Engine Combustion System Optimization Using Computational Fluid Dynamics and Machine Learning: A Methodological Approach
AuthorsBadra, Jihad A.
KAUST DepartmentChemical Kinetics & Laser Sensors Laboratory
Clean Combustion Research Center
Mechanical Engineering Program
Physical Science and Engineering (PSE) Division
Online Publication Date2020-08-27
Print Publication Date2021-02-01
Permanent link to this recordhttp://hdl.handle.net/10754/664988
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AbstractAbstract Gasoline compression ignition (GCI) engines are considered an attractive alternative to traditional spark-ignition and diesel engines. In this work, a Machine Learning-Grid Gradient Ascent (ML-GGA) approach was developed to optimize the performance of internal combustion engines. ML offers a pathway to transform complex physical processes that occur in a combustion engine into compact informational processes. The developed ML-GGA model was compared with a recently developed Machine Learning-Genetic Algorithm (ML-GA). Detailed investigations of optimization solver parameters and variable limit extension were performed in the present ML-GGA model to improve the accuracy and robustness of the optimization process. Detailed descriptions of the different procedures, optimization tools, and criteria that must be followed for a successful output are provided here. The developed ML-GGA approach was used to optimize the operating conditions (case 1) and the piston bowl design (case 2) of a heavy-duty diesel engine running on a gasoline fuel with a research octane number (RON) of 80. The ML-GGA approach yielded >2% improvements in the merit function, compared with the optimum obtained from a thorough computational fluid dynamics (CFD) guided system optimization. The predictions from the ML-GGA approach were validated with engine CFD simulations. This study demonstrates the potential of ML-GGA to significantly reduce the time needed for optimization problems, without loss in accuracy compared with traditional approaches.
CitationBadra, J. A., Khaled, F., Tang, M., Pei, Y., Kodavasal, J., Pal, P., … Aamir, F. (2020). Engine Combustion System Optimization Using Computational Fluid Dynamics and Machine Learning: A Methodological Approach. Journal of Energy Resources Technology, 143(2). doi:10.1115/1.4047978
SponsorsThis work has been supported by the Transport Technologies Division at Saudi Aramco R&DC. We would also like to thank Aramco Services Company for their support with the computing cluster at the Aramco Research Center Houston. The submitted manuscript was created partly by UChicago Argonne, LLC, Operator of Argonne National Laboratory. Argonne, a U.S. Department of Energy (DOE) Office of Science Laboratory, is operated under Contract No. DE-AC02-06CH11357. This research was partly funded by the U.S. DOE Office of Vehicle Technologies, Office of Energy Efficiency and Renewable Energy under Contract No. DE-AC02-06CH11357. Blues High Performance LCRC cluster facilities at Argonne National Laboratory were used for some of the simulations. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan. http://energy.gov/downloads/doepublic-accessplan