http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
- 中文 을 입력하시려면 zhongwen을 입력하시고 space를누르시면됩니다.
- 北京 을 입력하시려면 beijing을 입력하시고 space를 누르시면 됩니다.
RISS 인기검색어
- 검색키워드
- 저자 : Rio Asakawa (검색결과 1 건)
- 무료
- 기관 내 무료
- 유료
-
MACHINE LEARNING APPLICATION TO PREDICT COMBUSTION PHASE OF A DIRECT INJECTION SPARK IGNITION ENGINE
Rio Asakawa,Keisuke Yokota,Iku Tanabe,Kyohei Yamaguchi,Ratnak Sok,Hiroyuki Ishii,Jin Kusaka 한국자동차공학회 2022 International journal of automotive technology Vol.23 No.1
Lean-diluted combustion can enhance thermal efficiency and reduce exhaust gas emissions from spark-ignited (SI) gasoline engines. However, excessive lean mixture with external dilution leads to combustion instability due to high cycle-to-cycle variations (CCV). The CCV should be controlled as low as possible to achieve stable combustion, high engine performance, and low emissions. Therefore, a stable combustion control function is required to predict the combustion phase with a low calculation load. A machine learning-based function is developed in this work to predict the 50 % mass fraction burn location (MFB50). Input parameters to the machine learning model consist of 1-, 2-, 3-, and 4-cycle from a three-cylinder production-based gasoline engine operated under stoichiometric to the lean-burn mixture. The results show that the MFB50 prediction model achieves high accuracy when 2-cycle data are used relative to 1-cycle data, which implies that the previous cycle data affects the predicted MFB50 of the next cycle. As a result, the neural network model can predict the cyclic MFB50 error within ± 3 oCA CCV and ± 5 oCA CCV with 70 % and 90 % accuracy, respectively. However, an increasing number of cycle data worsens the prediction accuracy due to model over-learning.
연관 검색어 추천
이 검색어로 많이 본 자료
활용도 높은 자료
