Energies | |
Prediction Modeling and Analysis of Knocking Combustion using an Improved 0D RGF Model and Supervised Deep Learning | |
Minjae Kim1  Jihwan Park2  Sechul Oh2  Seokwon Cho2  Chiheon Song2  Kyoungdoug Min2  Sangyul Lee3  | |
[1] Department of Mechanical Engineering, Myongji University, Yongin 17058, Korea;Department of Mechanical and Aerospace Engineering, Seoul National University, Seoul 08826, Korea;Department of Robotics and Automation Engineering, Hoseo University, 31702, Korea; | |
关键词: SI engine; knock; knock prediction; ignition delay; residual gas; knock onset; deep learning; 1D simulation; variable valve timing; | |
DOI : 10.3390/en12050844 | |
来源: DOAJ |
【 摘 要 】
The knock phenomenon is one of the major hindrances for enhancing the thermal efficiency in spark-ignited engines. Due to the stochastic behavior of knocking combustion, analytical cycle studies are required. However, there are many problems to be addressed with regard to the individual cycle analysis of in-cylinder pressure data. This study thus proposes novel, comprehensive and efficient methodologies for evaluating the knocking combustion in the internal combustion engine. The proposed methodologies include a filtering method for the in-cylinder pressure, the determination of the knock onset, and the calculation of the residual gas fraction. Consequently, a smart knock onset model with high accuracy could be developed using a supervised deep learning that was not available in the past. Moreover, an improved zero-dimensional (0D) estimation model for the residual gas fraction was developed to obtain better accuracy for closed system analysis. Finally, based on a cyclic analysis, a knock prediction model is suggested; the model uses 0D ignition delay correlation under various experimental conditions including aggressive cam phase shifting by a dual variable valve timing (VVT) system. Using the proposed analysis method, insight into stochastic knocking combustion can be obtained, and a faster combustion speed can lead to a higher knock intensity in a steady-state operation.
【 授权许可】
Unknown