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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Seokwon Cho & Jihwan Park & Chiheon Song & Sechul Oh & Sangyul Lee & Minjae Kim & Kyoungdoug Min, 2019. "Prediction Modeling and Analysis of Knocking Combustion using an Improved 0D RGF Model and Supervised Deep Learning," Energies, MDPI, vol. 12(5), pages 1-25, March.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:5:p:844-:d:210872
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    References listed on IDEAS

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    1. Zhen, Xudong & Wang, Yang & Xu, Shuaiqing & Zhu, Yongsheng & Tao, Chengjun & Xu, Tao & Song, Mingzhi, 2012. "The engine knock analysis – An overview," Applied Energy, Elsevier, vol. 92(C), pages 628-636.
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    Cited by:

    1. Zhu, Sipeng & Akehurst, Sam & Lewis, Andrew & Yuan, Hao, 2022. "A review of the pre-chamber ignition system applied on future low-carbon spark ignition engines," Renewable and Sustainable Energy Reviews, Elsevier, vol. 154(C).
    2. Denghao Zhu & Jun Deng & Jinqiu Wang & Shuo Wang & Hongyu Zhang & Jakob Andert & Liguang Li, 2020. "Development and Application of Ion Current/Cylinder Pressure Cooperative Combustion Diagnosis and Control System," Energies, MDPI, vol. 13(21), pages 1-21, October.
    3. Achilles Kefalas & Andreas B. Ofner & Gerhard Pirker & Stefan Posch & Bernhard C. Geiger & Andreas Wimmer, 2021. "Detection of Knocking Combustion Using the Continuous Wavelet Transformation and a Convolutional Neural Network," Energies, MDPI, vol. 14(2), pages 1-19, January.
    4. Diego Perrone & Angelo Algieri & Pietropaolo Morrone & Teresa Castiglione, 2021. "Energy and Economic Investigation of a Biodiesel-Fired Engine for Micro-Scale Cogeneration," Energies, MDPI, vol. 14(2), pages 1-28, January.
    5. Haruki Tajima & Takuya Tomidokoro & Takeshi Yokomori, 2022. "Deep Learning for Knock Occurrence Prediction in SI Engines," Energies, MDPI, vol. 15(24), pages 1-14, December.

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