pyMechOpt can optimize the chemical reaction mechanism and reduce the difference with the detailed reaction mechanism.
It is recommended to install pyMechOpt in a newly created python environment.
| Package | version |
|---|---|
| Cantera | >=3.00 |
| Pymoo | >=0.6.0 |
| pyDecision | >=4.5.8 |
After installing the above package, you can follow the following command to install pyMechOpt:
git clone https://github.com/disihan/pyMechOpt.git
pip install ./pyMechOpt
You can import modules by using the following code:
import numpy as np
import cantera as ct
from pyMechOpt.multi_obj import mo_problem # For multi-objective
# from pyMechOpt.single_obj import so_problem # For single-objective
# from pyMechOpt.line_search import ls_problem # For line search method
Firstly, provide the mechanism that needs to be optimized and an accurate mechanism:
mech_detailed = 'mech/gri30.yaml' # detailed mech
mech_reduced = 'mech/gri30-r45.yaml' # reduced mech
Given the species to be optimized and the initial conditions:
spcs_int = ['CH4',
'O2']
spcs_peak = ['CH2O',
'CO',
'CO2']
pressure = 1e05 * np.array([1, 1, 1, 100, 100, 100])
temperature = np.array([1000, 1000, 1000, 1000, 1000, 1000])
ratio = np.array([0.25, 1, 4, 0.25, 1, 4])
fuel = "CH4:1"
oxydizer = "O2:1"
Then you can start optimizing.
opt_prob = mo_problem(
mech_detailed,
mech_reduced,
fuel,
oxydizer,
temperature,
ratio,
pressure,
spcs_int,
spcs_peak,
range_input=0.1,
weight=[1, 1, 1, 1],
hist_dir="./hist_nsga3/",
res_dir="./res_nsga3/",
)
opt_prob.calc_skip()
opt_prob.run(algorithm="NSGA3", max_gen=400, pop_size=200)