Prediction Modeling and Analysis of Knocking Combustion using an Improved 0D RGF Model and Supervised Deep Learning

Seokwon Cho, Jihwan Park, Chiheon Song, Sechul Oh, Sangyul Lee, Minjae Kim and Kyoungdoug Min
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Seokwon Cho: Department of Mechanical and Aerospace Engineering, Seoul National University, Seoul 08826, Korea
Jihwan Park: Department of Mechanical and Aerospace Engineering, Seoul National University, Seoul 08826, Korea
Chiheon Song: Department of Mechanical and Aerospace Engineering, Seoul National University, Seoul 08826, Korea
Sechul Oh: Department of Mechanical and Aerospace Engineering, Seoul National University, Seoul 08826, Korea
Sangyul Lee: Department of Robotics and Automation Engineering, Hoseo University; 31702, Korea
Minjae Kim: Department of Mechanical Engineering, Myongji University; Yongin 17058, Korea
Kyoungdoug Min: Department of Mechanical and Aerospace Engineering, Seoul National University, Seoul 08826, Korea

Energies, 2019, vol. 12, issue 5, 1-25

Abstract: 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.

Keywords: SI engine; knock; knock prediction; ignition delay; residual gas; knock onset; deep learning; 1D simulation; variable valve timing (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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