The combustion system of a heavy-duty diesel engine operated in a gasoline compression ignition mode was optimized using a CFD-based response surface methodology and a machine learning genetic algorithm. One common dataset obtained from a CFD design of experiment campaign was used to construct response surfaces and train machine learning models. 128 designs were included in the campaign and were evaluated across three engine load conditions using the CONVERGE CFD solver. The design variables included piston bowl geometry, injector specifications, and swirl ratio, and the objective variables were fuel consumption, criteria emissions, and mechanical design constraints. In this study, the two approaches were extensively investigated and applied to a common dataset. The response surface-based approach utilized a combination of three modeling techniques to construct response surfaces to enhance the performance of predictions. The machine learning-genetic algorithm optimization strategy adopted an active learning approach. Its training and prediction accuracies were significantly improved by combining the datasets from three different loads into one single training dataset. Both methodologies generated designs that performed better than the optimal CFD-DoE design, with improved weighted average merit values 6% above the baseline.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Machine Learning and Response Surface-Based Numerical Optimization of the Combustion System for a Heavy-Duty Gasoline Compression Ignition Engine


    Additional title:

    Sae Technical Papers


    Contributors:

    Conference:

    SAE WCX Digital Summit ; 2021



    Publication date :

    2021-04-06




    Type of media :

    Conference paper


    Type of material :

    Print


    Language :

    English




    EGR System Optimization for Light-Duty Gasoline Compression Ignition (GCI) Engine

    Sellnau, Mark / Kumar, Praveen | SAE Technical Papers | 2021


    Computational Optimization of a Heavy-Duty Compression Ignition Engine Fueled with Conventional Gasoline

    Dempsey, A.B. / Reitz, R.D. / Society of Automotive Engineers | British Library Conference Proceedings | 2011



    Numerical Evaluation of Gasoline Compression Ignition at Cold Conditions in a Heavy-Duty Diesel Engine

    Zhao, Le / Traver, Michael / Ameen, Muhsin et al. | SAE Technical Papers | 2020


    Numerical Optimization of a Light-Duty Compression Ignition Engine Fuelled With Low-Octane Gasoline

    Adhikary, B.D. / Ra, Y. / Reitz, R. et al. | British Library Conference Proceedings | 2012