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Predictions and analysis of flash boiling spray characteristics of gasoline direct injection injectors based on optimized machine learning algorithm

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  • Chang, Mengzhao
  • Park, Suhan

Abstract

The main purpose of this study is to make a machine model that can predict the characteristics of flash boiling spray according to the injector design parameters and the injection conditions, and then use the machine learning model to further analyze the spray characteristics. In order to achieve described purpose, this study uses three decision tree algorithms (random forest (RF), gradient boosted regression tree (GBRT) and extreme gradient boosting (XGB)) to build machine learning models, and utilizes the tree-structured parzen estimator (TPE) for farther improvement of models' performance. The influence of injector design parameters and injection conditions on spray characteristics was analyzed, basing on the constructed machine learning model. Finally, the spray characteristic map was made by predicting the spray characteristics under 1 million conditions, and the method of using the spray characteristic map to design the spray characteristics was proposed. The results have shown that TPE-GBRT has the most accurate prediction result for spray tip penetration (STP), and TPE-XGB has the most accurate result for spray downstream angle (SA_down). The feature importance analysis indicated the greater importance of the injection conditions over the injector design parameters for the spray characteristics. Further, by analyzing the spray characteristics map, the injection conditions have less design freedom, while injector parameters can assume different values to achieve the same spray characteristics.

Suggested Citation

  • Chang, Mengzhao & Park, Suhan, 2023. "Predictions and analysis of flash boiling spray characteristics of gasoline direct injection injectors based on optimized machine learning algorithm," Energy, Elsevier, vol. 262(PA).
    • Handle: RePEc:eee:energy:v:262:y:2023:i:pa:s0360544222021880
    DOI: 10.1016/j.energy.2022.125304
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    References listed on IDEAS

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