| 제목 |
진동신호를 이용한 CNG-디젤 이종연료 엔진 연소압력 예측 DNN 모델에서
천연가스 대체율 및 디젤 분사시기가 예측 정확도에 미치는 영향 |
| 분야 |
엔진 및 연소 |
| 언어 |
Korean |
| 저자 |
김경곤(한국과학기술원), 박찬수(한국과학기술원), 김우영(한국과학기술원), 전지연(한국조선해양), 전미연(한국조선해양), 배충식(한국과학기술원) |
| Key Words |
Deep neural network(심층 신경망), Block vibration(블록 진동), Dual fuel(이종 연료), In-cylinder pressure(실린더 내부압력), Injection timing(분사시기), Substitution ratio(대체율) |
| 초록 |
In-cylinder pressure of a single-cylinder dual-fuel engine using compressed natural gas(CNG) and diesel was predicted from engine block vibration. A supervised learning model was built using a deep neural network(DNN). Vibration and in-cylinder pressure signals measured by the accelerometer and piezoelectric pressure sensor were used as input and output labels. A parametric study was conducted in order to check the effects of the composition of train conditions on the accuracy of the model. CNG substitution ratio and diesel injection timing were selected as main parameters. Train conditions were divided by combustion modes, which are the pilot-dual fuel(pilot-DF) and reactivity-controlled compression ignition(RCCI), in order to study the effects of parameters on the accuracy of the model. The results showed that the prediction accuracy of the model is related to the amount of train data which have similar trends of in-cylinder pressure with the test conditions, regardless of the total amount of train data. |
| 원문(PDF) |
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