if __name__ == '__main__':
# build a test mechanism with inert oxygen
base_mech = 'gri30'
test_mech = 'test_mech'
file_types = ['.cti', '.xml']
funcs = [make_inert_cti, make_inert_xml]
inert_species = ['O2']
init_temp = 1000
init_press = ct.one_atm
for ftype, func in zip(file_types, funcs):
mechs = [base_mech + ftype, test_mech + ftype]
_, _, file_out = _get_file_locations(mechs[0], mechs[1])
func(mechs[0], inert_species, mechs[1])
gases = [ct.Solution(m) for m in mechs]
for idx, g in enumerate(gases):
g.set_equivalence_ratio(1, 'H2', 'O2')
init_mf = g.mole_fraction_dict()['O2']
g.TP = init_temp, init_press
r = ct.Reactor(g)
n = ct.ReactorNet([r])
n.advance_to_steady_state()
print(mechs[idx])
print('-' * len(mechs[idx]))
print('# reactions: {:1.0f}'.format(len(
g.reaction_equations())))
print('final temp: {:1.0f} K'.format(r.thermo.TP[0]))
print('final pressure: {:1.0f} atm'.format(r.thermo.TP[1] /
ct.one_atm))
print('Y_02 init/final: {:0.3f} / {:0.3f}'.format(
)
parser.add_argument(
"--yields",
nargs='+',
type=int,
required=False,
default=[-1],
dest='yields',
help=
'Indices of concentration yields to include in error. -1 for none. Default -1.'
)
args = parser.parse_args()
start = timeit.default_timer()
gas = ct.Solution(args.mechanism)
reactions = gas.reactions()
Npoints = args.Npoints
filebase = args.filebase
ns = gas.n_total_species
nr = gas.n_reactions
expfile = open(args.experiments, 'r')
measure_ind = args.measure
sensitivity = args.sensitivity
num_remove = args.remove
num_exclude = args.retain
maxes = args.maxes
yields = args.yields
seed = 1000 * 1000 * num_exclude + 1000 * num_remove + args.seed
ktol = args.ktol
outflag = args.out
def run_drgep(model_file,
importance_coeffs,
ignition_conditions,
psr_conditions,
flame_conditions,
error_limit,
species_targets,
species_safe,
phase_name='',
threshold_upper=None,
num_threads=1,
path=''):
"""Main function for running DRGEP reduction.
Parameters
----------
model_file : str
Original model file
ignition_conditions : list of InputIgnition
List of autoignition initial conditions.
psr_conditions : list of InputPSR
List of PSR simulation conditions.
flame_conditions : list of InputLaminarFlame
List of laminar flame simulation conditions.
error_limit : float
Maximum allowable error level for reduced model
species_targets : list of str
List of target species names
species_safe : list of str
List of species names to always be retained
phase_name : str, optional
Optional name for phase to load from CTI file (e.g., 'gas').
threshold_upper : float, optional
Upper threshold (epsilon^*) to identify limbo species for sensitivity analysis
num_threads : int, optional
Number of CPU threads to use for performing simulations in parallel.
Optional; default = 1, in which the multiprocessing module is not used.
If 0, then use the available number of cores minus one. Otherwise,
use the specified number of threads.
path : str, optional
Optional path for writing files
Returns
-------
ReducedModel
Return reduced model and associated metadata
"""
solution = ct.Solution(model_file, phase_name)
assert species_targets, 'Need to specify at least one target species.'
# first, sample thermochemical data and generate metrics for measuring error
# (e.g, ignition delays). Also produce adjacency matrices for graphs, which
# will be used to produce graphs for any threshold value.
sampled_metrics, sampled_data = drgep.sample(model_file,
ignition_conditions,
phase_name=phase_name,
num_threads=num_threads,
path=path)
matrices = []
for state in sampled_data:
matrices.append(
drgep.create_drgep_matrix((state[0], state[1], state[2:]),
solution))
# For DRGEP, find the overall interaction coefficients for all species
# using the maximum over all the sampled states
# importance_coeffs = drgep.get_importance_coeffs(
# solution.species_names, species_targets, matrices
# )
# begin reduction iterations
logging.info('Beginning reduction loop')
logging.info(45 * '-')
logging.info('Threshold | Number of species | Max error (%)')
# make lists to store data
threshold_data = []
num_species_data = []
error_data = []
# start with detailed (starting) model
previous_model = reduce_model.ReducedModel(model=solution,
filename=model_file,
error=0.0)
first = True
error_current = 0.0
threshold = 0.01
threshold_increment = 0.01
while error_current <= error_limit:
reduced_model = reduce_drgep(model_file,
species_safe,
threshold,
importance_coeffs,
ignition_conditions,
sampled_metrics,
phase_name=phase_name,
previous_model=previous_model,
num_threads=num_threads,
path=path)
error_current = reduced_model.error
num_species = reduced_model.model.n_species
# reduce threshold if past error limit on first iteration
if first and error_current > error_limit:
error_current = 0.0
threshold /= 10
threshold_increment /= 10
if threshold <= 1e-6:
raise SystemExit(
'Threshold value dropped below 1e-6 without producing viable reduced model'
)
logging.info('Threshold value too high, reducing by factor of 10')
continue
logging.info(
f'{threshold:^9.2e} | {num_species:^17} | {error_current:^.2f}')
# store data
threshold_data.append(threshold)
num_species_data.append(num_species)
error_data.append(error_current)
threshold += threshold_increment
first = False
# cleanup files
if previous_model.model.n_species != reduced_model.model.n_species:
os.remove(reduced_model.filename)
previous_model = reduce_model.ReducedModel(
model=reduced_model.model,
filename=reduced_model.filename,
error=reduced_model.error,
limbo_species=reduced_model.limbo_species)
if reduced_model.error > error_limit:
threshold -= (2 * threshold_increment)
reduced_model = reduce_drgep(model_file,
species_safe,
threshold,
importance_coeffs,
ignition_conditions,
sampled_metrics,
phase_name=phase_name,
num_threads=num_threads,
path=path)
else:
soln2cti.write(reduced_model,
f'reduced_{reduced_model.model.n_species}.cti',
path=path)
if threshold_upper:
for sp in reduced_model.model.species_names:
if importance_coeffs[sp] < threshold_upper and (
sp not in species_safe):
reduced_model.limbo_species.append(sp)
logging.info(45 * '-')
logging.info('Reduction complete.')
logging.info(
f'Skeletal model: {reduced_model.model.n_species} species and '
f'{reduced_model.model.n_reactions} reactions.')
logging.info(f'Maximum error: {reduced_model.error:.2f}%')
logging.info('Final reduced model saved as ' + reduced_model.filename)
return threshold_data, num_species_data, error_data
# set the gas to a state with the same entropy and composition but
# the perturbed pressure
gas.SP = s0, p1
# frozen sound speed
afrozen = math.sqrt((p1 - p0) / (gas.density - r0))
# now equilibrate the gas holding S and P constant
gas.equilibrate('SP', rtol=rtol, maxiter=maxiter)
# equilibrium sound speed
aequil = math.sqrt((p1 - p0) / (gas.density - r0))
# compute the frozen sound speed using the ideal gas expression as a check
gamma = gas.cp / gas.cv
afrozen2 = math.sqrt(gamma * ct.gas_constant * gas.T /
gas.mean_molecular_weight)
return aequil, afrozen, afrozen2
# test program
if __name__ == "__main__":
gas = ct.Solution('gri30.yaml')
gas.X = 'CH4:1.00, O2:2.0, N2:7.52'
for n in range(27):
T = 300.0 + 100.0 * n
gas.TP = T, ct.one_atm
print(T, equilSoundSpeeds(gas))
get_ipython().run_line_magic('matplotlib', 'inline')
import sys
import os
import math
import csv
import numpy as np
import cantera as ct
#combustion chamber volume and mass flows assesment based on the dimensions of Rolls Royce AE3007
lambda_air = 1.5 #air–fuel equivalence ratio
air = ct.Solution('air.cti')
air.TP = 800, 1.4e+06 #typical temperature and pressure behind HPC
air_in=ct.Reservoir(air)
air_mdot=ct.Quantity(air, mass=20)
fuel = ct.Solution('Dagaut_Ori.cti')
fuel.TPY = 300, 3e+05, 'NC10H22:0.74,PHC3H7:0.15,CYC9H18:0.11'
fuel_in=ct.Reservoir(fuel)
fuel_mdot = ct.Quantity(fuel, mass=1)
fuel_mdot.mass=air_mdot.mass/lambda_air/14.9
#igniter (like in "combustor.py")
fuel.TPX = 1500, 2e+06, 'H:1.0'
igniter = ct.Reservoir(fuel)
fuel.TPX = 1100, 1.2e+6, 'N2:1.0' #combustion chamber already hot, otherwise some mechanims doesn't integrate properly when combustor temperature is below 1000 K
# composition of the inlet premixed gas for the methane/air case
comp2 = 'CH4:0.095, O2:0.21, N2:0.78, AR:0.01'
# The inlet/surface separation is 10 cm.
width = 0.1 # m
loglevel = 1 # amount of diagnostic output (0 to 5)
################ create the gas object ########################
#
# This object will be used to evaluate all thermodynamic, kinetic, and
# transport properties. The gas phase will be taken from the definition of
# phase 'gas' in input file 'ptcombust.cti,' which is a stripped-down version
# of GRI-Mech 3.0.
gas = ct.Solution('ptcombust.cti', 'gas')
gas.TPX = tinlet, p, comp1
################ create the interface object ##################
#
# This object will be used to evaluate all surface chemical production rates.
# It will be created from the interface definition 'Pt_surf' in input file
# 'ptcombust.cti,' which implements the reaction mechanism of Deutschmann et
# al., 1995 for catalytic combustion on platinum.
#
surf_phase = ct.Interface('ptcombust.cti', 'Pt_surf', [gas])
surf_phase.TP = tsurf, p
# integrate the coverage equations in time for 1 s, holding the gas
# composition fixed to generate a good starting estimate for the coverages.
surf_phase.advance_coverages(1.0)
def f4(ER_,T_0=300,P_=1,alpha_= 0,beta_ = 0):
gas_new = ct.Solution('gri30.cti')
gas_new.TPX = T_0,P_*ct.one_atm,'H2:%f, O2:1, N2:3.76, H2O:%f, CO:%f'%(2.*ER_,alpha_,beta_)
gas_new.equilibrate('HP')
return float(gas_new.T)
start = time.time() #### Input mechanism data ####: input_file0 = 'ethanol_pt.cti' input_file = 'ethanol_pt_redux.cti' surf_name = 'Pt_surface' #### Data files path ####: basepath = os.path.dirname(__file__) filepath = os.path.join(basepath, '../..', 'data') #### Coverage dependency matrix file ####: cov_file = os.path.join(filepath, 'cov_matrix/covmatrix_et_pt.inp') #Load phases solution objects gas0 = ct.Solution(input_file0) surfCT0 = ct.Interface(input_file0, surf_name, [gas0]) #Load in-house surface phase object surf0 = pfr.SurfacePhase(gas0, surfCT0, cov_file=cov_file) #Load phases solution objects for reduced mechanism gas = ct.Solution(input_file) surfCT = ct.Interface(input_file, surf_name, [gas]) #Load in-house surface phase object surf = pfr.SurfacePhase(gas, surfCT, cov_file=cov_file) #### Current thermo state ####: X_in = 'CH3CH2OH:0.125, H2O:0.375, HE:0.50' T_in = 668
return soln, fullArrayFlowData, PdropTotal / 1e6
#%%
if __name__ == '__main__':
mdot = 0.3 #mass flow rate [kg/s]
T0 = 300 # #ambient temperature [K]/stagnation
stag = T0
P1 = psi_to_MPa(
1500
) * 1e6 #(100/145.038)*1e6#MPa #pressure at pipe inlet [Pa] 1500 PSI
PInit = P1
#define gas
gas = ct.Solution('air.cti')
gas.transport_model = 'Mix'
gas.TPX = T0, P1, {'O2': 1, 'N2': 3.76}
R = 8.314
gamma = gas.cp / gas.cv #ratio of specific heats [-]
MW = gas.mean_molecular_weight
step = 0.001 #(m) break tube up into 1mm increments
#for loss calculations https://docs.google.com/spreadsheets/d/1gn8gLvc1uLY0blPmABOoTxjrHLD8UaT0t1OvKjpqaj4/edit#gid=0
#Minor/Major Loss calculations:
#https://docs.google.com/spreadsheets/d/1gn8gLvc1uLY0blPmABOoTxjrHLD8UaT0t1OvKjpqaj4/edit#gid=89965144
Kvec = [0.75, 0.00001006424457, 0.2, 1.816115216, 7.5, 0.5, 3.30000078]
Lvec = [1e-16, 1e-16, 1.2192, 0.5461, 1.5367, 1.2192, 0.0635]
Dvec = [
0.004572, 0.009398, 0.008636, 0.009398, 0.009398, 0.008636, 0.009398
]
epsvec = [
# -*- coding: utf-8 -*-
"""
Created on Fri Feb 02 13:01:11 2018
@author: Mark Barbet
"""
import os
import cantera as ct
import numpy as np
import matplotlib.pyplot as plt
import troe_rate as tr
gas = ct.Solution(os.getcwd() + '\\TMRP codes\\FFCM-1\\h_c2h2')
kp = []
kcalc = []
pressures = np.arange(0.0001 * ct.one_atm, 100000 * ct.one_atm, 50000)
pressures = np.arange(0.00001, 100000 * ct.one_atm, 50000)
high_rate = gas.reactions()[0].high_rate.pre_exponential_factor * (
1000**gas.reactions()[0].high_rate.temperature_exponent) * np.exp(
-gas.reactions()[0].high_rate.activation_energy / 1000.0 /
ct.gas_constant)
low_rate = gas.reactions()[0].low_rate.pre_exponential_factor * (
1000**gas.reactions()[0].low_rate.temperature_exponent) * np.exp(
gas.reactions()[0].low_rate.activation_energy / 1000.0 /
ct.gas_constant)
for i in np.arange(len(pressures)):
gas.TP = 1000.0, pressures[i]
kp.append(gas.forward_rate_constants[0])
kcalc.append(
tr.rate(low_rate, high_rate, 0.215, 10.7, 1043.0, 2341.0, 1000.0,
Windows 8.1, Windows 10, Linux (Debian 9) """ import cantera as ct from sdtoolbox.thermo import soundspeed_fr, soundspeed_eq from sdtoolbox.postshock import CJspeed, PostShock_eq ## set initial state, composition, and gas object P1 = 100000. T1 = 300. #q = 'H2:2.00 O2:1.0 N2:3.76' #q = 'C2H4:1.00 O2:3 N2:11.28' q = 'C2H2:1 O2:2.5 AR:3' #q = 'C2H4:1. O2:3' #q = 'O2:1. N2:3.76' mech = 'gri30_highT.cti' gas1 = ct.Solution(mech) gas1.TPX = T1, P1, q ## Evaluate initial state R1 = gas1.density c1_fr = soundspeed_fr(gas1) cp1 = gas1.cp_mass w1 = gas1.mean_molecular_weight gamma1_fr = c1_fr**2 * R1 / P1 ## Set shock speed cj_speed = CJspeed(P1, T1, q, mech) Us = cj_speed ## Evaluate gas state # q = 'O2:1. N2:3.76'
@author: dkorff
This is the object initialization file for the Li-s model. It imports input
values from li_s_battery_inputs.py and initializes the necessary objects to
run the simulations
"""
import numpy as np
import cantera as ct
import importlib
from math import pi
from li_s_battery_inputs import inputs
"Import cantera objects - this step is the same regardless of test type"
elyte_obj = ct.Solution(inputs.ctifile, inputs.elyte_phase)
sulfur_obj = ct.Solution(inputs.ctifile, inputs.cat_phase1)
Li2S_obj = ct.Solution(inputs.ctifile, inputs.cat_phase2)
carbon_obj = ct.Solution(inputs.ctifile, inputs.cat_phase3)
conductor_obj = ct.Solution(inputs.ctifile, inputs.metal_phase)
lithium_obj = ct.Solution(inputs.ctifile, inputs.an_phase)
sulfur_el_s = ct.Interface(inputs.ctifile, inputs.sulfur_elyte_phase,
[sulfur_obj, elyte_obj, conductor_obj])
Li2S_el_s = ct.Interface(inputs.ctifile, inputs.Li2S_elyte_phase,
[Li2S_obj, elyte_obj, conductor_obj])
carbon_el_s = ct.Interface(inputs.ctifile, inputs.graphite_elyte_phase,
[carbon_obj, elyte_obj, conductor_obj])
lithium_el_s = ct.Interface(inputs.ctifile, inputs.anode_elyte_phase,
[lithium_obj, elyte_obj, conductor_obj])
Li2S_tpb = ct.Interface(inputs.ctifile, 'tpb', [elyte_obj, Li2S_obj, conductor_obj])
class FlameExtinguished(Exception):
pass
# Create directory for output data files
data_directory = 'diffusion_flame_batch_data/'
if not os.path.exists(data_directory):
os.makedirs(data_directory)
# PART 1: INITIALIZATION
# Set up an initial hydrogen-oxygen counterflow flame at 1 bar and low strain
# rate (maximum axial velocity gradient = 2414 1/s)
reaction_mechanism = 'h2o2.yaml'
gas = ct.Solution(reaction_mechanism)
width = 18e-3 # 18mm wide
f = ct.CounterflowDiffusionFlame(gas, width=width)
# Define the operating pressure and boundary conditions
f.P = 1.e5 # 1 bar
f.fuel_inlet.mdot = 0.5 # kg/m^2/s
f.fuel_inlet.X = 'H2:1'
f.fuel_inlet.T = 300 # K
f.oxidizer_inlet.mdot = 3.0 # kg/m^2/s
f.oxidizer_inlet.X = 'O2:1'
f.oxidizer_inlet.T = 300 # K
# Set refinement parameters, if used
f.set_refine_criteria(ratio=3.0, slope=0.1, curve=0.2, prune=0.03)
def cj_speed(
cls,
initial_pressure,
initial_temperature,
species_mole_fractions,
mechanism,
use_multiprocessing=False,
return_r_squared=False,
return_state=False
):
"""
This function calculates CJ detonation velocity
Original function: CJspeed in PostShock.py
Parameters
----------
initial_pressure : float
initial pressure (Pa)
initial_temperature : float
initial temperature (K)
species_mole_fractions : str or dict
string or dictionary of reactant species mole fractions
mechanism : str
cti file containing mechanism data (e.g. 'gri30.cti')
use_multiprocessing : bool
use multiprocessing to speed up CJ speed calculation
return_r_squared : bool
return the R^2 value of the CJ speed vs. density ratio fit
return_state : bool
return the CJ state corresponding to the calculated velocity
Returns
-------
dict
"""
# DECLARATIONS
num_steps = 20
max_density_ratio = 2.0
min_density_ratio = 1.5
a = 0
b = 0
c = 0
if use_multiprocessing:
pool = mp.Pool()
# Set error tolerances for CJ state calculation
error_tol_temperature = 1e-4
error_tol_velocity = 1e-4
counter = 1
r_squared = 0.0
delta_r_squared = 0.0
adjusted_density_ratio = 0.0
while (counter <= 4) and (
(r_squared < 0.99999) or (delta_r_squared < 1e-7)
):
density_ratio_array = np.linspace(
min_density_ratio,
max_density_ratio,
num_steps + 1
)
if use_multiprocessing:
# parallel loop through density ratios
stargs = [[number,
ratio,
initial_temperature,
initial_pressure,
species_mole_fractions,
mechanism,
error_tol_temperature,
error_tol_velocity
]
for number, ratio in
zip(
range(len(density_ratio_array)),
density_ratio_array
)
]
# noinspection PyUnboundLocalVariable
result = pool.starmap(cls._calculate_over_ratio_range, stargs)
else:
# no multiprocessing, just use map
result = list(map(
cls._calculate_over_ratio_range,
[item for item in range(len(density_ratio_array))],
density_ratio_array,
[initial_temperature for _ in density_ratio_array],
[initial_pressure for _ in density_ratio_array],
[species_mole_fractions for _ in density_ratio_array],
[mechanism for _ in density_ratio_array],
[error_tol_temperature for _ in density_ratio_array],
[error_tol_velocity for _ in density_ratio_array]
))
result.sort()
cj_velocity_calculations = np.array(
[item for (_, item) in result]
)
# Get curve fit
a, b, c, r_squared = cj_curve_fit(
density_ratio_array,
cj_velocity_calculations
)
adjusted_density_ratio = -b / (2. * a)
min_density_ratio = adjusted_density_ratio * (1 - 0.001)
max_density_ratio = adjusted_density_ratio * (1 + 0.001)
counter += 1
cj_speed = a * adjusted_density_ratio**2 + \
b * adjusted_density_ratio + c
if return_state:
initial_state_gas = ct.Solution(mechanism)
working_gas = ct.Solution(mechanism)
initial_state_gas.TPX = [
initial_temperature,
initial_pressure,
species_mole_fractions
]
working_gas.TPX = [
initial_temperature,
initial_pressure,
species_mole_fractions
]
cls.cj_state(
working_gas,
initial_state_gas,
error_tol_temperature,
error_tol_velocity,
adjusted_density_ratio
)
if return_r_squared and return_state:
# noinspection PyUnboundLocalVariable
return {'cj speed': cj_speed,
'R^2': r_squared,
'cj state': working_gas
}
elif return_state:
# noinspection PyUnboundLocalVariable
return {'cj speed': cj_speed,
'cj state': working_gas
}
elif return_r_squared:
return {'cj speed': cj_speed,
'R^2': r_squared
}
else:
return {'cj speed': cj_speed}
def setUpClass(cls):
cls.gas = ct.Solution("jacobian-tests.yaml", transport_model=None)
# species: [H2, H, O, O2, OH, H2O, HO2, H2O2, AR]
cls.gas.TPX = 300, 2 * ct.one_atm, "H2:1, O2:3, AR:0.4"
cls.gas.equilibrate("HP")
super().setUpClass()
#githubtest
#print(sys.version)
import inspect
import os
print(inspect.getfile(ct))
#圧力kPa,温度K
P1 = 101.325
T1 = 288.15
phi = 0.8
P2 = P1 * 1
E2 = 0.75
E4 = 0.82
P4 = P1
inigas = ct.Solution('gri30.xml')
#初期条件を設定(Air)
gas1 = ct.Quantity(inigas)
gas1.TPX = T1, P1 * 1000, {'O2': 1, 'N2': 3.76}
H1 = gas1.h
#print(gas1.report())
#初期条件を設定(H2)
gas2 = ct.Quantity(inigas)
gas2.TPX = T1, P2 * 1000, {'H2': 2 * phi}
gas1.moles = 1
nO2 = gas1.X[gas1.species_index('O2')]
gas2.moles = nO2 * 2 * phi
def create_solution_mechanism():
# Defining Reaction Mechanism
# Mechanism II - Marinov + Mevel
marinov_species = ct.Species.listFromFile('marinov_ethanol_mechanism.cti')
marinov_reactions = ct.Reaction.listFromFile(
'marinov_ethanol_mechanism.cti')
mevel_species = ct.Species.listFromFile('mevel_ethanol_mechanism.cti')
mevel_reactions = ct.Reaction.listFromFile('mevel_ethanol_mechanism.cti')
new_species = []
new_reactions = []
# Filter species
for specie in mevel_species:
# Include all nitrogen compounds except for N2
if 'N' in specie.composition and specie.composition != {'N': 2}:
new_species.append(specie)
new_species_names = {specie.name for specie in new_species}
# print('N based species: {0}'.format(', '.join(name for name in new_species_names)))
marinov_mevel_species = marinov_species + new_species
marinov_mevel_species_names = {
specie.name.upper()
for specie in marinov_mevel_species
}
# Filter reactions, keeping only those that only involve the selected species
# print('\nReactions:')
for R in mevel_reactions:
if any(reactant in new_species_names
for reactant in R.reactants) or any(product in new_species_names
for product in R.products):
# for reactant in R.reactants:
# if reactant not in marinov_mevel_species_names:
# print('Missing reactant:', reactant, 'when analyzing reaction', R.equation)
# for product in R.products:
# if product not in marinov_mevel_species_names:
# print('Missing product:', product, 'when analyzing reaction', R.equation)
if all(reactant in marinov_mevel_species_names
for reactant in R.reactants):
if all(product in marinov_mevel_species_names
for product in R.products):
new_reactions.append(R)
# print('Accepted reaction:', R.equation)
# print('\n')
marinov_mevel_species = marinov_species + new_species
marinov_mevel_reactions = marinov_reactions + new_reactions
marinov_mevel_gas = ct.Solution(thermo='IdealGas',
kinetics='GasKinetics',
species=marinov_mevel_species,
reactions=marinov_mevel_reactions)
marinov_mevel_gas = ct.Solution('sandiego2016_plus_N_CK.cti')
print('Number of species:', marinov_mevel_gas.n_species)
print('Number of reactions:', marinov_mevel_gas.n_reactions)
return marinov_mevel_gas
_T4 = []
_X1 = []
_X2 = []
_X3 = []
_X4 = []
_h12 = []
_h23 = []
_h34 = []
_h41 = []
_s12 = []
_s23 = []
_s34 = []
_s41 = []
"Define Fluids"
air_1 = ct.Solution('air.cti')
air_2 = ct.Solution('air.cti')
WF_1 = ct.Hfc134a()
WF_2 = ct.Hfc134a()
WF_3 = ct.Hfc134a()
WF_4 = ct.Hfc134a()
"Knowns"
T1_air = 38+273.15 # T1 ambient = 38C
P1_air = 1*10**5 # P1 ambient = 1 bar
T2_air = 10+273.15 # T2 into cabin = 10C
P2_air = P1_air # P2 = 1 bar
air_1.TP = T1_air,P1_air # Define state
air_2.TP = T2_air,P2_air # Define state
h1_air = air_1.h
h2_air = air_2.h
Requires: cantera >= 2.5.0
"""
import cantera as ct
# Simulation parameters
p = ct.one_atm # pressure [Pa]
Tin = 300.0 # unburned gas temperature [K]
reactants = 'H2:1.1, O2:1, AR:5' # premixed gas composition
width = 0.03 # m
loglevel = 1 # amount of diagnostic output (0 to 8)
# Solution object used to compute mixture properties, set to the state of the
# upstream fuel-air mixture
gas = ct.Solution('h2o2.yaml')
gas.TPX = Tin, p, reactants
# Set up flame object
f = ct.FreeFlame(gas, width=width)
f.set_refine_criteria(ratio=3, slope=0.06, curve=0.12)
f.show_solution()
# Solve with mixture-averaged transport model
f.transport_model = 'Mix'
f.solve(loglevel=loglevel, auto=True)
# Solve with the energy equation enabled
try:
# save to HDF container file if h5py is installed
f.write_hdf('adiabatic_flame.h5', group='mix', mode='w',
Since reactors can have multiple inlets and outlets, they can be used to
implement mixers, splitters, etc. In this example, air and methane are mixed
in stoichiometric proportions. Due to the low temperature, no reactions occur.
Note that the air stream and the methane stream use *different* reaction
mechanisms, with different numbers of species and reactions. When gas flows
from one reactor or reservoir to another one with a different reaction
mechanism, species are matched by name. If the upstream reactor contains a
species that is not present in the downstream reaction mechanism, it will be
ignored. In general, reaction mechanisms for downstream reactors should
contain all species that might be present in any upstream reactor.
"""
import cantera as ct
# Use air for stream a.
gas_a = ct.Solution('air.xml')
gas_a.TPX = 300.0, ct.one_atm, 'O2:0.21, N2:0.78, AR:0.01'
rho_a = gas_a.density
# Use GRI-Mech 3.0 for stream b (methane) and for the mixer. If it is desired
# to have a pure mixer, with no chemistry, use instead a reaction mechanism
# for gas_b that has no reactions.
gas_b = ct.Solution('gri30.xml')
gas_b.TPX = 300.0, ct.one_atm, 'CH4:1'
rho_b = gas_b.density
# Create reservoirs for the two inlet streams and for the outlet stream. The
# upsteam reservoirs could be replaced by reactors, which might themselves be
# connected to reactors further upstream. The outlet reservoir could be
# replaced with a reactor with no outlet, if it is desired to integrate the
# composition leaving the mixer in time, or by an arbitrary network of
def write(solution):
"""Function to write cantera solution object to inp file.
Parameters
----------
solution : obj
Cantera solution object
Returns : str
Name of trimmed Mechanism file (.inp)
Example
-------
soln2ck.write(gas)
"""
trimmed_solution = solution
input_file_name_stripped = trimmed_solution.name
cwd = os.getcwd()
output_file_name = os.path.join(cwd,
'pym_' + input_file_name_stripped + '.inp')
f = open(output_file_name, 'w+')
#Work functions
calories_constant = 4184.0 #number of calories in 1000 Joules of energy
def eliminate(input_string, char_to_replace, spaces='single'):
"""
Eliminate characters from a string
:param input_string
string to be modified
:param char_to_replace
array of character strings to be removed
"""
for char in char_to_replace:
input_string = input_string.replace(char, "")
if spaces == 'double':
input_string = input_string.replace(" ", " ")
return input_string
def section_break(title):
"""
Insert break and new section title into cti file
:param title:
title string for next section_break
"""
f.write('!' + "-" * 75 + '\n')
f.write('! ' + title + '\n')
f.write('!' + "-" * 75 + '\n')
def replace_multiple(input_string, replace_list):
"""
Replace multiple characters in a string
:param input_string
string to be modified
:param replace list
list containing items to be replaced (value replaces key)
"""
for original_character, new_character in replace_list.items():
input_string = input_string.replace(original_character,
new_character)
return input_string
def build_arrhenius(equation_object, equation_type):
"""
Builds Arrhenius coefficient string
:param equation_objects
cantera equation object
:param equation_type:
string of equation type
"""
coeff_sum = sum(equation_object.reactants.values())
pre_exponential_factor = equation_object.rate.pre_exponential_factor
temperature_exponent = '{:.3f}'.format(
equation_object.rate.temperature_exponent)
activation_energy = '{:.2f}'.format(
equation_object.rate.activation_energy / calories_constant)
if equation_type == 'ElementaryReaction':
if coeff_sum == 1:
pre_exponential_factor = str(
'{:.3E}'.format(pre_exponential_factor))
if coeff_sum == 2:
pre_exponential_factor = str('{:.3E}'.format(
pre_exponential_factor * 10**3))
if coeff_sum == 3:
pre_exponential_factor = str('{:.3E}'.format(
pre_exponential_factor * 10**6))
if equation_type == 'ThreeBodyReaction':
if coeff_sum == 1:
pre_exponential_factor = str('{:.3E}'.format(
pre_exponential_factor * 10**3))
if coeff_sum == 2:
pre_exponential_factor = str('{:.3E}'.format(
pre_exponential_factor * 10**6))
if (equation_type != 'ElementaryReaction'
and equation_type != 'ThreeBodyReaction'):
pre_exponential_factor = str(
'{:.3E}'.format(pre_exponential_factor))
arrhenius = [
pre_exponential_factor, temperature_exponent, activation_energy
]
return arrhenius
def build_modified_arrhenius(equation_object, t_range):
"""
Builds Arrhenius coefficient strings for high and low temperature ranges
:param equation_objects
cantera equation object
:param t_range:
simple string ('high' or 'low') to designate temperature range
"""
coeff_sum = sum(equation_object.products.values())
if t_range == 'high':
pre_exponential_factor = equation_object.high_rate.pre_exponential_factor
temperature_exponent = '{:.3f}'.format(
equation_object.high_rate.temperature_exponent)
activation_energy = '{:.2f}'.format(
equation_object.high_rate.activation_energy /
calories_constant)
if coeff_sum == 1:
pre_exponential_factor = str('{:.5E}'.format(
pre_exponential_factor * 10**3))
else:
pre_exponential_factor = str(
'{:.5E}'.format(pre_exponential_factor))
arrhenius_high = [
pre_exponential_factor, temperature_exponent, activation_energy
]
return arrhenius_high
if t_range == 'low':
pre_exponential_factor = equation_object.low_rate.pre_exponential_factor
temperature_exponent = '{:.3f}'.format(
equation_object.low_rate.temperature_exponent)
activation_energy = '{:.2f}'.format(
equation_object.low_rate.activation_energy / calories_constant)
if coeff_sum == 1:
pre_exponential_factor = str('{:.5E}'.format(
pre_exponential_factor * 10**6))
else:
pre_exponential_factor = str('{:.5E}'.format(
pre_exponential_factor * 10**3))
arrhenius_low = [
pre_exponential_factor, temperature_exponent, activation_energy
]
return arrhenius_low
def build_nasa(nasa_coeffs, row):
"""
Creates string of nasa polynomial coefficients
:param nasa_coeffs
cantera species thermo coefficients object
:param row
which row to write coefficients in
"""
line_coeffs = ''
lines = [[1, 2, 3, 4, 5], [6, 7, 8, 9, 10], [11, 12, 13, 14]]
line_index = lines[row - 2]
for ix, c in enumerate(nasa_coeffs):
if ix in line_index:
if c >= 0:
line_coeffs += ' '
line_coeffs += str('{:.8e}'.format(c))
return line_coeffs
def build_species_string():
"""
formats species string for writing
"""
species_list_string = ''
line = 1
for sp_index, sp_string in enumerate(trimmed_solution.species_names):
sp = ' '
#get length of string next species is added
length_new = len(sp_string)
length_string = len(species_list_string)
total = length_new + length_string + 3
#if string will go over width, wrap to new line
if total >= 70 * line:
species_list_string += '\n'
line += 1
species_list_string += sp_string + ((16 - len(sp_string)) * sp)
return species_list_string
#Write title block to file
section_break('Chemkin File converted from Solution Object by pyMARS')
#Write phase definition to file
element_names = eliminate(str(trimmed_solution.element_names),
['[', ']', '\'', ','])
element_string = Template('ELEMENTS\n' + '$element_names\n' + 'END\n')
f.write(element_string.substitute(element_names=element_names))
species_names = build_species_string()
species_string = Template('SPECIES\n' + '$species_names\n' + 'END\n')
f.write(species_string.substitute(species_names=species_names))
#Write species to file
section_break('Species data')
f.write('THERMO ALL' + '\n' + ' 300.000 1000.000 5000.000' + '\n')
phase_unknown_list = []
#write data for each species in the Solution object
for sp_index in xrange(len(trimmed_solution.species_names)):
d = 3.33564e-30 #1 debye = d coulomb-meters
species = trimmed_solution.species(sp_index)
name = str(trimmed_solution.species(sp_index).name)
nasa_coeffs = trimmed_solution.species(sp_index).thermo.coeffs
#Species attributes from trimmed solution object
t_low = '{0:.3f}'.format(species.thermo.min_temp)
t_max = '{0:.3f}'.format(species.thermo.max_temp)
t_mid = '{0:.3f}'.format(species.thermo.coeffs[0])
temp_range = str(t_low) + ' ' + str(t_max) + ' ' + t_mid
species_comp = ''
for atom in species.composition:
species_comp += '{:<4}'.format(atom)
species_comp += str(int(species.composition[atom]))
if type(species.transport).__name__ == 'GasTransportData':
species_phase = 'G'
else:
phase_unknown_list.append(name)
species_phase = 'G'
line_1 = ('{:<18}'.format(name) + '{:<6}'.format(' ') +
'{:<20}'.format(species_comp) +
'{:<4}'.format(species_phase) + '{:<31}'.format(temp_range) +
'{:<1}'.format('1') + '\n')
f.write(line_1)
line_2_coeffs = build_nasa(nasa_coeffs, 2)
line_2 = line_2_coeffs + ' 2\n'
f.write(line_2)
line_3_coeffs = build_nasa(nasa_coeffs, 3)
line_3 = line_3_coeffs + ' 3\n'
f.write(line_3)
line_4_coeffs = build_nasa(nasa_coeffs, 4)
line_4 = line_4_coeffs + ' 4\n'
f.write(line_4)
f.write('END\n')
#Write reactions to file
section_break('Reaction Data')
f.write('REACTIONS\n')
#write data for each reaction in the Solution Object
for reac_index in xrange(len(trimmed_solution.reaction_equations())):
equation_string = str(trimmed_solution.reaction_equation(reac_index))
equation_string = eliminate(equation_string, ' ', 'single')
equation_object = trimmed_solution.reaction(reac_index)
equation_type = type(equation_object).__name__
m = str(reac_index + 1)
if equation_type == 'ThreeBodyReaction':
arrhenius = build_arrhenius(equation_object, equation_type)
main_line = ('{:<51}'.format(equation_string) +
'{:>9}'.format(arrhenius[0]) +
'{:>9}'.format(arrhenius[1]) +
'{:>11}'.format(arrhenius[2]) + '\n')
f.write(main_line)
#trimms efficiencies list
efficiencies = equation_object.efficiencies
trimmed_efficiencies = equation_object.efficiencies
for s in efficiencies:
if s not in trimmed_solution.species_names:
del trimmed_efficiencies[s]
replace_list_2 = {'{': '', '}': '/', '\'': '', ':': '/', ',': '/'}
efficiencies_string = replace_multiple(str(trimmed_efficiencies),
replace_list_2)
secondary_line = str(efficiencies_string) + '\n'
if bool(efficiencies) is True:
f.write(secondary_line)
if equation_type == 'ElementaryReaction':
arrhenius = build_arrhenius(equation_object, equation_type)
main_line = ('{:<51}'.format(equation_string) +
'{:>9}'.format(arrhenius[0]) +
'{:>9}'.format(arrhenius[1]) +
'{:>11}'.format(arrhenius[2]) + '\n')
f.write(main_line)
if equation_type == 'FalloffReaction':
arr_high = build_modified_arrhenius(equation_object, 'high')
main_line = ('{:<51}'.format(equation_string) +
'{:>9}'.format(arr_high[0]) +
'{:>9}'.format(arr_high[1]) +
'{:>11}'.format(arr_high[2]) + '\n')
f.write(main_line)
arr_low = build_modified_arrhenius(equation_object, 'low')
second_line = (' LOW /' + ' ' + arr_low[0] + ' ' +
arr_low[1] + ' ' + arr_low[2] + '/\n')
f.write(second_line)
j = equation_object.falloff.parameters
#If optional Arrhenius data included:
try:
third_line = (' TROE/' + ' ' + str(j[0]) + ' ' +
str(j[1]) + ' ' + str(j[2]) + ' ' + str(j[3]) +
' /\n')
f.write(third_line)
except IndexError:
pass
#trimms efficiencies list
efficiencies = equation_object.efficiencies
trimmed_efficiencies = equation_object.efficiencies
for s in efficiencies:
if s not in trimmed_solution.species_names:
del trimmed_efficiencies[s]
replace_list_2 = {'{': '', '}': '/', '\'': '', ':': '/', ',': '/'}
efficiencies_string = replace_multiple(str(trimmed_efficiencies),
replace_list_2)
fourth_line = str(efficiencies_string) + '\n'
if bool(efficiencies) is True:
f.write(fourth_line)
#dupluicate option
if equation_object.duplicate is True:
duplicate_line = ' DUPLICATE' + '\n'
f.write(duplicate_line)
f.write('END')
f.close()
#Test mechanism file
original_solution = solution
#convert written chemkin file to cti, and get solution
parser = ck2cti.Parser()
outName = 'test_file.cti'
parser.convertMech(output_file_name, outName=outName)
new_solution = ct.Solution(outName)
#test new solution vs original solutoin
#test(original_solution, new_solution)
os.remove(outName)
return output_file_name
"""
def setUpClass(cls):
cls.gas = ct.Solution("jacobian-tests.yaml", transport_model=None)
# species: [H2, H, O, O2, OH, H2O, HO2, H2O2, AR]
cls.gas.X = [0.1, 1e-4, 1e-5, 0.2, 2e-4, 0.3, 1e-6, 5e-5, 0.4]
cls.gas.TP = 800, 2 * ct.one_atm
super().setUpClass()
fuel_inflow_temperature = 300 #[K]
# mixture fraction
mixture_fraction_element = 'N'
# Reaction on/off
reactions = 1
# Restart from previous particle list
restart = 0
## End of settings
#..........................................................................#
# get a mixture object going
gas = ct.Solution(mech)
# equilibrate the intial compposition
gas.TPY = T, 101000, initial_composition
gas.equilibrate('HP')
for counter, mass_fraction in enumerate(gas.Y):
initial_composition[gas.species_name(counter)] = mass_fraction
T = gas.T
# Mixture fraction setup
gas.TPY = T, 101000, oxidizer_inflow_composition
#elemental mass fraction of mt_element in oxidizer stream
Z_oxidizer = gas.elemental_mass_fraction(mixture_fraction_element)
_, rho_fuel = gas.TD
gas.TPY = T, 101000, fuel_inflow_composition
def setUpClass(cls):
cls.gas = ct.Solution("kineticsfromscratch.yaml", transport_model=None)
# species: [AR, O, H2, H, OH, O2, H2O, H2O2, HO2]
cls.gas.X = [0.1, 3e-4, 5e-5, 6e-6, 3e-3, 0.6, 0.25, 1e-6, 2e-5]
cls.gas.TP = 2000, 5 * ct.one_atm
super().setUpClass()
tab2 = []
for itcon in range(ncon):
for k in range(noptim):
tab2.append(k)
return (np.array(tab), np.array(tab2))
else:
return OptFn.gradient_constraints(x)
if __name__ == '__main__':
# Main process, common to all tasks (master and slaves)
# Reading input
up = imp.load_source('user_param', sys.argv[1])
# Cantera gas phase
Global.gas = ct.Solution(up.mechanism)
# Specific storage for flame computations
Global.nflames = len(up.P_fl)*len(up.T_fl)*len(up.phi_fl)
Global.flame_inits = [False for ii in range(Global.nflames)]
Global.sims = [False for ii in range(Global.nflames)]
Global.gases = [False for ii in range(Global.nflames)]
Global.directory = up.directory
Global.mechanism = up.mechanism
# Define MPI message tags
tags = Par.enum('READY', 'DONE', 'EXIT', 'START', 'SLEEP', 'WAKEUP')
# Initializations and preliminaries
Global.MPI = MPI
comm = MPI.COMM_WORLD # get MPI communicator object
def setUpClass(cls):
cls.gas = ct.Solution("h2o2.yaml", transport_model=None)
cls.gas.TPX = 300, 5 * ct.one_atm, "H2:1, O2:3"
cls.gas.equilibrate("HP")
super().setUpClass()
def reduce_drgep(model_file,
species_safe,
threshold,
importance_coeffs,
ignition_conditions,
sampled_metrics,
phase_name='',
previous_model=None,
num_threads=1,
path=''):
"""Given a threshold and DRGEP coefficients, reduce the model and determine the error.
Parameters
----------
model_file : str
Filename for model being reduced
species_safe : list of str
List of species to always be retained
threshold : float
DRG threshold for trimming graph
importance_coeffs : dict
Dictionary with species and their overall interaction coefficients.
ignition_conditions : list of InputIgnition
List of autoignition initial conditions.
sampled_metrics: numpy.ndarray
Global metrics from original model used to evaluate error
phase_name : str, optional
Optional name for phase to load from CTI file (e.g., 'gas').
previous_model : ReducedModel, optional
Model produced at previous threshold level; used to avoid repeated work.
num_threads : int, optional
Number of CPU threads to use for performing simulations in parallel.
Optional; default = 1, in which the multiprocessing module is not used.
If 0, then use the available number of cores minus one. Otherwise,
use the specified number of threads.
path : str, optional
Optional path for writing files
Returns
-------
ReducedModel
Return reduced model and associated metadata
"""
solution = ct.Solution(model_file, phase_name)
species_removed = [
sp for sp in solution.species_names
if importance_coeffs[sp] < threshold and sp not in species_safe
]
if (previous_model and len(species_removed)
== solution.n_species - previous_model.model.n_species):
return previous_model
# Cut the exclusion list from the model.
reduced_model = reduce_model.trim(model_file,
species_removed,
f'reduced_{model_file}',
phase_name=phase_name)
reduced_model_filename = soln2cti.write(
reduced_model, f'reduced_{reduced_model.n_species}.cti', path=path)
reduced_model_metrics = sampling.sample_metrics(reduced_model_filename,
ignition_conditions,
phase_name=phase_name,
num_threads=num_threads,
path=path)
error = sampling.calculate_error(sampled_metrics, reduced_model_metrics)
return reduce_model.ReducedModel(model=reduced_model,
filename=reduced_model_filename,
error=error)
def setUpClass(cls):
cls.gas = ct.Solution("gri30.yaml", transport_model=None)
cls.gas.TPX = 300, 5 * ct.one_atm, "CH4:1, C3H8:.1, O2:1, N2:3.76"
cls.gas.equilibrate("HP")
super().setUpClass()
from SDToolbox import CJspeed
from scipy.optimize import fsolve
import time
def CJ_func(comp, A):
[Vel, _] = CJspeed(101325, 298, comp, 'gri30.cti', 0)
print(Vel, comp)
A.append(Vel)
return A, Vel
start = time.time()
T = 298
P = 101325
gas = ct.Solution('gri30.cti')
j = 1
comp = 'C3H8:1, N2O:10, N2:{0}'.format(j)
gas.TPX = T, P, comp
k_N2 = gas.cp_mass / gas.cv_mass
k_CO2 = 0.0
k_m = []
i = 0
i_m = []
gas2 = ct.Solution('gri30.cti')
gas_check = ct.Solution('gri30.cti')
gas_check.TPX = T, P, 'CO2:1'
if k_N2 > gas_check.cp_mass / gas_check.cv_mass:
def test_noninteger_atomicity(self):
gas = ct.Solution('noninteger-atomicity.cti')
self.assertNear(
gas.molecular_weights[gas.species_index('CnHm')],
10.65 * gas.atomic_weight('C') + 21.8 * gas.atomic_weight('H'))
