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超燃冲压发动机仿真: 从数值飞行到数智飞行

孙明波 安彬 汪洪波 王成龙

孙明波, 安彬, 汪洪波, 王成龙. 超燃冲压发动机仿真: 从数值飞行到数智飞行. 力学学报, 2022, 54(3): 588-600 doi: 10.6052/0459-1879-21-397
引用本文: 孙明波, 安彬, 汪洪波, 王成龙. 超燃冲压发动机仿真: 从数值飞行到数智飞行. 力学学报, 2022, 54(3): 588-600 doi: 10.6052/0459-1879-21-397
Sun Mingbo, An Bin, Wang Hongbo, Wang Chenglong. Numerical simulation of the scramjet engine: from numerical flight to intelligent numerical flight. Chinese Journal of Theoretical and Applied Mechanics, 2022, 54(3): 588-600 doi: 10.6052/0459-1879-21-397
Citation: Sun Mingbo, An Bin, Wang Hongbo, Wang Chenglong. Numerical simulation of the scramjet engine: from numerical flight to intelligent numerical flight. Chinese Journal of Theoretical and Applied Mechanics, 2022, 54(3): 588-600 doi: 10.6052/0459-1879-21-397

超燃冲压发动机仿真: 从数值飞行到数智飞行

doi: 10.6052/0459-1879-21-397
基金项目: 国家自然科学基金资助项目(11925207)
详细信息
    作者简介:

    孙明波, 教授, 主要研究方向: 高超声速推进技术. E-mail: sunmingbo@nudt.edu.cn

  • 中图分类号: V211.3

NUMERICAL SIMULATION OF THE SCRAMJET ENGINE: FROM NUMERICAL FLIGHT TO INTELLIGENT NUMERICAL FLIGHT

  • 摘要: 数值计算方法、物理模型和计算硬件的进步极大地促进了超燃冲压发动机仿真的发展, 基于内外流一体化仿真的数值飞行技术已日渐成熟并逐步应用于工程实践, 伴随燃烧、气动、结构、材料以及传热多物理场耦合模型和计算方法的发展, 叠加多场计算的广义数值飞行技术有望近期得到突破. 目前人工智能技术的快速发展, 将赋能于数值飞行技术, “数智飞行”这一新的研究模式应运而生. 一方面, 数智飞行将利用人工智能突破传统数值飞行技术在网格生成与自适应、高保真物理模型、数据处理与知识挖掘等方面的发展瓶颈, 全面提升数值飞行的精度、准度和效能; 另一方面, 数智飞行将突破传统发动机研发模式, 通过构建智能化发动机数字孪生体, 实现发动机在虚拟空间中的全弹道飞行考核, 加快发动机设计迭代. 此外, 数字孪生体在试验中可与实体发动机同步运行, 根据感知数据快速预测多物理场, 实现对实体发动机工作状态的实时评估. 为促进数智飞行技术的发展, 未来需要重点针对数据驱动与物理约束的有机结合、智能化多物理场联合仿真平台、发动机数字孪生体构建等方面开展研究.

     

  • 图  1  超燃冲压发动机示意图[12]

    Figure  1.  Scramjet engine concept[12]

    图  2  超燃冲压发动机内外流一体化仿真典型结果

    Figure  2.  Representative numerical results of internal and external coupling flow

    图  3  两种内外流一体化仿真平台

    Figure  3.  Two software platforms for numerical simulation of internal and external coupling flow

    图  4  超燃冲压发动机中的多物理场耦合

    Figure  4.  Multi-physics coupling in a scramjet engine

    图  5  TX-V结构响应与寿命预测[30]

    Figure  5.  Structural response and life prediction of TX-V[30]

    图  6  基于卷积神经网络的网格分类器[36]

    Figure  6.  A mesh classifier based on convolutional neural network[36]

    图  7  基于神经网络的流场快速预测[42]

    Figure  7.  Fast flow field prediction based on neural network[42]

    图  8  构建数据驱动湍流模型的流程图[47]

    Figure  8.  Flow chart for building a data-driven turbulence model[47]

    图  9  Shock-Net和Tecplot软件识别的激波[60]

    Figure  9.  Shock waves detected by Shock-Net and Tecplot[60]

    图  10  基于本征正交分解和长短时记忆神经网络的混合模型降阶方法[64]

    Figure  10.  A hybrid model order reduction method based on proper orthogonal decomposition and long short-term memory neural network[64]

    图  11  航空发动机的数字孪生体

    Figure  11.  Digital twin of an aero engine

    图  12  超燃冲压发动机的数字孪生体

    Figure  12.  Digital twin of a scramjet engine

    图  13  数字孪生体在超燃冲压发动机设计中的应用

    Figure  13.  Application of digital twin in the field of scramjet engine design

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出版历程
  • 收稿日期:  2021-08-15
  • 录用日期:  2022-02-13
  • 网络出版日期:  2022-02-14
  • 刊出日期:  2022-03-18

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