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<p>The turbulent combustion characteristics within the combustion chamber of rocket engines and other propulsion systems are crucial to engine performance. In recent years, advancements in deep learning techniques have significantly enriched the study methods for turbulent combustion by balancing research efficiency and data accuracy, resulting in numerous research achievements. Reconstruction of the characteristic parameters of a turbulent combustion flame is an important research direction. The high-precision data obtained through such reconstruction are valuable for real-time monitoring of combustor conditions, as well as for predicting and optimizing combustion performance. This paper reviews recent applications of deep learning in reconstructing flame structures in turbulent combustion, discusses current challenges, and provides perspectives for future developments.</p>

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Review on the Application of Deep Learning in Reconstruction of Turbulent Flame

  • 期刊论文

  • 发表日期：

    2025年5月5日

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  • Weiyi Zhu,
  • Chuang Zhou,
  • Yang Zhang,
  • Bo Zhang,
  • Haotian Qi,
  • Xiao Zhu

摘要

The turbulent combustion characteristics within the combustion chamber of rocket engines and other propulsion systems are crucial to engine performance. In recent years, advancements in deep learning techniques have significantly enriched the study methods for turbulent combustion by balancing research efficiency and data accuracy, resulting in numerous research achievements. Reconstruction of the characteristic parameters of a turbulent combustion flame is an important research direction. The high-precision data obtained through such reconstruction are valuable for real-time monitoring of combustor conditions, as well as for predicting and optimizing combustion performance. This paper reviews recent applications of deep learning in reconstructing flame structures in turbulent combustion, discusses current challenges, and provides perspectives for future developments.