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The study showcases the strength of machine learning (ML) models in imitating the operation of an advanced engine concept - the gasoline compression ignition (GCI) - at low loads. The GCI engine is prone to exceeding the limits of criteria emissions at such loads, especially at the cold start when the catalyst is not activated. One proposition to accelerate catalyst light-off is using spark-ignition. This, however, adds an extra level of complexity in identifying an optimum operation point. The ML models can be a useful tool in guiding the engine calibration process. In this study, the ML models are trained on GCI engine experiments, covering different intake conditions, injection strategies, and spark settings. The models can predict seven engine performance parameters: fuel consumption, four engine-out emissions, exhaust temperature, and coefficient of variation (COV) in indicated mean effective pressure (IMEP).

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.

Gasoline compression ignition (GCI) engines are becoming more popular alternative for conventional spark engines to harvest the advantage of high volatility. Recent experimental study demonstrated that high reactivity gasoline fuel can be operated in a conventional mixing controlled combustion mode producing lower soot emissions than that of diesel fuel under similar efficiency and NOx level [1]. Therefore, there is much interest in using gasoline-like fuels in compression ignition engines. In order to improve the fidelity of simulation-based GCI combustion system development, it is mandatory to enhance the prediction of spray combustion of gasoline-like fuels. The purpose of this study is to model the spray characteristics of high reactivity gasoline fuels and validate the models with experimental results obtained through an optically accessible constant volume vessel under vaporizing [2] and reactive conditions [3].