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 run_sa(original_model, reduced_model, ep_star, final_error, args):
print(final_error)
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, original_model
) #Turn conditions array into unran simulation objects for the original solution
id_detailed = helper.simulate(
sim_array) #Run simulations and process results
rate_edge_data = get_rates(
sim_array, original_model) #Get edge weight calculation data.
drgep_coeffs = make_dic_drgep(
original_model, rate_edge_data,
args.target) #Make a dictionary of overall interaction coefficients.
if (id_detailed.all() == 0): #Ensure that ignition occured
print("Original model did not ignite. Check initial conditions.")
exit()
old = reduced_model
while True:
og_sn = [] #Original species names
new_sn = [] #Species names in current reduced model
keep = [] #Species retained from removals
og_excl = [
] #Species that will be excluded from the final model (Reduction will be preformed on original model)
species_objex = old.species()
for sp in species_objex:
new_sn.append(sp.name)
species_objex = original_model.species()
for sp in species_objex:
og_sn.append(sp.name)
for sp in og_sn:
if sp in new_sn:
keep.append(sp)
for sp in original_model.species():
if not (sp.name in keep):
og_excl.append(sp.name)
limbo = create_limbo(old, ep_star, drgep_coeffs,
args.keepers) #Find all the species in limbo.
if len(limbo) == 0:
return old
print("In limbo:")
print(limbo)
dic = get_limbo_dic(
original_model, old, limbo, final_error, args, id_detailed,
conditions_array
) #Calculate error for removing each limbo species.
rm = dic_lowest(dic) #Species that should be removed (Lowest error).
exclude = [rm]
for sp in og_excl: #Add to list of species that should be excluded from final model.
exclude.append(sp)
print()
print("attempting to remove " + rm)
new_sol_obs = trim(
original_model, exclude,
"sa_trim.cti") #Remove exclusion list from original model
new_sol = new_sol_obs[1]
#simulated reduced solution
new_sim = helper.setup_simulations(
conditions_array, new_sol
) #Create simulation objects for reduced model for all conditions
id_new = helper.simulate(new_sim) #Run simulations and process results
error = (abs(id_new - id_detailed) / id_detailed) * 100
error = round(np.max(error), 2)
print("Error of: " + str(error))
print()
if error > args.error: #If error is greater than allowed, previous reduced model was final reduction.
print("Final Solution:")
print(str(old.n_species) + " Species")
return old
else: #If error is still within allowed limit, loop through again to further reduce.
old = new_sol
def run_drg(solution_object, conditions_file, error_limit, target_species,
retained_species, model_file, final_error):
"""
Main function for running DRG reduction.
Parameters
----------
solution_object : ~cantera.Solution
A Cantera object of the solution to be reduced.
conditions_file : str
Name of file with list of autoignition initial conditions.
error_limit : float
Maximum allowable error level for reduced model.
target_species : list of str
List of target species
retained_species : list of str
List of species to always be retained
model_file : string
The path to the file where the solution object was generated from
final_error: singleton float
To hold the error level of simulation
Returns
-------
Writes reduced Cantera file and returns reduced Cantera solution object
"""
assert target_species, 'Need to specify at least one target species.'
# Singleton to hold whether any more species can be cut from the simulation.
done = []
done.append(False)
threshold = 0.1 # Starting threshold value
threshold_increment = 0.1
num_iterations = 1
error = [10.0]
conditions_array = readin_conditions(conditions_file)
# Turn conditions array into unrun simulation objects for the original solution
sim_array = helper.setup_simulations(conditions_array, solution_object)
# Run simulations and process results
ignition_delay_detailed = helper.simulate(sim_array)
# Get edge weight calculation data.
rate_edge_data = get_rates_drg(sim_array, solution_object)
print("Testing for starting threshold value")
# Trim the solution at that threshold and find the error.
drg_loop_control(solution_object, target_species, retained_species,
model_file, error, threshold, done, rate_edge_data,
ignition_delay_detailed, conditions_array)
# While the error for trimming with that threshold value is greater than allowed.
while error[0] != 0:
# Reduce the starting threshold value and try again.
threshold /= 10
threshold_increment /= 10
num_iterations += 1
drg_loop_control(solution_object, target_species, retained_species,
model_file, error, threshold, done, rate_edge_data,
ignition_delay_detailed, conditions_array)
if error[0] <= 0.02:
error[0] = 0
print("Starting with a threshold value of " + str(threshold))
sol_new = solution_object
final_error[0] = 0
done[0] = False
# Run the simulation until nothing else can be cut.
while not done[0] and error[0] < error_limit:
# Trim at this threshold value and calculate error.
sol_new = drg_loop_control(solution_object, target_species,
retained_species, model_file, error,
threshold, done, rate_edge_data,
ignition_delay_detailed, conditions_array)
# If a new max species cut without exceeding what is allowed is reached, save that threshold.
if error_limit > error[0]:
max_t = threshold
final_error[0] = error[0]
# if (final_error[0] == error[0]): #If error wasn't increased, increase the threshold at a higher rate.
# threshold = threshold + (threshold_increment * 4)
# if (threshold >= .01):
# threshold_increment = .01
threshold += threshold_increment
threshold = round(threshold, num_iterations)
print("Greatest result: ")
sol_new = drg_loop_control(solution_object, target_species,
retained_species, model_file, error, max_t,
done, rate_edge_data, ignition_delay_detailed,
conditions_array)
return sol_new
def autoignition_loop_control(solution_object, args):
"""Controls autoignition module
Parameters
----------
solution_object : class
Cantera solution object
args : class
Arguments from terminal
Returns
-------
sim_result : obj
Object containing autoignition results
.time
.temp
.initial_temperature_array
.sp_data
.test (h5py object)
.tau
.Temp
.frac
"""
class condition(object):
def __init__(self, name):
self.name = str(name)
self.pressure = float(raw_input('Initial Pressure (atm): '))
self.temperature = float(raw_input('Initial Temperature (K): '))
self.reactants = str(
raw_input(
'List of reactants and mole fractions (ex.Ch4:1,o2:11): '))
self.reactant_list = self.reactants.split(',')
self.species = {}
for reactant in self.reactant_list:
self.species[reactant.split(':')[0]] = reactant.split(':')[1]
conditions_array = []
#get initial conditions from readin, or prompt user if none given
if args.conditions_file:
conditions_array = readin_conditions(str(args.conditions_file))
elif not args.conditions_file:
print 'No initial conditions file found. Please enter below'
number_conditions = int(
float(raw_input('Enter # of initial conditions: ')))
if number_conditions > 1.0:
for i in range(number_conditions):
conditions_array.append(condition(i))
else:
conditions_array.append(condition(1.0))
args.multiple_conditions = False
tau_array = []
initial_temperature_array = []
#send initial conditions to autoignition script
if args.multiple_conditions is True:
for condition in conditions_array:
sim_result = run_sim(solution_object, condition, args)
initial_temperature_array.append(condition.temperature)
try:
tau_array.append(sim_result.tau)
except AttributeError:
tau_array.append(0.0)
else:
sim_result = run_sim(solution_object, conditions_array[0], args)
initial_temperature_array.append(conditions_array[0].temperature)
try:
tau_array.append(sim_result.tau)
except AttributeError:
tau_array.append(0.0)
sim_result.tau_array = tau_array
sim_result.initial_temperature_array = initial_temperature_array
return sim_result
def run_pfa(solution_object, conditions_file, error_limit, target_species, retained_species, model_file, final_error):
""""
This is the MAIN top level function for running PFA
Parameters
----------
solution_object: a Cantera object of the solution to be reduced
conditions_file: The file holding the initial conditions to simulate
error_limit: The highest allowed error percentage
target_species: The target species for reduction
retained_species: A list of species to be retained even if they should be cut by the algorithm
model_file: The path to the file where the solution object was generated from
final_error: To hold the error level of the simulation
Returns
-------
Writes reduced Cantera file and returns reduced Catnera solution object
"""
if len(target_species) == 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 conditions_file:
conditions_array = readin_conditions(str(conditions_file))
elif not conditions_file:
print("Conditions file not found")
exit()
# Turn conditions array into unran simulation objects for the original solution
sim_array = helper.setup_simulations(conditions_array,solution_object)
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")
# Trim the solution at that threshold and find the error.
pfa_loop_control(
solution_object, target_species, retained_species, model_file, error, threshold, done, rate_edge_data, ignition_delay_detailed, conditions_array)
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, target_species, retained_species, model_file, 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
final_error[0] = 0 # An integer representing the error introduced in the final simulation.
done[0] = False
while not done[0] and error[0] < error_limit: # Run the simulation until nothing else can be cut.
# Trim at this threshold value and calculate error.
sol_new = pfa_loop_control(
solution_object, target_species, retained_species, model_file, error, threshold, done, rate_edge_data, ignition_delay_detailed, conditions_array)
if error_limit >= error[0]: # If a new max species cut without exceeding what is allowed is reached, save that threshold.
max_t = threshold
#if (final_error == error[0]): #If error wasn't increased, increase the threshold at a higher rate.
# threshold = threshold + (threshold_i * 4)
final_error[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, target_species, retained_species, model_file, error, max_t, done, rate_edge_data, ignition_delay_detailed, conditions_array)
return sol_new
def autoignition_loop_control(solution_object, args, plot=False):
"""Controls autoignition module
Parameters
----------
solution_object : class
Cantera solution object
args : class
Arguments from terminal
plot : boolean
A boolean value that represents if this run will use additional features such as making plots or csv files.
Returns
-------
sim_result : obj
Object containing autoignition results
.time
.temp
.initial_temperature_array
.sp_data
.test (h5py object)
.tau
.Temp
.frac
"""
class condition(object):
def __init__(self, name):
self.name = str(name)
self.pressure = float(raw_input('Initial Pressure (atm): '))
self.temperature = float(raw_input('Initial Temperature (K): '))
self.reactants = str(
raw_input(
'List of reactants and mole fractions (ex.Ch4:1,o2:11): '))
self.reactant_list = self.reactants.split(',')
self.species = {}
for reactant in self.reactant_list:
self.species[reactant.split(':')[0]] = reactant.split(':')[1]
conditions_array = []
#get initial conditions from readin, or prompt user if none given
if args.conditions_file:
conditions_array = readin_conditions(str(args.conditions_file))
elif not args.conditions_file:
print 'No initial conditions file found. Please enter below'
number_conditions = int(
float(raw_input('Enter # of initial conditions: ')))
if number_conditions > 1.0:
for i in range(number_conditions):
conditions_array.append(condition(i))
else:
conditions_array.append(condition(1.0))
args.multiple_conditions = False
tau_array = []
initial_temperature_array = []
#send initial conditions to autoignition script
i = 1 #iterate which condition number it is for file naming.
for condition in conditions_array:
sim_result = run_sim(i, solution_object, condition, args, plot)
if (sim_result == 0):
return 0
initial_temperature_array.append(condition.temperature)
i = i + 1
try:
tau_array.append(sim_result.tau)
except AttributeError:
tau_array.append(0.0)
#Store the information from the simulation in an object and return it.
sim_result.tau_array = tau_array
sim_result.initial_temperature_array = initial_temperature_array
return sim_result
def autoignition_loop_control(solution_object, args):
"""Controls autoignition module
Parameters
----------
solution_object : class
Cantera solution object
args : class
Arguments from terminal
Returns
-------
sim_result : obj
Object containing autoignition results
.time
.temp
.initial_temperature_array
.sp_data
.test (h5py object)
.tau
.Temp
.frac
"""
class condition(object):
def __init__(self, name):
self.name = str(name)
self.pressure = float(raw_input('Initial Pressure (atm): '))
self.temperature = float(raw_input('Initial Temperature (K): '))
self.reactants = str(raw_input('List of reactants and mole fractions (ex.Ch4:1,o2:11): '))
self.reactant_list = self.reactants.split(',')
self.species={}
for reactant in self.reactant_list:
self.species[reactant.split(':')[0]] = reactant.split(':')[1]
conditions_array = []
#get initial conditions from readin, or prompt user if none given
if args.conditions_file:
conditions_array = readin_conditions(str(args.conditions_file))
elif not args.conditions_file:
print 'No initial conditions file found. Please enter below'
number_conditions = int(float(raw_input('Enter # of initial conditions: ')))
if number_conditions > 1.0:
for i in range(number_conditions):
conditions_array.append(condition(i))
else:
conditions_array.append(condition(1.0))
args.multiple_conditions = False
tau_array = []
initial_temperature_array = []
#send initial conditions to autoignition script
if args.multiple_conditions is True:
for condition in conditions_array:
sim_result = run_sim(solution_object, condition, args)
initial_temperature_array.append(condition.temperature)
try:
tau_array.append(sim_result.tau)
except AttributeError:
tau_array.append(0.0)
else:
sim_result = run_sim(solution_object, conditions_array[0], args)
initial_temperature_array.append(conditions_array[0].temperature)
try:
tau_array.append(sim_result.tau)
except AttributeError:
tau_array.append(0.0)
sim_result.tau_array = tau_array
sim_result.initial_temperature_array = initial_temperature_array
return sim_result
def run_sa(original_model, reduced_model, ep_star, final_error,
conditions_file, target, keepers, error_limit):
"""
Runs a sensitivity analysis on a resulting reduced model.
Parameters
----------
original_model: The original version of the model being reduced
reduced_model: The model produced by the previous reduction
ep_star: The epsilon star value for the sensitivity analysis
final_error: Error percentage between the reduced and origanal models
conditions_file: The file holding the initial conditions for simulations
target: The target species for the reduction
keepers: A list of species that should be retained no matter what
error_limit: The maximum allowed error between the reduced and original models
Returns
-------
The model after the sensitivity analysis has been preformed on it.
"""
if conditions_file:
conditions_array = readin_conditions(str(conditions_file))
elif not conditions_file:
print("Conditions file not found")
exit()
# Turn conditions array into unran simulation objects for the original solution
sim_array = helper.setup_simulations(conditions_array, original_model)
id_detailed = helper.simulate(
sim_array) # Run simulations and process results
rate_edge_data = get_rates(
sim_array, original_model) # Get edge weight calculation data.
# Make a dictionary of overall interaction coefficients.
drgep_coeffs = make_dic_drgep(original_model, rate_edge_data, target)
if (id_detailed.all() == 0): # Ensure that ignition occured
print("Original model did not ignite. Check initial conditions.")
exit()
old = reduced_model
while True:
og_sn = [] # Original species names
new_sn = [] # Species names in current reduced model
keep = [] # Species retained from removals
og_excl = [
] # Species that will be excluded from the final model (Reduction will be preformed on original model)
species_objex = old.species()
for sp in species_objex:
new_sn.append(sp.name)
species_objex = original_model.species()
for sp in species_objex:
og_sn.append(sp.name)
for sp in og_sn:
if sp in new_sn:
keep.append(sp)
for sp in original_model.species():
if not (sp.name in keep):
og_excl.append(sp.name)
# Find all the species in limbo.
limbo = create_limbo(old, ep_star, drgep_coeffs, keepers)
if len(limbo) == 0:
return old
print("In limbo:")
print(limbo)
# Calculate error for removing each limbo species.
dic = get_limbo_dic(original_model, old, limbo, final_error,
id_detailed, conditions_array)
rm = dic_lowest(dic) # Species that should be removed (Lowest error).
exclude = [rm]
for sp in og_excl: # Add to list of species that should be excluded from final model.
exclude.append(sp)
print()
print("attempting to remove " + rm)
# Remove exclusion list from original model
new_sol_obs = trim(original_model, exclude, "sa_trim.cti")
new_sol = new_sol_obs[1]
# Simulated reduced solution
new_sim = helper.setup_simulations(
conditions_array, new_sol
) # Create simulation objects for reduced model for all conditions
id_new = helper.simulate(
new_sim) # Run simulations and process results
error = (abs(id_new - id_detailed) / id_detailed) * 100
error = round(np.max(error), 2)
print("Error of: " + str(error))
print()
# If error is greater than allowed, previous reduced model was final reduction.
if error > error_limit:
print("Final Solution:")
print(str(old.n_species) + " Species")
return old
else: # If error is still within allowed limit, loop through again to further reduce.
old = new_sol