Due to the significant improvement in computing power and the rapid advancement of data processing technologies, artificial intelligence (AI) has introduced new tools and methodologies to address the challenges posed by high nonlinearity and strong coupling characteristics in traditional supersonic flow and combustion. This article reviews the considerable progress AI has made in applications within the fields of supersonic flow and combustion, covering three main aspects: intelligent turbulence combustion simulation, supersonic flow field intelligent reconstruction based on deep learning, and the intelligent design of the full-flow passage of supersonic engines. In recent years, the field of turbulent combustion has seen the utilization of large volume of data combined with implementation of advanced machine learning models, enabling accurate predictions of combustion efficiency and optimization of the combustion process. Flow field intelligent reconstruction employs deep learning networks to accurately reconstruct the detailed information of the entire flow field from limited observational data, enhancing the capacity to analyze and predict supersonic flows. The intelligent design of the full-flow passage of supersonic engines has led to efficient design and optimization of complex flow systems through the integration of advanced optimization algorithms and AI technology. These advancements have driven the development of supersonic flow and combustion theories and provided innovative solutions for related engineering applications. Finally, the challenges and future applications of machine learning in combustion research are discussed.

中文翻译：

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人工智能在超音速流动与燃烧领域的发展与应用进展与展望


由于计算能力的显著提高和数据处理技术的快速发展，人工智能 （AI） 引入了新的工具和方法，以应对传统超音速流动和燃烧中高非线性和强耦合特性带来的挑战。本文综述了人工智能在超音速流与燃烧领域内应用方面取得的长足进展，涵盖智能湍流燃烧模拟、基于深度学习的超音速流场智能重构、超声速发动机全流道智能设计三个主要方面。近年来，湍流燃烧领域利用大量数据并实施先进的机器学习模型，能够准确预测燃烧效率并优化燃烧过程。流场智能重构采用深度学习网络，从有限的观测数据中准确重建整个流场的详细信息，增强超音速流的分析和预测能力。超音速发动机全流道的智能设计通过集成先进的优化算法和 AI 技术，实现了复杂流道的高效设计和优化。这些进步推动了超音速流动和燃烧理论的发展，并为相关工程应用提供了创新的解决方案。最后，讨论了机器学习在燃烧研究中面临的挑战和未来应用。