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Title: Evaluating Optimization Strategies for Engine Simulations Using Machine Learning Emulators

Abstract

This work evaluates different optimization algorithms for computational fluid dynamics (CFD) simulations of engine combustion. Due to the computational expense of CFD simulations, emulators built with machine learning algorithms were used as surrogates for the optimizers. Two types of emulators were used: a Gaussian process (GP) and a weighted variety of machine learning methods called SuperLearner (SL). The emulators were trained using a dataset of 2048 CFD simulations that were run concurrently on a supercomputer. The design of experiments (DOE) for the CFD runs was obtained by perturbing nine input parameters using a Monte-Carlo method. The CFD simulations were of a heavy duty engine running with a low octane gasoline-like fuel at a partially premixed compression ignition mode. Ten optimization algorithms were tested, including types typically used in research applications. Each optimizer was allowed 800 function evaluations and was randomly tested 100 times. The optimizers were evaluated for the median, minimum, and maximum merits obtained in the 100 attempts. Some optimizers required more sequential evaluations, thereby resulting in longer wall clock times to reach an optimum. The best performing optimization methods were particle swarm optimization (PSO), differential evolution (DE), GENOUD (an evolutionary algorithm), and micro-genetic algorithm (GA). These methods foundmore » a high median optimum as well as a reasonable minimum optimum of the 100 trials. Moreover, all of these methods were able to operate with less than 100 successive iterations, which reduced the wall clock time required in practice. Two methods were found to be effective but required a much larger number of successive iterations: the DIRECT and MALSCHAINS algorithms. A random search method that completed in a single iteration performed poorly in finding optimum designs but was included to illustrate the limitation of highly concurrent search methods. The last three methods, Nelder–Mead, bound optimization by quadratic approximation (BOBYQA), and constrained optimization by linear approximation (COBYLA), did not perform as well.« less

Authors:
; ; ; ; ; ; ;
Publication Date:
Research Org.:
Argonne National Lab. (ANL), Argonne, IL (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE) - Office of Vehicle Technologies (VTO)
OSTI Identifier:
1550800
DOE Contract Number:  
AC02-06CH11357
Resource Type:
Journal Article
Journal Name:
Journal of Engineering for Gas Turbines and Power
Additional Journal Information:
Journal Volume: 141; Journal Issue: 9
Country of Publication:
United States
Language:
English

Citation Formats

Probst, Daniel M., Raju, Mandhapati, Senecal, Peter K., Kodavasal, Janardhan, Pal, Pinaki, Som, Sibendu, Moiz, Ahmed A., and Pei, Yuanjiang. Evaluating Optimization Strategies for Engine Simulations Using Machine Learning Emulators. United States: N. p., 2019. Web. doi:10.1115/1.4043964.
Probst, Daniel M., Raju, Mandhapati, Senecal, Peter K., Kodavasal, Janardhan, Pal, Pinaki, Som, Sibendu, Moiz, Ahmed A., & Pei, Yuanjiang. Evaluating Optimization Strategies for Engine Simulations Using Machine Learning Emulators. United States. doi:10.1115/1.4043964.
Probst, Daniel M., Raju, Mandhapati, Senecal, Peter K., Kodavasal, Janardhan, Pal, Pinaki, Som, Sibendu, Moiz, Ahmed A., and Pei, Yuanjiang. Thu . "Evaluating Optimization Strategies for Engine Simulations Using Machine Learning Emulators". United States. doi:10.1115/1.4043964.
@article{osti_1550800,
title = {Evaluating Optimization Strategies for Engine Simulations Using Machine Learning Emulators},
author = {Probst, Daniel M. and Raju, Mandhapati and Senecal, Peter K. and Kodavasal, Janardhan and Pal, Pinaki and Som, Sibendu and Moiz, Ahmed A. and Pei, Yuanjiang},
abstractNote = {This work evaluates different optimization algorithms for computational fluid dynamics (CFD) simulations of engine combustion. Due to the computational expense of CFD simulations, emulators built with machine learning algorithms were used as surrogates for the optimizers. Two types of emulators were used: a Gaussian process (GP) and a weighted variety of machine learning methods called SuperLearner (SL). The emulators were trained using a dataset of 2048 CFD simulations that were run concurrently on a supercomputer. The design of experiments (DOE) for the CFD runs was obtained by perturbing nine input parameters using a Monte-Carlo method. The CFD simulations were of a heavy duty engine running with a low octane gasoline-like fuel at a partially premixed compression ignition mode. Ten optimization algorithms were tested, including types typically used in research applications. Each optimizer was allowed 800 function evaluations and was randomly tested 100 times. The optimizers were evaluated for the median, minimum, and maximum merits obtained in the 100 attempts. Some optimizers required more sequential evaluations, thereby resulting in longer wall clock times to reach an optimum. The best performing optimization methods were particle swarm optimization (PSO), differential evolution (DE), GENOUD (an evolutionary algorithm), and micro-genetic algorithm (GA). These methods found a high median optimum as well as a reasonable minimum optimum of the 100 trials. Moreover, all of these methods were able to operate with less than 100 successive iterations, which reduced the wall clock time required in practice. Two methods were found to be effective but required a much larger number of successive iterations: the DIRECT and MALSCHAINS algorithms. A random search method that completed in a single iteration performed poorly in finding optimum designs but was included to illustrate the limitation of highly concurrent search methods. The last three methods, Nelder–Mead, bound optimization by quadratic approximation (BOBYQA), and constrained optimization by linear approximation (COBYLA), did not perform as well.},
doi = {10.1115/1.4043964},
journal = {Journal of Engineering for Gas Turbines and Power},
number = 9,
volume = 141,
place = {United States},
year = {2019},
month = {6}
}