=trans.FreeTrans,
n_degrees=3,
vib_model=vib.HarmonicVib,
elec_model=elec.GroundStateElec,
rot_model=rot.RigidRotor,
potentialenergy=-14.2209,
atoms=molecule('H2O'),
symmetrynumber=2,
spin=0,
vib_wavenumbers=np.array(
[0.47462, 0.46033, 0.19633]),
T_low=100.,
T_mid=1610.97,
T_high=5000.)
self.mw = get_molecular_weight({'H': 2, 'O': 1}) # g/mol
# In[3]:
from pmutt.empirical.nasa import Nasa
# Lower and higher temperatures
T_low = 298. # K
T_high = 800. # K
species_data = read_excel(io=input_path, sheet_name='species')
species = []
species_phases = {}
for ind_species_data in species_data:
# Initialize NASA from statistical mechanical data
ind_species = Nasa.from_model(T_low=T_low,
T_high=T_high,
references=refs,
**ind_species_data)
species.append(ind_species)
# Group the species by phase for later use
try:
species_phases[ind_species.phase].append(ind_species)
except KeyError:
species_phases[ind_species.phase] = [ind_species]
# ### Adding species from other empirical sources
# In[4]:
import numpy as np
from pmutt.empirical.shomate import Shomate
'The "refs" sheet could not be found in {}. Skiping references'.format(
input_path))
refs = None
else:
refs = [Reference(**ref_data) for ref_data in refs_data]
refs = References(references=refs)
# ### Reading Species
#
# Third, we will use the ``refs`` defined before and the ``species`` sheet to convert statistical mechanical data to [``NASA``](https://vlachosgroup.github.io/pMuTT/api/empirical/nasa/pmutt.empirical.nasa.Nasa.html#pmutt.empirical.nasa.Nasa) objects.
# Read the species' data
species_data = read_excel(io=input_path, sheet_name='species')
# Create NASA polynomials from the species
species = [
Nasa.from_model(references=refs, **ind_species_data)
for ind_species_data in species_data
]
# ### Adding species from other empirical sources (optional)
#
# Note that OpenMKM also supports [``Shomate``](https://vlachosgroup.github.io/pMuTT/api/empirical/shomate/pmutt.empirical.shomate.Shomate.html#pmutt.empirical.shomate.Shomate) and [``NASA9``](https://vlachosgroup.github.io/pMuTT/api/empirical/nasa/pmutt.empirical.nasa.Nasa9.html) objects. Below, we define a single ``Shomate`` species.
Ar = Shomate(name='Ar',
elements={'Ar': 1},
phase='gas',
T_low=298.,
T_high=6000.,
a=np.array([
20.78600, 2.825911e-7, -1.464191e-7, 1.092131e-8,
-3.661371e-8, -6.19735, 179.999, 0.
]))
# ### Reading species
# In[9]:
from pmutt.empirical.nasa import Nasa
# Range of data to fit the Nasa polynomials
T_low = 298. # K
T_high = 800. # K
species_data = read_excel(io=excel_path, sheet_name='species')
species = []
for specie_data in species_data:
specie = Nasa.from_model(T_low=T_low, T_high=T_high, references=refs, **specie_data)
# If the species is a surface species, assign the catalyst site specified above
if specie.phase.lower() == 's':
specie.cat_site = cat_site
specie.n_sites = 1
species.append(specie)
# The warning above is typical when empirical objects are fitting to `StatMech` objects with the `placeholder` preset.
# ### Reading reactions
# In[10]:
from pmutt import pmutt_list_to_dict
# ### NASA polynomial
# The [``NASA``][0] format is used for our microkinetic modeling software, Chemkin.
#
# #### Initializing Nasa from StatMech
# Below, we initialize the NASA polynomial from the ``StatMech`` object we created earlier.
#
# [0]: https://vlachosgroup.github.io/pmutt/empirical.html#nasa
# In[6]:
from pmutt.empirical.nasa import Nasa
butane_nasa = Nasa.from_model(name='butane',
model=butane_statmech,
T_low=298.,
T_high=800.,
elements={'C': 4, 'H': 10},
phase='G')
H_nasa = butane_nasa.get_H(T=298., units='kJ/mol')
S_nasa = butane_nasa.get_S(T=298., units='J/mol/K')
print('H_butane(T=298) = {:.1f} kJ/mol'.format(H_nasa))
print('S_butane(T=298) = {:.2f} J/mol/K'.format(S_nasa))
# Although it is not covered here, you can also generate empirical objects from experimental data using the ``.from_data`` method. See [Experimental to Empirical][6] example.
#
# [6]: https://vlachosgroup.github.io/pmutt/examples.html#experimental-to-empirical
# #### Initializing Nasa Directly
# We can also initialize the NASA polynomial if we have the polynomials. Using an entry from the [Reaction Mechanism Generator (RMG) database][0].