• 한국분무공학회지
• /
• 제28권1호
• /
• pp.1-9
• /
• 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.

• 한국추진공학회:학술대회논문집
• /
• 한국추진공학회 2005년도 제25회 추계학술대회논문집
• /
• pp.224-228
• /
• 2005

동적 감쇠자로서 백홀에 대한 동적 제어 테스트를 수행하였다. 공급라인에서 압력 섭동에 의해 발생되는 가진과 스월 인젝터의 내부 파동 분석을 위해 수력학적 진동발생기와 1차원 가시화 모델 인젝터가 제작되었다. 음향학적 대신에 동적 감쇠자로서 백홀의 영향에 초점을 두었기 때문에 액막의 분열길이와 액막 두께를 측정하여 정상 상태와 가진 상태의 결과를 비교하였다.

• 한국분무공학회지
• /
• 제25권1호
• /
• pp.8-14
• /
• 2020

Recently, 1-D model-based engine development using virtual engine system is getting more attention than experimental-based engine development due to the advantages in time and cost. Injection rate profile is the one of the main parameters that determine the start and end of combustion. Therefore, it is essential to set up a sophisticated model to accurately predict the injection rate as starting point of virtual engine system. In this research, procedure of 1-D model setup based on AMESim is introduced to predict the dynamic behavior and injection rate of diesel injector. As a first step, detailed 3D cross-sectional drawing of the injector was achieved, which can be done with help of precision measurement system. Then an approximate AMESim model was provided based on the 3D drawing, which is composed of three part such as solenoid part, control chamber part and needle and nozzle orifice part. However, validation results in terms of total injection quantity showed some errors over the acceptable level. Therefore, experimental work including needle movement visualization, solenoid part analysis and flow characteristics of injector part was performed together to provide more accuracy of 1-D model. Finally, 1-D model with the accuracy of less than 10% of error compared with experimental result in terms of injection quantity and injection rate shape under normal temperature and single injection condition was established. Further work considering fuel temperature and multiple injection will be performed.