Development of High Compression-Ratio Stepped-Lip Piston using Machine Learning

Prabhat Jha; Chris Bitsis; Edward Smith; Thomas Briggs; Zainal Abidin; Bansal Shah; Khanh Cung

doi:10.4271/2022-01-1054

Interaction between a diesel spray and piston plays a significant role in overall combustion and emissions performance in compression-ignition engines. It is essential to design the lip feature respective to spray targeting and the following charge motion for combustion systems that rely on spray-piston interaction strongly, such as a stepped-lip piston. This study used a numerical campaign using computational fluid dynamics (CFD) simulation to optimize a stepped-lip combustion system at a 22:1 compression ratio (CR) for both performance and emissions. This is a substantial step up in CR from the stock value of 17:1 for the same engine platform.

A machine learning model was used to identify the best combination of features from a design space involving hundreds of potential piston designs and injector nozzle configurations. This study provides a discussion on the general combustion characteristics of the stepped-lip combustion system and the sensitivity of the design parameters. Comparison of the CFD predictions for the final system design and single-cylinder engine (SCE) experimental results showed good agreement for combustion performance and emissions. Further opportunities for improvements to the combustion system are also discussed.

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Development of High Compression-Ratio Stepped-Lip Piston using Machine Learning 2022-01-1054

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