Engine Combustion System Optimization Using Computational Fluid Dynamics and Machine Learning: A Methodological Approach

Jihad A. Badra, Fethi KHALED, Meng Tang, Yuanjiang Pei, Janardhan Kodavasal, Pinaki Pal, Opeoluwa Owoyele, Carsten Fuetterer, Brenner Mattia, Farooq Aamir

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

Abstract 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.
Original languageEnglish (US)
JournalJournal of Energy Resources Technology
Volume143
Issue number2
DOIs
StatePublished - Aug 6 2020

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