| 제목 |
CNG-디젤 이종연료 엔진에서 학습 조건이 엔진 블록 진동을 이용한 실린더
내부압력 예측 DNN 모델의 정확성에 미치는 영향 |
| 분야 |
엔진 및 연소 |
| 언어 |
Korean |
| 저자 |
박찬수(한국과학기술원), 김경곤(한국과학기술원), 김우영(한국과학기술원), 전지연(한국과학기술원), 전미연(한국과학기술원), 배충식(한국과학기술원) |
| Key Words |
Effect of training condition(학습조건의 영향), CNG-diesel dual-fuel engine(천연가스-디젤 이중연료 엔진), Deep neural network(딥 뉴런 네트워크), Engine block vibration(엔진 블록 진동), In-cylinder pressure prediction(실린더 내부 압력 예측), Accelerometer(가속도센서) |
| 초록 |
A deep neural network(DNN) model was developed in order to predict in-cylinder pressure. Engine block vibration signal and in-cylinder pressure measured by accelerometer and pressure transducer were used as the input and output of the model, respectively. The test engine was a single cylinder compressed natural gas-diesel(CNG diesel) dual fuel compression ignition engine. In order to evaluate the effect of training conditions on the accuracy of the model, CNG substitution ratio, diesel injection timing, and intake air pressure were changed while maintaining an engine load of 0.6 MPa. Test conditions were composed of unseen engine conditions in the training conditions to avoid overfitting problems. Basically, the coefficient of determination(R2) between the predicted and measured in-cylinder pressure trace was used as an indicator of the model performance. Peak in-cylinder pressure was also included in the analysis. The results showed different prediction accuracies depending on the different intake air pressure and statistically biased test conditions compared to the training conditions. |
| 원문(PDF) |
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