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import_kinetics.py
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import_kinetics.py
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import torch
def set_nasa(self):
self.nasa_low = torch.zeros([self.n_species, 7]).to(self.device)
self.nasa_high = torch.zeros([self.n_species, 7]).to(self.device)
yaml_species = self.model_yaml['species']
yaml_species_names = [s['name'] for s in yaml_species]
for i, species in enumerate(self.gas.species_names):
index = yaml_species_names.index(species)
self.nasa_low[i, :] = torch.Tensor(yaml_species[index]['thermo']['data'][0])
self.nasa_high[i, :] = torch.Tensor(yaml_species[index]['thermo']['data'][1])
def set_transport(self,
species_viscosities_poly,
thermal_conductivity_poly,
binary_diff_coeffs_poly):
# Transport Properties
self.species_viscosities_poly = torch.from_numpy(species_viscosities_poly).to(self.device)
self.thermal_conductivity_poly = torch.from_numpy(thermal_conductivity_poly).to(self.device)
self.binary_diff_coeffs_poly = torch.from_numpy(binary_diff_coeffs_poly).to(self.device)
self.poly_order = self.species_viscosities_poly.shape[0]
def set_reactions(self):
self.reaction = [[None]] * self.n_reactions
self.n_rate_constants = [[None]] * self.n_reactions
self.reactant_stoich_coeffs = torch.Tensor(self.gas.reactant_stoich_coeffs()).to(self.device)
self.reactant_orders = torch.Tensor(self.gas.reactant_stoich_coeffs()).to(self.device)
self.product_stoich_coeffs = torch.Tensor(self.gas.product_stoich_coeffs()).to(self.device)
self.net_stoich_coeffs = self.product_stoich_coeffs - self.reactant_stoich_coeffs
self.efficiencies_coeffs = torch.ones([self.n_species, self.n_reactions]).to(self.device)
self.Arrhenius_coeffs = torch.zeros([self.n_reactions, 3]).to(self.device)
self.is_reversible = torch.ones([self.n_reactions]).to(self.device)
# reaction type 2 will be set as 1
self.is_three_body = torch.zeros([self.n_reactions]).to(self.device)
self.is_falloff = torch.zeros([self.n_reactions]).to(self.device)
self.is_Troe_falloff = torch.zeros([self.n_reactions]).to(self.device)
self.list_reaction_type1 = []
self.list_reaction_type2 = []
self.list_reaction_type4 = []
self.list_reaction_type4_Troe = []
# the id of Troe in type 4
self.list_id_Troe_in_type4 = []
self.list_reaction_type5 = []
# count the num in type 4
count_type4 = -1
for i in range(self.n_reactions):
# Type 1: regular reaction, 2: three-body, 4:fall-off, 5:pressure-dependent-Arrhenius
yaml_reaction = self.model_yaml['reactions'][i]
self.reaction[i] = {'equation': self.gas.reaction_equation(i)}
self.reaction[i]['reactants'] = self.gas.reactants(i)
self.reaction[i]['products'] = self.gas.products(i)
self.reaction[i]['reaction_type'] = self.gas.reaction_type(i)
if self.gas.reaction_type(i) in [1]:
self.list_reaction_type1.append(i)
if self.gas.reaction_type(i) in [2]:
self.list_reaction_type2.append(i)
if self.gas.reaction_type(i) in [4]:
# id of the current item in the list
count_type4 = count_type4 + 1
self.list_reaction_type4.append(i)
if self.gas.reaction_type(i) in [5]:
self.list_reaction_type5.append(i)
if self.gas.is_reversible(i) is False:
self.is_reversible[i].fill_(0)
if self.gas.reaction_type(i) in [1, 2]:
self.reaction[i]['A'] = torch.Tensor(
[yaml_reaction['rate-constant']['A']]).to(self.device)
self.reaction[i]['b'] = torch.Tensor(
[yaml_reaction['rate-constant']['b']]).to(self.device)
if type(yaml_reaction['rate-constant']['Ea']) is str:
Ea = list(map(eval, [yaml_reaction['rate-constant']['Ea'].split(' ')[0]]))
else:
Ea = [yaml_reaction['rate-constant']['Ea']]
self.reaction[i]['Ea'] = torch.Tensor(Ea).to(self.device)
if self.gas.reaction_type(i) in [2]:
self.is_three_body[i] = 1
if 'efficiencies' in yaml_reaction:
self.reaction[i]['efficiencies'] = yaml_reaction['efficiencies']
for key, value in self.reaction[i]['efficiencies'].items():
self.efficiencies_coeffs[self.gas.species_index(key), i] = value
if self.gas.reaction_type(i) in [4]:
self.is_falloff[i] = 1
if 'efficiencies' in yaml_reaction:
self.reaction[i]['efficiencies'] = yaml_reaction['efficiencies']
for key, value in self.reaction[i]['efficiencies'].items():
self.efficiencies_coeffs[self.gas.species_index(key), i] = value
high_p = yaml_reaction['high-P-rate-constant']
low_p = yaml_reaction['low-P-rate-constant']
self.reaction[i]['A'] = torch.Tensor([high_p['A']]).to(self.device)
self.reaction[i]['b'] = torch.Tensor([high_p['b']]).to(self.device)
if type(high_p['Ea']) is str:
Ea = list(map(eval, [high_p['Ea'].split(' ')[0]]))
else:
Ea = [high_p['Ea']]
self.reaction[i]['Ea'] = torch.Tensor(Ea).to(self.device)
self.reaction[i]['A_0'] = torch.Tensor([low_p['A']]).to(self.device)
self.reaction[i]['b_0'] = torch.Tensor([low_p['b']]).to(self.device)
if type(low_p['Ea']) is str:
Ea = list(map(eval, [low_p['Ea'].split(' ')[0]]))
else:
Ea = [low_p['Ea']]
self.reaction[i]['Ea_0'] = torch.Tensor(Ea).to(self.device)
if 'Troe' in yaml_reaction:
self.is_Troe_falloff[i] = 1
self.list_reaction_type4_Troe.append(i)
self.list_id_Troe_in_type4.append(count_type4)
Troe = yaml_reaction['Troe']
if 'T2' in Troe:
self.reaction[i]['Troe'] = {'A': torch.Tensor([Troe['A']]).to(self.device),
'T1': torch.Tensor([Troe['T1']]).to(self.device),
'T2': torch.Tensor([Troe['T2']]).to(self.device),
'T3': torch.Tensor([Troe['T3']]).to(self.device)
}
else:
self.reaction[i]['Troe'] = {'A': torch.Tensor([Troe['A']]).to(self.device),
'T1': torch.Tensor([Troe['T1']]).to(self.device),
'T2': torch.Tensor([1e30]).to(self.device),
'T3': torch.Tensor([Troe['T3']]).to(self.device)
}
if self.gas.reaction_type(i) in [5]:
self.n_rate_constants[i] = len(self.gas.reaction(i).rates)
self.reaction[i]['p_dep'] = {}
self.reaction[i]['p_dep']['A'] = [[None]] * self.n_rate_constants[i]
self.reaction[i]['P'] = [[None]] * self.n_rate_constants[i]
self.reaction[i]['b'] = [[None]] * self.n_rate_constants[i]
self.reaction[i]['Ea'] = [[None]] * self.n_rate_constants[i]
for j in range(self.n_rate_constants[i]):
pdep_arrhenius = yaml_reaction['rate-constants'][j]
if type(pdep_arrhenius['P']) is str:
P = list(map(eval, [pdep_arrhenius['P'].split(' ')[0]]))
self.reaction[i]['P'][j] = torch.Tensor(P).to(self.device)
if ([pdep_arrhenius['P'].split(' ')[1]] == ['atm'] or
[pdep_arrhenius['P'].split(' ')[1]] == ['ATM']):
self.reaction[i]['P'][j] = 101325 * self.reaction[i]['P'][j]
if [pdep_arrhenius['P'].split(' ')[1]] == ['MPa']:
self.reaction[i]['P'][j] = 1000000 * self.reaction[i]['P'][j]
else:
P = [pdep_arrhenius['P']]
self.reaction[i]['P'][j] = torch.Tensor(P).to(self.device)
if type(pdep_arrhenius['A']) is str:
A = list(map(eval, [pdep_arrhenius['A'].split(' ')[0]]))
else:
A = [pdep_arrhenius['A']]
self.reaction[i]['p_dep']['A'][j] = torch.Tensor(A).to(self.device)
if type(pdep_arrhenius['b']) is str:
b = list(map(eval, [pdep_arrhenius['b'].split(' ')[0]]))
else:
b = [pdep_arrhenius['b']]
self.reaction[i]['b'][j] = torch.Tensor(b).to(self.device)
if type(pdep_arrhenius['Ea']) is str:
Ea = list(map(eval, [pdep_arrhenius['Ea'].split(' ')[0]]))
else:
Ea = [pdep_arrhenius['Ea']]
self.reaction[i]['Ea'][j] = torch.Tensor(Ea).to(self.device)
if type(pdep_arrhenius['A']) is str:
A = list(map(eval, [pdep_arrhenius['A'].split(' ')[0]]))
else:
A = [pdep_arrhenius['A']]
self.reaction[i]['A'] = torch.Tensor(A).to(self.device)
if 'orders' in yaml_reaction:
for key, value in yaml_reaction['orders'].items():
self.reactant_orders[self.gas.species_index(key), i] = value
if 'units' in self.model_yaml:
if (self.model_yaml['units']['length'] == 'cm' and
self.model_yaml['units']['quantity'] == 'mol'):
if self.gas.reaction_type(i) in [1, 2, 4]:
self.reaction[i]['A'] *= 1e-3 ** (
self.reactant_stoich_coeffs[:, i].sum().item() - 1)
if self.gas.reaction_type(i) in [2]:
self.reaction[i]['A'] *= 1e-3
if self.gas.reaction_type(i) in [4]:
self.reaction[i]['A_0'] *= 1e-3
self.reaction[i]['A_0'] *= 1e-3 ** (
self.reactant_stoich_coeffs[:, i].sum().item() - 1)
if self.gas.reaction_type(i) in [5]:
for j in range(self.n_rate_constants[i]):
self.reaction[i]['p_dep']['A'][j] *= 1e-3 ** (
self.reactant_stoich_coeffs[:, i].sum().item() - 1)
if self.gas.reaction_type(i) in [1, 2, 4]:
self.Arrhenius_coeffs[i, 0] = self.reaction[i]['A']
self.Arrhenius_coeffs[i, 1] = self.reaction[i]['b']
self.Arrhenius_coeffs[i, 2] = self.reaction[i]['Ea']
if self.gas.reaction_type(i) in [5]:
self.Arrhenius_coeffs[i, 0] = self.reaction[i]['A']
self.Arrhenius_coeffs[i, 1] = self.reaction[i]['b'][0]
self.Arrhenius_coeffs[i, 2] = self.reaction[i]['Ea'][0]
self.Arrhenius_A = self.Arrhenius_coeffs[:, 0]
self.Arrhenius_b = self.Arrhenius_coeffs[:, 1]
self.Arrhenius_Ea = self.Arrhenius_coeffs[:, 2]
if self.vectorize is True:
# for falloff and Troe
self.length_type4 = len(self.list_reaction_type4)
self.Arrhenius_A0 = torch.zeros([self.length_type4]).to(self.device)
self.Arrhenius_b0 = torch.zeros([self.length_type4]).to(self.device)
self.Arrhenius_Ea0 = torch.zeros([self.length_type4]).to(self.device)
self.Arrhenius_Ainf = torch.zeros([self.length_type4]).to(self.device)
self.Arrhenius_binf = torch.zeros([self.length_type4]).to(self.device)
self.Arrhenius_Eainf = torch.zeros([self.length_type4]).to(self.device)
self.efficiencies_coeffs_type4 = torch.ones(
[self.n_species, self.length_type4]).to(self.device)
self.length_type4_Troe = len(self.list_reaction_type4_Troe)
self.Troe_A = torch.zeros([self.length_type4_Troe]).to(self.device)
self.Troe_T1 = torch.zeros([self.length_type4_Troe]).to(self.device)
self.Troe_T2 = torch.zeros([self.length_type4_Troe]).to(self.device)
self.Troe_T3 = torch.zeros([self.length_type4_Troe]).to(self.device)
# for matrix size transfer
self.mat_transfer_type4 = torch.zeros([self.length_type4, self.n_reactions]).to(self.device)
self.mat_transfer_type4_Troe = torch.zeros(
[self.length_type4_Troe, self.n_reactions]).to(
self.device)
self.mat_transfer_type4_to_Troe = torch.zeros(
self.length_type4, self.length_type4_Troe).to(
self.device)
for i in range(self.length_type4):
index = self.list_reaction_type4[i]
self.Arrhenius_A0[i] = self.reaction[index]['A_0']
self.Arrhenius_b0[i] = self.reaction[index]['b_0']
self.Arrhenius_Ea0[i] = self.reaction[index]['Ea_0']
self.Arrhenius_Ainf[i] = self.reaction[index]['A']
self.Arrhenius_binf[i] = self.reaction[index]['b']
self.Arrhenius_Eainf[i] = self.reaction[index]['Ea']
self.efficiencies_coeffs_type4[:, i] = self.efficiencies_coeffs[:, index]
self.mat_transfer_type4[i, index] = 1
for i in range(self.length_type4_Troe):
index = self.list_reaction_type4_Troe[i]
self.Troe_A[i] = self.reaction[index]['Troe']['A']
self.Troe_T1[i] = self.reaction[index]['Troe']['T1']
self.Troe_T2[i] = self.reaction[index]['Troe']['T2']
self.Troe_T3[i] = self.reaction[index]['Troe']['T3']
self.mat_transfer_type4_Troe[i, index] = 1
index_in_type4 = self.list_id_Troe_in_type4[i]
self.mat_transfer_type4_to_Troe[index_in_type4, i] = 1