DoE-ML guided optimization of an active pre-chamber geometry using CFD

Mickael Silva*, Balaji Mohan, Jihad Badra, Anqi Zhang, Ponnya Hlaing, Emre Cenker, Abdullah S. AlRamadan, Hong G. Im

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

An optimized active pre-chamber geometry was obtained by combining computational fluid dynamics (CFD) and machine learning (ML). A heavy-duty engine operating with methane under lean conditions was considered. The combustion process was modeled with a multi-zone well-stirred reactor (MZ-WSR) with a skeletal methane oxidation mechanism. The simulations were run for a complete cycle. For the optimization study, the pre-chamber was parametrized; six independent and three dependent variables were considered, while the volume was kept constant. Three hundred pre-chamber designs were generated, and a one-shot design of experiments (DoE) optimization was first considered. A merit function was adopted to rank the designs, and an optimum design was found from the DoE results, which yielded considerable improvements in merit ranking, considering fuel consumption, engine-out emissions, noise, and safety; secondly, machine learning algorithms were trained by utilizing the DoE results aiming at finding a globally optimum geometry for the considered operating condition. Five sequential iterations were performed, and the ML algorithms were capable of proposing a new design with superior performance compared to the best DoE.

Original languageEnglish (US)
Pages (from-to)2936-2948
Number of pages13
JournalInternational Journal of Engine Research
Volume24
Issue number7
DOIs
StatePublished - Jul 2023

Keywords

  • computational fluid dynamics (CFD)
  • design of experiments (DoE)
  • engines
  • machine learning (ML)
  • Pre-chamber combustion (PCC)

ASJC Scopus subject areas

  • Automotive Engineering
  • Aerospace Engineering
  • Ocean Engineering
  • Mechanical Engineering

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