• Journal of ILASS-Korea
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• v.28 no.1
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• pp.1-9
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• 2023

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.

• 한국신재생에너지학회:학술대회논문집
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• 2010.11a
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• pp.41.1-41.1
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• 2010

전기수력학적 분무를 이용한 액적 미립화 기술은 나노사이즈의 액적 형성, 쿨롱 반발력에 의한 균일한 액적 형성, 그리고 향상된 액적 타겟팅을 가능하게 한다. 따라서 이를 이용하여 매우 균일한 박막 코팅이 가능하다. 이러한 점에 힘입어 현재 진공 공정으로 제작되고 있는 CIGS태양전지의 광흡수층을 비진공 공정중 하나인 전기수력학적 미립화를 이용하여 실험하였다. Ethanol-based 의 CIGS나노 입자를 포함하는 콜로이드 상태의 전구체를 이용하여 적절히 가열된 몰리브덴 배면 전극위에 적용하였다. 미립화한 액적은 접지된 몰리브덴 층에 부착되는 즉시 증발하여 CIGS입자를 남긴다. 여기서 가장 중요하게 다루어야 할 조건은 기판의 온도, 인가 전압, 전구체의 유량이다. 분사 모드는 Cone-jet을 적용하였으며 5~15kV의 인가 전압에서 1ml/hr내외의 유량을 공급하여 3분 이내에 적절한 광흡수층 두께인 1마이크론 내외에 도달할 수 있다. 이와같은 조건으로 형성된 박막층에 관한 SEM image를 통해 다른 비진공 코팅 방식과 비교하였다.