.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "examples/multiprocessing/sensitivity_multiproc.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code. .. rst-class:: sphx-glr-example-title .. _sphx_glr_examples_multiprocessing_sensitivity_multiproc.py: .. _ref_brute_force_sensitivity_multi-processor: =============================================================== Perform brute-force sensitivity analysis on multiple processors =============================================================== The brutal force sensitivity analysis for ignition delay time shown in this example is based on the equation below: .. math:: S[i] = ln(IDT_1/IDT_2)/ln(perturb\_factor^2) :math:`IDT_1` is the ignition delay times when :math:`A` factor of reaction :math:`i` is multiplied by :math:`perturb\_factor` :math:`IDT_2` is the ignition delay times when :math:`A` factor of reaction :math:`i` is multiplied by :math:`1/perturb\_factor` *In literatures of combustion research, the* :math:`perturb\_factor` *is usually 2.0.* To conduct the brute-force A-factor sensitivity analysis, you will have to define the conditions in function ``run_cases()``, then define the mechanism files, perturb factor, and number of processors in function ``run_sens()``. Running sensitivity on multiple processors is a more efficient approach, especially when the mechanism is large. A known limit is that this script would skip reactions with negative :math:`A` factors. Therefore, the sensitivity of reactions in duplicate format with one or more negative :math:`A` factors may not be calculated correctly. This example is created by Kuiwen Zhang. .. GENERATED FROM PYTHON SOURCE LINES 54-56 .. code-block:: Python :dedent: 1 .. GENERATED FROM PYTHON SOURCE LINES 58-60 Import PyChemkin package and start the logger ============================================= .. GENERATED FROM PYTHON SOURCE LINES 60-84 .. code-block:: Python import multiprocessing as mp # multi processors import os from pathlib import Path import time import matplotlib.pyplot as plt # plotting import numpy as np # number crunching import ansys.chemkin.core as ck # Chemkin from ansys.chemkin.core import Color # chemkin batch reactor models (transient) from ansys.chemkin.core.batchreactors.batchreactor import ( GivenVolumeBatchReactorEnergyConservation as BatchReactor, ) # set interactive mode for plotting the results # interactive = True: display plot # interactive = False: save plot as a PNG file global interactive interactive = True .. GENERATED FROM PYTHON SOURCE LINES 85-89 Run a list of cases ==================== This function runs a list of :math:`i` cases. In each case, the :math:`A` factor of reaction :math:`ids[i]` will be multiplied by :math:`factors[i]`. .. GENERATED FROM PYTHON SOURCE LINES 89-232 .. code-block:: Python def run_cases(work_path: str, chemfile: str, thermfile: str, ids: list, factors: list): """ Run a list of cases for sensitivity analysis. Parameters ---------- work_path : str The working directory for the cases. chemfile : str The full path to the Chemkin gas chemistry file. thermfile : str The full path to the Chemkin thermodynamic data file. ids : list A list of reaction indices for which the A factor will be modified. factors : list A list of factors by which the A factor of the corresponding reaction will be multiplied. """ # check working directory os.chdir(work_path) # set verbose mode ck.set_verbose(False) ###################################### # Create a chemistry set for all cases # ==================================== my_gas_mech = ck.Chemistry(label="GRI 3.0") my_gas_mech.chemfile = chemfile my_gas_mech.thermfile = thermfile ################################### # Pre-process the ``Chemistry Set`` # ================================= _ = my_gas_mech.preprocess() #################################################################### # Set up gas mixtures based on the species in this ``Chemistry Set`` # ================================================================== # Use the *equivalence ratio method* so that you can easily set up # the premixed fuel-oxidizer mixture composition by assigning an # the *equivalence ratio* value. In this case, the fuel mixture consists # of methane, ethane, and propane as the simulated "natural gas". # The premixed air-fuel mixture has an equivalence ratio of 1.1. oxid = ck.Mixture(my_gas_mech) oxid.x = [("O2", 0.21), ("N2", 0.79)] oxid.temperature = 900 oxid.pressure = ck.P_ATM fuel = ck.Mixture(my_gas_mech) fuel.x = [("C3H8", 0.1), ("CH4", 0.8), ("H2", 0.1)] fuel.temperature = oxid.temperature fuel.pressure = oxid.pressure equi = 1.1 mixture = ck.Mixture(my_gas_mech) mixture.pressure = oxid.pressure mixture.temperature = oxid.temperature products = ["CO2", "H2O", "N2"] add_frac = np.zeros(my_gas_mech.kk, dtype=np.double) # create the air-fuel mixture by using the equivalence ratio method ierror = mixture.x_by_equivalence_ratio( my_gas_mech, fuel.x, oxid.x, add_frac, products, equivalenceratio=equi ) # check fuel-oxidizer mixture creation status if ierror != 0: print("Error: Failed to create the fuel-oxidizer mixture.") exit() ################################################################ # Create the reactor object for ignition delay time calculations # ============================================================== # Use ``GivenVolumeBatchReactorEnergyConservation`` to instantiate a # *constant volume batch reactor that also includes the energy equation*. You # should use the ``mixture`` you just created. conv_bomb = BatchReactor(mixture, label="CONV") # show initial gas composition inside the reactor for verification conv_bomb.list_composition(mode="mole") # ########################################## # Set up additional reactor model parameters # ========================================== conv_bomb.volume = 10.0 conv_bomb.pressure = 20.0 * ck.P_ATM # simulation end time [sec] conv_bomb.time = 0.1 # turn ON adaptive solution saving conv_bomb.adaptive_solution_saving(mode=True, steps=20) # set ignition delay conv_bomb.set_ignition_delay(method="T_inflection") # set tolerances in tuple: (absolute tolerance, relative tolerance) conv_bomb.tolerances = (1.0e-10, 1.0e-8) ################################### # Change A factor and run the cases # ================================== # Get original A factors afactor, beta, active_energy = my_gas_mech.get_reaction_parameters() # create sensitivity coefficient array idts = [] # Create a file for sensitivity analysis results output with Path("sen_output.txt").open("w", encoding="utf-8") as f: f.write(f"cases to run = {len(ids)}, from {ids[0] + 1} to {ids[-1] + 1}\n") f.flush() # loop over all reactions in the array for i in range(len(ids)): # Skip negative A factors if afactor[ids[i]] < 0: delaytime = conv_bomb.time else: afactor_new = afactor[ids[i]] * factors[i] # actual reaction index ireac = ids[i] + 1 # update the A factor my_gas_mech.set_reaction_afactor(ireac, afactor_new) # run the reactor model conv_bomb.stop_output() runstatus = conv_bomb.run() if runstatus == 0: # get ignition delay time delaytime = conv_bomb.get_ignition_delay() # restore the A factor my_gas_mech.set_reaction_afactor(ireac, afactor[ids[i]]) else: # if get this, most likely the END time is too short print(f"trouble finding ignition delay time for reaction {ireac}") print(Color.RED + ">>> Run failed. <<<", end=Color.END) # Set ignition delay time to simulation time limit delaytime = conv_bomb.time idts.append(delaytime) # write reaction id and ignitino delay time to output file f.write(f"{ids[i] + 1}, {delaytime}\n") f.flush() # f.close() # No need to close explicitly when using 'with' context # Return the ignition delay times return idts .. GENERATED FROM PYTHON SOURCE LINES 233-235 Run brutal force sensitivity analysis on multiple processors ============================================================= .. GENERATED FROM PYTHON SOURCE LINES 235-387 .. code-block:: Python def run_sens(): """ Run brutal force sensitivity analysis on multiple processors. This function sets up the mechanism, divides the tasks among multiple processors, and computes the sensitivity coefficients for the ignition delay times. """ # current_dir = str(Path.cwd()) num_processors = 6 perturb = 1 perturb_factor = ( 1.0 + perturb ) # A factor is multiplied and divided by 2 in sensitivity analysis # set mechanism directory (the default Chemkin mechanism data directory) data_dir = Path(ck.ansys_dir) / "reaction" / "data" mechanism_dir = data_dir # create a chemistry set based on the diesel 14 components mechanism my_gas_mech = ck.Chemistry(label="GRI 3.0") # set mechanism input files # including the full file path is recommended my_gas_mech.chemfile = str(mechanism_dir / "grimech30_chem.inp") my_gas_mech.thermfile = str(mechanism_dir / "grimech30_thermo.dat") # set the start wall time to get the total simulation run time start_time = time.time() ierror = my_gas_mech.preprocess() if ierror == 0: print("mechanism information:") print(f"number of gas species = {my_gas_mech.kk:d}") print(f"number of gas reactions = {my_gas_mech.ii_gas:d}") else: # When a non-zero value is returned from the process, # check the text output files # chem.out, tran.out, or summary.out for potential error messages # about the mechanism data. print(f"Preprocessing error encountered. Code = {ierror:d}.") print(f"see the summary file {my_gas_mech.summaryfile} for details") exit() ids = np.arange(my_gas_mech.ii_gas) ids = np.repeat(ids, 2) factors = np.tile([perturb_factor, 1 / perturb_factor], my_gas_mech.ii_gas) base_num_task = len(ids) // num_processors # basic number of tasks per core num_shift = len(ids) % num_processors # number of cores taking +1 task args = [] offset = 0 # make a new directory for each array of cases and run the cases on # multiple processors for i in range(num_processors): work_path = Path(current_dir) / "RUN_" / str(i + 1).zfill(4) if not work_path.exists(): work_path.mkdir(parents=True, exist_ok=True) if i < num_shift: div = base_num_task + 1 else: div = base_num_task args.append( ( work_path, my_gas_mech.chemfile, my_gas_mech.thermfile, ids[offset : offset + div], factors[offset : offset + div], ) ) offset += div with mp.Pool(processes=num_processors) as pool: results = pool.starmap(run_cases, args) flat_results = np.concatenate(results) sens = (np.log(flat_results[0::2]) - np.log(flat_results[1::2])) / ( np.log(perturb_factor * perturb_factor) ) top = 10 # rank the positive coefficients posindex = np.argpartition(sens, -top)[-top:] poscoeffs = sens[posindex] pos_sorted_index = np.argsort(poscoeffs) pos_sorted = poscoeffs[pos_sorted_index] # rank the negative coefficients neg_ig_sen = np.negative(sens) negindex = np.argpartition(neg_ig_sen, -top)[-top:] negcoeffs = sens[negindex[::-1]] neg_sorted_index = np.argsort(negcoeffs) neg_sorted = negcoeffs[neg_sorted_index] # print the top sensitivity coefficients print("positive sensitivity coefficients") for i in range(top): print( f"reaction {posindex[np.flip(pos_sorted_index)[i]] + 1}: " f"coefficient = {np.flip(pos_sorted)[i]}" ) print() print("negative sensitivity coefficients") for i in range(top): print( f"reaction {negindex[neg_sorted_index[i]] + 1}: " f"coefficient = {neg_sorted[i]}" ) # compute and report the total runtime (wall time) runtime = time.time() - start_time print( f"\ntotal simulation time: {runtime} [sec] over {my_gas_mech.ii_gas + 1} runs" ) ########################################## # Plot the ranked sensitivity coefficients # ======================================== # Create plots to show the reactions whose A-factors have most positive # and negative influence on the ignition delay time. plt.rcParams.update({"figure.autolayout": True, "ytick.color": "blue"}) plt.subplots(2, 1, sharex="col", figsize=(10, 5)) # convert reaction # from integers to strings rxnstring = [] for i in range(top): # the array index starting from 0 so the actual reaction # = index + 1 rxnid = posindex[pos_sorted_index[i]] + 1 # add reaction index before reaction string rxnstring.append(str(rxnid) + ". " + my_gas_mech.get_gas_reaction_string(rxnid)) # use horizontal bar chart plt.subplot(211) plt.barh(rxnstring, pos_sorted, color="orange", height=0.4) plt.axvline(x=0, c="gray", lw=1) # convert reaction # from integers to strings rxnstring.clear() for i in range(top): # the array index starting from 0 so the actual reaction # = index + 1 rxnid = negindex[neg_sorted_index[i]] + 1 rxnstring.append(str(rxnid) + ". " + my_gas_mech.get_gas_reaction_string(rxnid)) plt.subplot(212) plt.barh(rxnstring, neg_sorted, color="orange", height=0.4) plt.axvline(x=0, c="gray", lw=1) plt.xlabel("Sensitivity Coefficients") plt.suptitle("Ignition Delay Time Sensitivity", fontsize=16) # plot results if interactive: plt.show() else: plt.savefig("plot_sensitivity_analysis_multiproc.png", bbox_inches="tight") return if __name__ == "__main__": run_sens() .. _sphx_glr_download_examples_multiprocessing_sensitivity_multiproc.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: sensitivity_multiproc.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: sensitivity_multiproc.py ` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: sensitivity_multiproc.zip ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_