def run_pfa(args, solution_object,error,past):
if len(args.target) == 0: #If the target species are not specified, puke and die.
print("Please specify a target species.")
exit()
done = [] #Singleton to hold wether or not any more species can be cut from the simulation.
done.append(False)
threshold = .1 #Starting threshold value
threshold_i = .1
n = 1
error = [10.0] #Singleton to hold the error value of the previously ran simulation.
#Check to make sure that conditions exist
if args.conditions_file:
conditions_array = readin_conditions(str(args.conditions_file))
elif not args.conditions_file:
print("Conditions file not found")
exit()
sim_array = helper.setup_simulations(conditions_array,solution_object) #Turn conditions array into unran simulation objects for the original solution
ignition_delay_detailed = helper.simulate(sim_array) #Run simulations and process results
rate_edge_data = get_rates_pfa(sim_array, solution_object) #Get edge weight calculation data.
print("Testing for starting threshold value")
pfa_loop_control(solution_object, args, error, threshold, done, rate_edge_data, ignition_delay_detailed, conditions_array) #Trim the solution at that threshold and find the error.
while error[0] != 0: #While the error for trimming with that threshold value is greater than allowed.
threshold = threshold / 10 #Reduce the starting threshold value and try again.
threshold_i = threshold_i / 10
n = n + 1
pfa_loop_control(solution_object, args, error, threshold, done, rate_edge_data, ignition_delay_detailed, conditions_array)
if error[0] <= .02:
error[0] = 0
print("Starting with a threshold value of " + str(threshold))
sol_new = solution_object
past[0] = 0 #An integer representing the error introduced in the past simulation.
done[0] = False
while not done[0] and error[0] < args.error: #Run the simulation until nothing else can be cut.
sol_new = pfa_loop_control( solution_object, args, error, threshold, done, rate_edge_data, ignition_delay_detailed, conditions_array) #Trim at this threshold value and calculate error.
if args.error > error[0]: #If a new max species cut without exceeding what is allowed is reached, save that threshold.
max_t = threshold
#if (past == error[0]): #If error wasn't increased, increase the threshold at a higher rate.
# threshold = threshold + (threshold_i * 4)
past[0] = error[0]
#if (threshold >= .01):
# threshold_i = .01
threshold = threshold + threshold_i
threshold = round(threshold, n)
print("\nGreatest result: ")
sol_new = pfa_loop_control( solution_object, args, error, max_t, done, rate_edge_data, ignition_delay_detailed, conditions_array)
drgep_trimmed_file = soln2cti.write(sol_new) #Write the solution object with the greatest error that isn't over the allowed ammount.
return sol_new[1]
def obtain_cti_file_nicely_named(chemkinfilepath,
readComments=True,
original_ck_file='chem_annotated.inp'):
"""
Given a chemkin file path, this method reads in the chemkin file and species
dictionary into RMG objects, renames the species more intuitively, and then
saves the output as 'input_nicely_named.cti' to the same file path.
"""
from rmgpy.chemkin import loadChemkinFile
import os
import soln2cti
import cantera as ct
chemkinPath = os.path.join(chemkinfilepath, original_ck_file)
speciesDictPath = os.path.join(chemkinfilepath, 'species_dictionary.txt')
species, reactions = loadChemkinFile(chemkinPath,
speciesDictPath,
readComments=readComments,
useChemkinNames=False)
make_string_labels_independent(species)
for spec in species:
if len(spec.molecule) == 0:
print(spec)
# convert species
ct_species = [
spec.toCantera(useChemkinIdentifier=False) for spec in species
]
# convert reactions
# since this can return a rxn or list of reactions, this allows to make a list based on the returned type
ct_reactions = []
for rxn in reactions:
ct_rxn = rxn.toCantera(useChemkinIdentifier=False)
if isinstance(ct_rxn, list):
ct_reactions.extend(ct_rxn)
else:
ct_reactions.append(ct_rxn)
# save new file
gas = ct.Solution(thermo='IdealGas',
kinetics='GasKinetics',
species=ct_species,
reactions=ct_reactions)
new_file = soln2cti.write(gas)
# move the file to new location
os.rename(new_file, os.path.join(chemkinfilepath,
'input_nicely_named.cti'))
# save species dictionary
dictionary = ''
for spec in species:
dictionary += spec.toAdjacencyList() + '\n\n'
f = open(
os.path.join(chemkinfilepath, 'species_dictionary_nicely_named.txt'),
'w')
f.write(dictionary)
f.close()
print("Cleaning all species and reactions with " + element)
clean_species = ()
clean_reactions = ()
#loop once over the species
for i in mechanism_to_clean.species():
if not element in i.name:
clean_species += (i, )
#loop once over the reactions
for i in range(0, mechanism_to_clean.n_reactions):
if not element in mechanism_to_clean.reaction_equation(i):
reaction = mechanism_to_clean.reaction(i)
if hasattr(reaction, 'efficiencies'):
copy = {}
for key in reaction.efficiencies.keys():
if not re.search(element, key):
copy.update({key: reaction.efficiencies[key]})
reaction.efficiencies = copy
clean_reactions += (mechanism_to_clean.reaction(i), )
#print(mechanism_to_clean.element_names)
clean_mechanism = ct.Solution(thermo='IdealGas',
kinetics='GasKinetics',
species=clean_species,
reactions=clean_reactions)
#print(clean_species)
for x in clean_mechanism.reactions():
print(x, '\n')
print("writing cleaned file at ", cti_path_clean)
soln2cti.write(clean_mechanism, cti_path_clean)
def pymars(model_file,
conditions,
error,
method,
target_species,
retained_species=None,
run_sensitivity_analysis=False,
epsilon_star=0.1):
"""Driver function for pyMARS to reduce a model.
Parameters
----------
model_file : str
Cantera-format model to be reduced (e.g., 'mech.cti').
conditions : str
File with list of autoignition initial conditions.
error : float
Maximum error % for the reduced model.
method : {'DRG', 'DRGEP', 'PFA'}
Skeletal reduction method to use.
target_species: list of str
List of target species for reduction.
retained_species : list of str, optional
List of non-target species to always retain.
run_sensitivity_analysis : bool, optional
Flag to run sensitivity analysis after completing another method.
epsilon_star : float, optional
Epsilon^* value used to determine species for sensitivity analysis.
Returns
-------
Converted mechanism file
Trimmed Solution Object
Trimmed Mechanism file
Examples
--------
>>> pymars('gri30.cti', conditions_file, 10.0, 'DRGEP', ['CH4', 'O2'], retained_species=['N2'])
"""
solution_object = Solution(model_file)
final_error = [0]
if method == 'DRG':
reduced_model = run_drg(solution_object, conditions, error,
target_species, retained_species, model_file,
final_error)
elif method == 'PFA':
reduced_model = run_pfa(solution_object, conditions, error,
target_species, retained_species, model_file,
final_error)
elif method == 'DRGEP':
reduced_model = run_drgep(solution_object, conditions, error,
target_species, retained_species, model_file,
final_error)
if run_sensitivity_analysis:
reduced_model = run_sa(solution_object, reduced_model, epsilon_star,
final_error, conditions, target_species,
retained_species, error)
output_file = soln2cti.write(reduced_model)
for i in np.arange(len(phi)):
for filename in np.arange(len(mechanisms)):
for j in np.arange(len(fuels)):
for t in np.arange(len(temps)):
for p in np.arange(len(pressures)):
for truth in efficiency_manipulate:
conditionsTups.append([
mechanisms[filename], phi[i], fuels[j],
oxidizers[j], temps[t], pressures[p], truth
])
if True in efficiency_manipulate:
gas = ct.Solution(mechanisms[0])
gas = em.efficiency_rate_swap(gas)
gas.name = fuels[0] + '_' + mechanisms[0].rstrip('.cti').split('\\')[-1]
import soln2cti as ctiw
new_file = ctiw.write(gas)
#print(conditionsTups)
def solver(conditionsTup):
try:
if not conditionsTup[6]:
fuels = conditionsTup[2]
#oxidizer={'O2':0.5, 'N2':1.88}
oxidizer = conditionsTup[3]
gas = ct.Solution(conditionsTup[0])
pressures = conditionsTup[5]
#temps=[298]
temps = conditionsTup[4]
width = 0.03
def readin(args='none', **argv):
"""Main function for pyMARS
Parameters
----------
file:
Input mechanism file (ex. file='gri30.cti')
species:
Species to eliminate (ex. species='H, OH')
thermo:
Thermo data file if Chemkin format (ex. thermo= 'thermo.dat')
transport:
Transport data file if Chemkin format
plot:
plot ignition curve (ex. plot='y')
points:
print ignition point and sample range (ex. points='y')
writecsv:
write data to csv (ex. writecsv='y')
writehdf5:
write data to hdf5 (ex. writehdf5='y')
run_drg:
Run DRG model reduction
Returns
-------
Converted mechanism file
Trimmed Solution Object
Trimmed Mechanism file
Examples
--------
readin(file='gri30.cti', plot='y', species='OH, H')
"""
class args():
#package from terminal use case
if args is not 'none':
plot = args.plot
points = args.points
writecsv = args.writecsv
writehdf5 = args.writehdf5
data_file = args.file
thermo_file = args.thermo
transport_file = args.transport
run_drg = args.run_drg
threshold_values = args.thresholds
conditions_file = args.conditions
convert = args.convert
if args.species is None:
exclusion_list = []
else:
exclusion_list = [
str(item) for item in args.species.split(',')
]
#strip spaces
for i, sp in enumerate(exclusion_list):
exclusion_list[i] = sp.strip()
file_extension = os.path.splitext(args.data_file)[1]
if file_extension == ".cti" or file_extension == ".xml":
print("\nThis is an Cantera xml or cti file\n")
solution_object = ct.Solution(args.data_file)
if args.plot is True or args.writecsv is True or args.points is True or args.writehdf5 is True:
print 'running sim'
sim_result = autoignition_loop_control(solution_object, args)
if args.run_drg is True:
new_solution_objects = drg_loop_control(solution_object, args)
os.system('rm production_rates.hdf5')
os.system('rm mass_fractions.hdf5')
drg_trimmed_file = soln2cti.write(new_solution_objects[1])
try:
os.system('rm production_rates.hdf5')
except Exception:
pass
if args.convert is True:
soln2ck.write(solution_object)
elif file_extension == ".inp" or file_extension == ".dat" or file_extension == ".txt":
print("\n\nThis is a Chemkin file")
#convert file to cti
converted_file_name = convert(args.data_file, args.thermo_file,
args.transport_file)
else:
print("\n\nFile type not supported")
def cti_write(x={},original_cti='',master_rxns='',master_index=[]):
if not original_cti:
raise Exception('Please provide a name for the original mechanism file and try again.')
if not master_rxns and np.any(master_index):
raise Exception('Please provide a mechanism file for reactions analysed with master equation or leave master_index empty')
if master_rxns and not np.any(master_index):
raise Exception('Please provide master_index, a non-empty list of reaction numbers from original file which are analysed with master equation.')
if not master_rxns and not master_index:
master_index=np.ones(ct.Solution(original_cti).n_reactions,dtype=bool)
elif master_rxns and np.any(master_index):
temp=np.ones(ct.Solution(original_cti).n_reactions,dtype=bool)
for j in np.arange(len(master_index)):
temp[master_index[j]-1]=False
master_index=temp
lineList=[]
with open(original_cti) as f:
lineList=f.readlines()
done=False
count=0
while not done or count<len(lineList):
if 'Reaction data' in lineList[count] or 'Reaction Data' in lineList[count] or 'reaction data' in lineList[count]:
done=True
lineList=lineList[0:count-1]
else:count+=1
with open('tempcti.cti','w') as p:
p.writelines(lineList)
NewModel=ct.Solution('tempcti.cti')
original_mechanism=ct.Solution(original_cti)
master_count=0
if master_rxns:
master_reactions=ct.Solution(master_rxns)
for i in np.arange(original_mechanism.n_reactions):
if master_index[i]==True:
NewModel.add_reaction(original_mechanism.reaction(i))
elif master_index[i]==False:
NewModel.add_reaction(master_reactions.reaction(master_count))
master_count+=1
if x=={}:
for j in np.arange(original_mechanism.n_reactions):
if master_index[j]:
if 'ThreeBodyReaction' in str(type(original_mechanism.reaction(j))):
NewModel.reaction(j).rate=original_mechanism.reaction(j).rate
elif 'ElementaryReaction' in str(type(original_mechanism.reaction(j))):
NewModel.reaction(j).rate=original_mechanism.reaction(j).rate
elif 'FalloffReaction' in str(type(original_mechanism.reaction(j))):
NewModel.reaction(j).high_rate=original_mechanism.reaction(j).high_rate
NewModel.reaction(j).low_rate=original_mechanism.reaction(j).low_rate
if original_mechanism.reaction(j).falloff.type=='Troe':
NewModel.reaction(j).falloff=original_mechanism.reaction(j).falloff
if original_mechanism.reaction(j).falloff.type=='Sri':
NewModel.reaction(j).falloff=original_mechanism.reaction(j).falloff
elif 'ChemicallyActivatedReaction' in str(type(original_mechanism.reaction(j))):
NewModel.reaction(j).high_rate=original_mechanism.reaction(j).high_rate
NewModel.reaction(j).low_rate=original_mechanism.reaction(j).low_rate
if original_mechanism.reaction(j).falloff.type=='Troe':
NewModel.reaction(j).falloff=original_mechanism.reaction(j).falloff
if original_mechanism.reaction(j).falloff.type=='Sri':
NewModel.reaction(j).falloff=original_mechanism.reaction(j).falloff
elif 'PlogReaction' in str(type(original_mechanism.reaction(j))):
NewModel.reaction(j).rates=original_mechanism.reaction(j).rates
elif 'ChebyshevReaction' in str(type(original_mechanism.reaction(j))):
NewModel.reaction(j).set_parameters(original_mechanism.reaction(j).Tmin,original_mechanism.reaction(j).Tmax,original_mechanism.reaction(j).Pmin,original_mechanism.reaction(j).Pmax,original_mechanism.reaction(j).coeffs)
if x!={}:
for j in np.arange(original_mechanism.n_reactions):
if master_index[j]:
try:
if 'ThreeBodyReaction' in str(type(original_mechanism.reaction(j))):
A=original_mechanism.reaction(j).rate.pre_exponential_factor
n=original_mechanism.reaction(j).rate.temperature_exponent
Ea=original_mechanism.reaction(j).rate.activation_energy
NewModel.reaction(j).rate=ct.Arrhenius(A*np.exp(x['r'+str(j)]['A']),n+x['r'+str(j)]['n'],Ea+x['r'+str(j)]['Ea'])
elif 'ElementaryReaction' in str(type(original_mechanism.reaction(j))):
A=original_mechanism.reaction(j).rate.pre_exponential_factor
n=original_mechanism.reaction(j).rate.temperature_exponent
Ea=original_mechanism.reaction(j).rate.activation_energy
NewModel.reaction(j).rate=ct.Arrhenius(A*np.exp(x['r'+str(j)]['A']),n+x['r'+str(j)]['n'],Ea+x['r'+str(j)]['Ea'])
elif 'FalloffReaction' in str(type(original_mechanism.reaction(j))):
A=original_mechanism.reaction(j).high_rate.pre_exponential_factor
n=original_mechanism.reaction(j).high_rate.temperature_exponent
Ea=original_mechanism.reaction(j).high_rate.activation_energy
NewModel.reaction(j).high_rate=ct.Arrhenius(A*np.exp(x['r'+str(j)]['A']),n+x['r'+str(j)]['n'],Ea+x['r'+str(j)]['Ea'])
A=original_mechanism.reaction(j).low_rate.pre_exponential_factor
n=original_mechanism.reaction(j).low_rate.temperature_exponent
Ea=original_mechanism.reaction(j).low_rate.activation_energy
NewModel.reaction(j).low_rate=ct.Arrhenius(A*np.exp(x['r'+str(j)]['A']),n+x['r'+str(j)]['n'],Ea+x['r'+str(j)]['Ea'])
if original_mechanism.reaction(j).falloff.type=='Troe':
NewModel.reaction(j).falloff=original_mechanism.reaction(j).falloff
if original_mechanism.reaction(j).falloff.type=='Sri':
NewModel.reaction(j).falloff=original_mechanism.reaction(j).falloff
elif 'ChemicallyActivatedReaction' in str(type(original_mechanism.reaction(j))):
A=original_mechanism.reaction(j).high_rate.pre_exponential_factor
n=original_mechanism.reaction(j).high_rate.temperature_exponent
Ea=original_mechanism.reaction(j).high_rate.activation_energy
NewModel.reaction(j).high_rate=ct.Arrhenius(A*np.exp(x['r'+str(j)]['A']),n+x['r'+str(j)]['n'],Ea+x['r'+str(j)]['Ea'])
A=original_mechanism.reaction(j).low_rate.pre_exponential_factor
n=original_mechanism.reaction(j).low_rate.temperature_exponent
Ea=original_mechanism.reaction(j).low_rate.activation_energy
NewModel.reaction(j).low_rate=ct.Arrhenius(A*np.exp(x['r'+str(j)]['A']),n+x['r'+str(j)]['n'],Ea+x['r'+str(j)]['Ea'])
if original_mechanism.reaction(j).falloff.type=='Troe':
NewModel.reaction(j).falloff=original_mechanism.reaction(j).falloff
if original_mechanism.reaction(j).falloff.type=='Sri':
NewModel.reaction(j).falloff=original_mechanism.reaction(j).falloff
elif 'PlogReaction' in str(type(original_mechanism.reaction(j))):
NewModel.reaction(j).rates=original_mechanism.reaction(j).rates
elif 'ChebyshevReaction' in str(type(original_mechanism.reaction(j))):
NewModel.reaction(j).set_parameters(original_mechanism.reaction(j).Tmin,original_mechanism.reaction(j).Tmax,original_mechanism.reaction(j).Pmin,original_mechanism.reaction(j).Pmax,original_mechanism.reaction(j).coeffs)
except:
if 'ThreeBodyReaction' in str(type(original_mechanism.reaction(j))):
NewModel.reaction(j).rate=original_mechanism.reaction(j).rate
elif 'ElementaryReaction' in str(type(original_mechanism.reaction(j))):
NewModel.reaction(j).rate=original_mechanism.reaction(j).rate
elif 'FalloffReaction' in str(type(original_mechanism.reaction(j))):
NewModel.reaction(j).high_rate=original_mechanism.reaction(j).high_rate
NewModel.reaction(j).low_rate=original_mechanism.reaction(j).low_rate
if original_mechanism.reaction(j).falloff.type=='Troe':
NewModel.reaction(j).falloff=original_mechanism.reaction(j).falloff
if original_mechanism.reaction(j).falloff.type=='Sri':
NewModel.reaction(j).falloff=original_mechanism.reaction(j).falloff
elif 'ChemicallyActivatedReaction' in str(type(original_mechanism.reaction(j))):
NewModel.reaction(j).high_rate=original_mechanism.reaction(j).high_rate
NewModel.reaction(j).low_rate=original_mechanism.reaction(j).low_rate
if original_mechanism.reaction(j).falloff.type=='Troe':
NewModel.reaction(j).falloff=original_mechanism.reaction(j).falloff
if original_mechanism.reaction(j).falloff.type=='Sri':
NewModel.reaction(j).falloff=original_mechanism.reaction(j).falloff
elif 'PlogReaction' in str(type(original_mechanism.reaction(j))):
NewModel.reaction(j).rates=original_mechanism.reaction(j).rates
elif 'ChebyshevReaction' in str(type(original_mechanism.reaction(j))):
NewModel.reaction(j).set_parameters(original_mechanism.reaction(j).Tmin,original_mechanism.reaction(j).Tmax,original_mechanism.reaction(j).Pmin,original_mechanism.reaction(j).Pmax,original_mechanism.reaction(j).coeffs)
new_file=ctiw.write(NewModel)
return new_file
coeffs = [[2.0, 1.5, 2.5]]
#coeffs=[[11.0]]
#coeffs=[[7.5]]
#coeffs=[[1.0]]
coeffs = [[
2.0, 3.5, 3.0, 2.5, 5.0, 4.5, 4.0, 6.5, 6.5, 6.0, 6.0, 6.0, 5.5, 5.5, 5.5,
5.5
]]
#coeffs=[[11.0],
# [9.0]]
for i in nominal_models:
gas = ct.Solution(i)
gas.name = 'igDelayRateSwap_' + i.split('\\')[-1].rstrip('.cti')
gas2 = em.efficiency_rate_swap(gas, [val])
newfilename = ntpath.dirname(i) + '\\modified2_' + ntpath.basename(i)
new_file = ctiw.write(gas2, newfilename)
modified_models.append(new_file)
conditionsTup = []
for n in np.arange(len(nominal_models)):
for p in np.arange(len(P)):
for i in np.arange(len(T)):
for subf in np.arange(len(fuels[n])):
oxidizer = {}
oxidizer = {
'O2': coeffs[n][subf],
'N2': 3.76 * coeffs[n][subf]
}
conditionsTup.append([
nominal_models[n], modified_models[n], P[p], phi,
fuels[n][subf], oxidizer, T[i]
def cti_write2(x={}, original_cti='', master_rxns='', master_index=[], MP={}):
print(MP)
if not original_cti:
raise Exception(
'Please provide a name for the original mechanism file and try again.'
)
if not master_rxns and np.any(master_index):
raise Exception(
'Please provide a mechanism file for reactions analysed with master equation or leave master_index empty'
)
if master_rxns and not np.any(master_index):
raise Exception(
'Please provide master_index, a non-empty list of reaction numbers from original file which are analysed with master equation.'
)
if not master_rxns and not master_index:
master_index = np.ones(ct.Solution(original_cti).n_reactions,
dtype=bool)
elif master_rxns and np.any(master_index):
temp = np.ones(ct.Solution(original_cti).n_reactions, dtype=bool)
for j in np.arange(len(master_index)):
temp[master_index[j] - 1] = False
master_index = temp
lineList = []
with open(original_cti) as f:
lineList = f.readlines()
done = False
count = 0
while not done or count < len(lineList):
if 'Reaction data' in lineList[count] or 'Reaction Data' in lineList[
count] or 'reaction data' in lineList[count]:
done = True
lineList = lineList[0:count - 1]
else:
count += 1
with open('tempcti.cti', 'w') as p:
p.writelines(lineList)
NewModel = ct.Solution('tempcti.cti')
original_mechanism = ct.Solution(original_cti)
original_rxn_count = 0
master_rxn_eqs = []
if master_rxns:
with open(master_rxns) as f:
reactionsList = f.readlines()
lineList = lineList + reactionsList
with open('masterTemp.cti', 'w') as f:
f.writelines(lineList)
master_reactions = ct.Solution('masterTemp.cti')
master_rxn_eqs = master_reactions.reaction_equations()
original_rxn_eqs = []
for i in np.arange(original_mechanism.n_reactions):
if master_index[i]:
NewModel.add_reaction(original_mechanism.reaction(i))
original_rxn_count += 1
original_rxn_eqs.append(original_mechanism.reaction_equation(i))
# if 'FalloffReaction' in str(type(original_mechanism.reaction(i))):
# print(original_mechanism.reaction(i).high_rate)
# print(original_mechanism.reaction(i).low_rate)
if master_rxns:
for i in np.arange(master_reactions.n_reactions):
print(master_reactions.reaction(i).rate)
NewModel.add_reaction(master_reactions.reaction(i))
#print(master_reactions.reaction(0).rate)
if x == {}:
for j in np.arange(original_rxn_count - 1):
#if master_index[j]:
#print(str(type(original_mechanism.reaction(j))),str(type(NewModel.reaction(j))))
if 'ThreeBodyReaction' in str(type(NewModel.reaction(j))):
NewModel.reaction(j).rate = NewModel.reaction(j).rate
elif 'ElementaryReaction' in str(type(NewModel.reaction(j))):
NewModel.reaction(j).rate = NewModel.reaction(j).rate
elif 'FalloffReaction' in str(type(NewModel.reaction(j))):
NewModel.reaction(j).high_rate = NewModel.reaction(j).high_rate
NewModel.reaction(j).low_rate = NewModel.reaction(j).low_rate
if NewModel.reaction(j).falloff.type == 'Troe':
NewModel.reaction(j).falloff = NewModel.reaction(j).falloff
if NewModel.reaction(j).falloff.type == 'Sri':
NewModel.reaction(j).falloff = NewModel.reaction(j).falloff
elif 'ChemicallyActivatedReaction' in str(
type(NewModel.reaction(j))):
NewModel.reaction(j).high_rate = NewModel.reaction(j).high_rate
NewModel.reaction(j).low_rate = NewModel.reaction(j).low_rate
if NewModel.reaction(j).falloff.type == 'Troe':
NewModel.reaction(j).falloff = NewModel.reaction(j).falloff
if NewModel.reaction(j).falloff.type == 'Sri':
NewModel.reaction(j).falloff = NewModel.reaction(j).falloff
elif 'PlogReaction' in str(type(NewModel.reaction(j))):
NewModel.reaction(j).rates = NewModel.reaction(j).rates
elif 'ChebyshevReaction' in str(type(NewModel.reaction(j))):
NewModel.reaction(j).set_parameters(
NewModel.reaction(j).Tmin,
NewModel.reaction(j).Tmax,
NewModel.reaction(j).Pmin,
NewModel.reaction(j).Pmax,
NewModel.reaction(j).coeffs)
#Rinv = 1/R #cal/mol*K
E = 1 #going test for energy
T = 4.186e3
if x != {}:
for j in np.arange(original_rxn_count - 1):
#if master_index[j]:
try:
if 'ThreeBodyReaction' in str(type(NewModel.reaction(j))):
A = NewModel.reaction(j).rate.pre_exponential_factor
n = NewModel.reaction(j).rate.temperature_exponent
Ea = NewModel.reaction(j).rate.activation_energy
NewModel.reaction(j).rate = ct.Arrhenius(
A * np.exp(x['r' + str(j)]['A']),
n + x['r' + str(j)]['n'],
Ea + x['r' + str(j)]['Ea'] * T)
elif 'ElementaryReaction' in str(type(NewModel.reaction(j))):
A = NewModel.reaction(j).rate.pre_exponential_factor
n = NewModel.reaction(j).rate.temperature_exponent
Ea = NewModel.reaction(j).rate.activation_energy
NewModel.reaction(j).rate = ct.Arrhenius(
A * np.exp(x['r' + str(j)]['A']),
n + x['r' + str(j)]['n'],
Ea + x['r' + str(j)]['Ea'] * T)
elif 'FalloffReaction' in str(type(NewModel.reaction(j))):
A = NewModel.reaction(j).high_rate.pre_exponential_factor
n = NewModel.reaction(j).high_rate.temperature_exponent
Ea = NewModel.reaction(j).high_rate.activation_energy
NewModel.reaction(j).high_rate = ct.Arrhenius(
A * np.exp(x['r' + str(j)]['A']),
n + x['r' + str(j)]['n'],
Ea + x['r' + str(j)]['Ea'] * T)
A = NewModel.reaction(j).low_rate.pre_exponential_factor
n = NewModel.reaction(j).low_rate.temperature_exponent
Ea = NewModel.reaction(j).low_rate.activation_energy
NewModel.reaction(j).low_rate = ct.Arrhenius(
A * np.exp(x['r' + str(j)]['A']),
n + x['r' + str(j)]['n'],
Ea + x['r' + str(j)]['Ea'] * T)
if NewModel.reaction(j).falloff.type == 'Troe':
NewModel.reaction(j).falloff = NewModel.reaction(
j).falloff
if NewModel.reaction(j).falloff.type == 'Sri':
NewModel.reaction(j).falloff = NewModel.reaction(
j).falloff
elif 'ChemicallyActivatedReaction' in str(
type(NewModel.reaction(j))):
A = NewModel.reaction(j).high_rate.pre_exponential_factor
n = NewModel.reaction(j).high_rate.temperature_exponent
Ea = NewModel.reaction(j).high_rate.activation_energy
NewModel.reaction(j).high_rate = ct.Arrhenius(
A * np.exp(x['r' + str(j)]['A']),
n + x['r' + str(j)]['n'],
Ea + x['r' + str(j)]['Ea'] * T)
A = NewModel.reaction(j).low_rate.pre_exponential_factor
n = NewModel.reaction(j).low_rate.temperature_exponent
Ea = NewModel.reaction(j).low_rate.activation_energy
NewModel.reaction(j).low_rate = ct.Arrhenius(
A * np.exp(x['r' + str(j)]['A']),
n + x['r' + str(j)]['n'],
Ea + x['r' + str(j)]['Ea'] * T)
if NewModel.reaction(j).falloff.type == 'Troe':
NewModel.reaction(j).falloff = NewModel.reaction(
j).falloff
if NewModel.reaction(j).falloff.type == 'Sri':
NewModel.reaction(j).falloff = NewModel.reaction(
j).falloff
elif 'PlogReaction' in str(type(NewModel.reaction(j))):
for number, reactions in enumerate(
NewModel.reaction(j).rates):
A = NewModel.reaction(
j)[number][1].pre_exponential_factor
n = NewModel.reaction(
j)[number][1].temperature_exponent
Ea = NewModel.reaction(j)[number][1].activation_energy
NewModel.reaction(j)[number][1] = ct.Arrhenius(
A * np.exp(x['r' + str(j)]['A']),
n + x['r' + str(j)]['n'],
Ea + x['r' + str(j)]['Ea'] * T)
NewModel.reaction(j).rates = NewModel.reaction(j).rates
elif 'ChebyshevReaction' in str(
type(original_mechanism.reaction(j))):
NewModel.reaction(j).set_parameters(
NewModel.reaction(j).Tmin,
NewModel.reaction(j).Tmax,
NewModel.reaction(j).Pmin,
NewModel.reaction(j).Pmax,
NewModel.reaction(j).coeffs)
except:
print('we are in the except statment in marks code', j)
if 'ThreeBodyReaction' in str(type(NewModel.reaction(j))):
NewModel.reaction(j).rate = NewModel.reaction(j).rate
elif 'ElementaryReaction' in str(type(NewModel.reaction(j))):
NewModel.reaction(j).rate = NewModel.reaction(j).rate
elif 'FalloffReaction' in str(type(NewModel.reaction(j))):
NewModel.reaction(j).high_rate = NewModel.reaction(
j).high_rate
NewModel.reaction(j).low_rate = NewModel.reaction(
j).low_rate
if NewModel.reaction(j).falloff.type == 'Troe':
NewModel.reaction(j).falloff = NewModel.reaction(
j).falloff
if NewModel.reaction(j).falloff.type == 'Sri':
NewModel.reaction(j).falloff = NewModel.reaction(
j).falloff
elif 'ChemicallyActivatedReaction' in str(
type(NewModel.reaction(j))):
NewModel.reaction(j).high_rate = NewModel.reaction(
j).high_rate
NewModel.reaction(j).low_rate = NewModel.reaction(
j).low_rate
if NewModel.reaction(j).falloff.type == 'Troe':
NewModel.reaction(j).falloff = NewModel.reaction(
j).falloff
if NewModel.reaction(j).falloff.type == 'Sri':
NewModel.reaction(j).falloff = NewModel.reaction(
j).falloff
elif 'PlogReaction' in str(type(NewModel.reaction(j))):
NewModel.reaction(j).rates = NewModel.reaction(j).rates
elif 'ChebyshevReaction' in str(type(NewModel.reaction(j))):
NewModel.reaction(j).set_parameters(
NewModel.reaction(j).Tmin,
NewModel.reaction(j).Tmax,
NewModel.reaction(j).Pmin,
NewModel.reaction(j).Pmax,
NewModel.reaction(j).coeffs)
if MP != {}:
print('insdie the MP if statment')
for j in np.arange(original_rxn_count, NewModel.n_reactions):
try:
if 'ThreeBodyReaction' in str(type(NewModel.reaction(j))):
A = NewModel.reaction(j).rate.pre_exponential_factor
n = NewModel.reaction(j).rate.temperature_exponent
Ea = NewModel.reaction(j).rate.activation_energy
NewModel.reaction(j).rate = ct.Arrhenius(
A * np.exp(MP['r' + str(j)]['A']),
n + MP['r' + str(j)]['n'],
Ea + MP['r' + str(j)]['Ea'] * E)
elif 'ElementaryReaction' in str(type(NewModel.reaction(j))):
A = NewModel.reaction(j).rate.pre_exponential_factor
n = NewModel.reaction(j).rate.temperature_exponent
Ea = NewModel.reaction(j).rate.activation_energy
NewModel.reaction(j).rate = ct.Arrhenius(
A * np.exp(MP['r' + str(j)]['A']),
n + MP['r' + str(j)]['n'],
Ea + MP['r' + str(j)]['Ea'] * E)
elif 'FalloffReaction' in str(type(NewModel.reaction(j))):
A = NewModel.reaction(j).high_rate.pre_exponential_factor
n = NewModel.reaction(j).high_rate.temperature_exponent
Ea = NewModel.reaction(j).high_rate.activation_energy
NewModel.reaction(j).high_rate = ct.Arrhenius(
A * np.exp(MP['r' + str(j)]['A']),
n + MP['r' + str(j)]['n'],
Ea + MP['r' + str(j)]['Ea'] * E)
A = NewModel.reaction(j).low_rate.pre_exponential_factor
n = NewModel.reaction(j).low_rate.temperature_exponent
Ea = NewModel.reaction(j).low_rate.activation_energy
NewModel.reaction(j).low_rate = ct.Arrhenius(
A * np.exp(MP['r' + str(j)]['A']),
n + MP['r' + str(j)]['n'],
Ea + MP['r' + str(j)]['Ea'] * E)
if NewModel.reaction(j).falloff.type == 'Troe':
NewModel.reaction(j).falloff = NewModel.reaction(
j).falloff
if NewModel.reaction(j).falloff.type == 'Sri':
NewModel.reaction(j).falloff = NewModel.reaction(
j).falloff
elif 'ChemicallyActivatedReaction' in str(
type(NewModel.reaction(j))):
A = NewModel.reaction(j).high_rate.pre_exponential_factor
n = NewModel.reaction(j).high_rate.temperature_exponent
Ea = NewModel.reaction(j).high_rate.activation_energy
NewModel.reaction(j).high_rate = ct.Arrhenius(
A * np.exp(MP['r' + str(j)]['A']),
n + MP['r' + str(j)]['n'],
Ea + MP['r' + str(j)]['Ea'] * E)
A = NewModel.reaction(j).low_rate.pre_exponential_factor
n = NewModel.reaction(j).low_rate.temperature_exponent
Ea = NewModel.reaction(j).low_rate.activation_energy
NewModel.reaction(j).low_rate = ct.Arrhenius(
A * np.exp(MP['r' + str(j)]['A']),
n + MP['r' + str(j)]['n'],
Ea + MP['r' + str(j)]['Ea'] * E)
if NewModel.reaction(j).falloff.type == 'Troe':
NewModel.reaction(j).falloff = NewModel.reaction(
j).falloff
if NewModel.reaction(j).falloff.type == 'Sri':
NewModel.reaction(j).falloff = NewModel.reaction(
j).falloff
elif 'PlogReaction' in str(type(NewModel.reaction(j))):
for number, reactions in enumerate(
NewModel.reaction(j).rates):
A = NewModel.reaction(
j)[number][1].pre_exponential_factor
n = NewModel.reaction(
j)[number][1].temperature_exponent
Ea = NewModel.reaction(j)[number][1].activation_energy
NewModel.reaction(j)[number][1] = ct.Arrhenius(
A * np.exp(MP['r' + str(j)]['A']),
n + MP['r' + str(j)]['n'],
Ea + MP['r' + str(j)]['Ea'] * E)
NewModel.reaction(j).rates = NewModel.reaction(j).rates
elif 'ChebyshevReaction' in str(
type(original_mechanism.reaction(j))):
NewModel.reaction(j).set_parameters(
NewModel.reaction(j).Tmin,
NewModel.reaction(j).Tmax,
NewModel.reaction(j).Pmin,
NewModel.reaction(j).Pmax,
NewModel.reaction(j).coeffs)
except:
print('we are in the except statment in marks code', j)
if 'ThreeBodyReaction' in str(type(NewModel.reaction(j))):
NewModel.reaction(j).rate = NewModel.reaction(j).rate
elif 'ElementaryReaction' in str(type(NewModel.reaction(j))):
NewModel.reaction(j).rate = NewModel.reaction(j).rate
elif 'FalloffReaction' in str(type(NewModel.reaction(j))):
NewModel.reaction(j).high_rate = NewModel.reaction(
j).high_rate
NewModel.reaction(j).low_rate = NewModel.reaction(
j).low_rate
if NewModel.reaction(j).falloff.type == 'Troe':
NewModel.reaction(j).falloff = NewModel.reaction(
j).falloff
if NewModel.reaction(j).falloff.type == 'Sri':
NewModel.reaction(j).falloff = NewModel.reaction(
j).falloff
elif 'ChemicallyActivatedReaction' in str(
type(NewModel.reaction(j))):
NewModel.reaction(j).high_rate = NewModel.reaction(
j).high_rate
NewModel.reaction(j).low_rate = NewModel.reaction(
j).low_rate
if NewModel.reaction(j).falloff.type == 'Troe':
NewModel.reaction(j).falloff = NewModel.reaction(
j).falloff
if NewModel.reaction(j).falloff.type == 'Sri':
NewModel.reaction(j).falloff = NewModel.reaction(
j).falloff
elif 'PlogReaction' in str(type(NewModel.reaction(j))):
NewModel.reaction(j).rates = NewModel.reaction(j).rates
elif 'ChebyshevReaction' in str(type(NewModel.reaction(j))):
NewModel.reaction(j).set_parameters(
NewModel.reaction(j).Tmin,
NewModel.reaction(j).Tmax,
NewModel.reaction(j).Pmin,
NewModel.reaction(j).Pmax,
NewModel.reaction(j).coeffs)
new_file = ctiw.write(NewModel)
return new_file, original_rxn_eqs, master_rxn_eqs
OUTPUT_REACTONS_FILE_NAME)
write_reactions.write_factors(best_factors, OUTPUT_FACTORS_FILE_NAME)
# use solution to save ck or cti file
if OUTPUT_CK:
gas = ct.Solution(thermo='IdealGas',
kinetics='GasKinetics',
species=species,
reactions=best_reactions)
soln2ck.write(gas)
if OUTPUT_CTI:
gas = ct.Solution(thermo='IdealGas',
kinetics='GasKinetics',
species=species,
reactions=best_reactions)
soln2cti.write(gas)
t_iteration_1 = time.time()
#print('Counter: %d, Loss: %.5f, Time cost: %.2f s' % (counter, loss, (t_iteration_1 - t_iteration_0)))
num_space = 5 - len(str(counter))
print('Counter:%s%d, Loss: %.5f, Time cost: %.2f s' %
(num_space * ' ', counter, loss, (t_iteration_1 - t_iteration_0)))
# end metrics
if loss <= MIN_LOSS or counter >= MAX_GAUSS:
if loss == original_loss:
print(
'Did not find a better mech, please increase MAX_GAUSS and retry.'
)
exit()
else:
def readin(args='none', **argv):
"""Main function for pyMARS
Parameters
----------
file:
Input mechanism file (ex. file='gri30.cti')
species:
Species to eliminate (ex. species='H, OH')
thermo:
Thermo data file if Chemkin format (ex. thermo= 'thermo.dat')
transport:
Transport data file if Chemkin format
plot:
plot ignition curve (ex. plot='y')
points:
print ignition point and sample range (ex. points='y')
writecsv:
write data to csv (ex. writecsv='y')
writehdf5:
write data to hdf5 (ex. writehdf5='y')
run_drg:
Run DRG model reduction
Returns
-------
Converted mechanism file
Trimmed Solution Object
Trimmed Mechanism file
Examples
--------
readin(file='gri30.cti', plot='y', species='OH, H')
"""
class args():
#package from terminal use case
if args is not 'none':
plot = args.plot
points = args.points
writecsv = args.writecsv
writehdf5 = args.writehdf5
data_file= args.file
thermo_file = args.thermo
transport_file = args.transport
run_drg = args.run_drg
threshold_values = args.thresholds
conditions_file = args.conditions
convert = args.convert
if args.species is None:
exclusion_list = []
else:
exclusion_list = [str(item) for item in args.species.split(',')]
#strip spaces
for i, sp in enumerate(exclusion_list):
exclusion_list[i]=sp.strip()
file_extension= os.path.splitext(args.data_file)[1]
if file_extension == ".cti" or file_extension == ".xml":
print("\nThis is an Cantera xml or cti file\n")
solution_object = ct.Solution(args.data_file)
if args.plot is True or args.writecsv is True or args.points is True or args.writehdf5 is True:
print 'running sim'
sim_result = autoignition_loop_control(solution_object, args)
if args.run_drg is True:
new_solution_objects = drg_loop_control(solution_object, args)
os.system('rm production_rates.hdf5')
os.system('rm mass_fractions.hdf5')
drg_trimmed_file = soln2cti.write(new_solution_objects[1])
try:
os.system('rm production_rates.hdf5')
except Exception:
pass
if args.convert is True:
soln2ck.write(solution_object)
elif file_extension == ".inp" or file_extension == ".dat" or file_extension == ".txt":
print("\n\nThis is a Chemkin file")
#convert file to cti
converted_file_name = convert(args.data_file, args.thermo_file, args.transport_file)
else:
print("\n\nFile type not supported")
def run_drgep(args, solution_object):
"""
Function to run the drgep method for model reduction
Parameters
----------
solution_object: ct._Solutuion object
An object that represents the solution to be trimmed.
args: args object
An object that contains the following:
file:
Input mechanism file (ex. file='gri30.cti')
species:
Species to eliminate (ex. species='H, OH')
thermo:
Thermo data file if Chemkin format (ex. thermo= 'thermo.dat')
transport:
Transport data file if Chemkin format
plot:
plot ignition curve (ex. plot='y')
points:
print ignition point and sample range (ex. points='y')
writecsv:
write data to csv (ex. writecsv='y')
writehdf5:
write data to hdf5 (ex. writehdf5='y')
run_drg:
Run DRG model reduction
run_drgep:
Run drgep model.
error:
Maximum ammount of error allowed.
keepers: list of strings
The string names of the species that should be kept in the model no matter what.
targets: list of strings
The string names of the species that should be used as target species.
"""
#Set up variables
if len(args.target
) == 0: #If the target species are not specified, puke and die.
print "Please specify a target species."
exit()
done = [
] #Singleton to hold wether or not any more species can be cut from the simulation.
done.append(False)
threshold = .1 #Starting threshold value
threshold_i = .1
error = [
10.0
] #Singleton to hold the error value of the previously ran simulation.
try:
os.system('rm mass_fractions.hdf5')
except Exception:
pass
#Find overall interaction coefficients.
detailed_result = autoignition_loop_control(
solution_object, args
) #The simulation needs to be ran to make the mass_fractions file which has the info to calucalte edge weights I think?
detailed_result.test.close()
ignition_delay_detailed = np.array(detailed_result.tau_array)
rate_edge_data = get_rates(
'mass_fractions.hdf5',
solution_object) #Get edge weight calculation data.
max_dic = make_dic_drgep(
solution_object, rate_edge_data,
args.target) #Make a dictionary of overall interaction coefficients.
print "Testing for starting threshold value"
drgep_loop_control(
solution_object, args, error, threshold, done,
max_dic) #Trim the solution at that threshold and find the error.
while error[
0] != 0: #While the error for trimming with that threshold value is greater than allowed.
threshold = threshold / 10 #Reduce the starting threshold value and try again.
threshold_i = threshold_i / 10
drgep_loop_control(solution_object, args, error, threshold, done,
max_dic)
print("Starting with a threshold value of " + str(threshold))
sol_new = solution_object
past = 0 #An integer representing the error introduced in the past simulation.
done[0] = False
while not done[0] and error[
0] < args.error: #Run the simulation until nothing else can be cut.
sol_new = drgep_loop_control(
solution_object, args, error, threshold, done,
max_dic) #Trim at this threshold value and calculate error.
if args.error > error[
0]: #If a new max species cut without exceeding what is allowed is reached, save that threshold.
max_t = threshold
#if (past == error[0]): #If error wasn't increased, increase the threshold at a higher rate.
# threshold = threshold + (threshold_i * 4)
past = error[0]
#if (threshold >= .01):
# threshold_i = .01
threshold = threshold + threshold_i
print "\nGreatest result: "
sol_new = drgep_loop_control(solution_object, args, error, max_t, done,
max_dic)
if os.path.exsists("production_rates.hdf5"):
os.system('rm production_rates.hdf5')
if os.path.exsists('mass_fractions.hdf5'):
os.system('rm mass_fractions.hdf5')
drgep_trimmed_file = soln2cti.write(
sol_new
) #Write the solution object with the greatest error that isn't over the allowed ammount.
try:
os.system('rm production_rates.hdf5')
except Exception:
pass