In the present paper, 3D combustion CFD coupled with design of experiment (DOE), multi-objective genetic algorithm, and response surface model is used to optimize the prechamber in a spark ignition heavy-duty engine. The engine is based on the architecture of a compression ignition engine. The geometry of the prechamber is characterized using 7 design parameters. Additionally, spark timing, nozzle hole size, jet angle, and swirl ratio are considered as design parameters. A novel optimization process is  developed, which starts from a DOE matrix of the 11 design parameters. 3D combustion CFD simulations are conducted with the inputs from the DOE matrix. The objectives include the gross indicated specific fuel consumption (GISFC), NOx, and soot emissions.The CFD results are then used to train the response surface model. The trained response surface model is used in the multi-objective genetic algorithm optimization without CFD runs. The optimized design suggested by this non-CFD optimization is confirmed in separated CFD runs. The new CFD results are added into the database to further train the response surface model. Iteration runs until a satisfactory result is achieved. Detailed analysis is conducted to investigate the physical mechanism about how the design of prechamber influence the engine's performance. The method can be applied to any engine optimization.


Computational Optimization of the Prechamer of a Spark Ignition Engine

Division / Sponsors: 
Internal Combustion Engine Division (ICED)

Paper Proceedings: https://asmedigitalcollection.asme.org/ICEF/ICEF2020/volume/84034

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