Journal of ILASS-Korea (한국분무공학회지)

Institute for Liquid Atomization and Spray Systems-Korea (한국분무공학회)
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Measurement and Prediction of Spray Targeting Points according to Injector Parameter and Injection Condition

인젝터 설계변수 및 분사조건에 따른 분무타겟팅 지점의 측정 및 예측

  • ;
  • ;
  • 박수한 (건국대학교 기계항공공학부)
  • Received : 2022.11.11
  • Accepted : 2022.12.07
  • Published : 2023.03.31
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Abstract

In the cylinder of gasoline direct injection engines, the spray targeting from injectors is of great significance for fuel consumption and pollutant emissions. The automotive industry is putting a lot of effort into improving injector targeting accuracy. To improve the targeting accuracy of injectors, it is necessary to develop models that can predict the spray targeting positions. When developing spray targeting models, the most used technique is computational fluid dynamics (CFD). Recently, due to the superiority of machine learning in prediction accuracy, the application of machine learning in this field is also receiving constant attention. The purpose of this study is to build a machine learning model that can accurately predict spray targeting based on the design parameters of injectors. To achieve this goal, this study firstly used laser sheet beam visualization equipment to obtain many spray cross-sectional images of injectors with different parameters at different injection pressures and measurement planes. The spray images were processed by MATLAB code to get the targeting coordinates of sprays. A total of four models were used for the prediction of spray targeting coordinates, namely ANN, LSTM, Conv1D and Conv1D & LSTM. Features fed into the machine learning model include injector design parameters, injection conditions, and measurement planes. Labels to be output from the model are spray targeting coordinates. In addition, the spray data of 7 injectors were used for model training, and the spray data of the remaining one injector were used for model performance verification. Finally, the prediction performance of the model was evaluated by R2 and RMSE. It is found that the Conv1D&LSTM model has the highest accuracy in predicting the spray targeting coordinates, which can reach 98%. In addition, the prediction bias of the model becomes larger as the distance from the injector tip increases.

Keywords

Acknowledgement

이 연구는 한국연구재단의 한-인도 해외협력기반조성사업(2020K1A3A1A19088692)의 지원으로 수행되었습니다. 지원기간에 감사드립니다.

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