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卷积神经网络在流场重构研究中的进展

陈皓 郭明明 田野 陈尔达 邓雪 乐嘉陵 李林静

陈皓, 郭明明, 田野, 陈尔达, 邓雪, 乐嘉陵, 李林静. 卷积神经网络在流场重构研究中的进展. 力学学报, 2022, 54(9): 2343-2360 doi: 10.6052/0459-1879-22-130
引用本文: 陈皓, 郭明明, 田野, 陈尔达, 邓雪, 乐嘉陵, 李林静. 卷积神经网络在流场重构研究中的进展. 力学学报, 2022, 54(9): 2343-2360 doi: 10.6052/0459-1879-22-130
Chen Hao, Guo Mingming, Tian Ye, Chen Erda, Deng Xue, Le Jialing, Li Linjing. Progress of convolution neural networks in flow field reconstruction. Chinese Journal of Theoretical and Applied Mechanics, 2022, 54(9): 2343-2360 doi: 10.6052/0459-1879-22-130
Citation: Chen Hao, Guo Mingming, Tian Ye, Chen Erda, Deng Xue, Le Jialing, Li Linjing. Progress of convolution neural networks in flow field reconstruction. Chinese Journal of Theoretical and Applied Mechanics, 2022, 54(9): 2343-2360 doi: 10.6052/0459-1879-22-130

卷积神经网络在流场重构研究中的进展

doi: 10.6052/0459-1879-22-130
基金项目: 国家自然科学基金(5106237)和中国科协青年人才托举项目(QT-026)资助
详细信息
    作者简介:

    田野, 副研究员, 主要研究方向: 超燃冲压发动机燃烧组织技术. E-mail: tianye_cardc@163.com

  • 中图分类号: V211.3, O351.2

PROGRESS OF CONVOLUTION NEURAL NETWORKS IN FLOW FIELD RECONSTRUCTION

  • 摘要: 近年来, 随着深度学习在图像处理、语音识别、自动驾驶、自然语言处理等领域迅速发展, 该技术也被越来越广泛地应用于处理具有复杂非线性、高维度、大数据量等特点的流体力学方向. 传统的方法无法有效地处理这些庞大的数据, 深度学习因其具有强大的函数拟合能力, 可以从大量的数据中挖掘有用的信息. 当前, 流体力学深度学习技术有了初步的一些研究成果, 在流动信息特征提取、多源数据信息融合及流场的智能重构等方面具有重要的工程价值, 其应用潜力逐渐得到证实. 如何利用地面风洞试验、数值模拟及飞行试验获取的数据进行深入挖掘, 快速智能感知及重构流场, 可为主动流动控制提供重要指导. 本文主要从深度学习不同类型的网络结构出发探讨了卷积神经网络在流场重构中的研究进展, 文章首先介绍卷积神经网络的一些基本概念以及基本网络结构, 之后简要介绍流场超分辨率重构网络、端到端的映射网络、长短期记忆网络的基本结构与理论, 并详细归纳出他们的改进形式在流场重构领域的一系列研究进展与成果, 最后对文章做出总结并探讨了流场重构深度学习技术所面临的挑战与展望.

     

  • 图  1  卷积

    Figure  1.  Convolution

    图  2  最大池化

    Figure  2.  Maxpooling

    图  3  全连接层

    Figure  3.  Full connection layer

    图  4  残差结构

    Figure  4.  Residual structure

    图  5  CBAM结构

    Figure  5.  CBAM structure

    图  6  SRCNN结构

    Figure  6.  SRCNN structure

    图  7  基于最显着特征的现有单图像超分辨率技术的分类

    Figure  7.  Classification of existing single image super-resolution technology based on the most salient features

    图  8  几种超分辨率重建方法的对比

    Figure  8.  Comparison of several super-resolution reconstruction methods

    图  9  Bicubic, SRCNN, MPSRC重建结果的对比

    Figure  9.  Comparison of reconstruction results of Bicubic, SRCNN and MPSRC

    图  10  多路径的模型架构

    Figure  10.  Multipath model architecture

    图  11  模型重建和实验结果之间瞬时流场的比较: (a)楔角为0°, (b)楔角为14°, (c)楔角为20°

    Figure  11.  Comparison of instantaneous flow field between model reconstruction and experimental results: (a) wedge angle of 0°, (b) wedge angle of 14° and (c) wedge angle of 20°

    图  12  四种方法的重建结果

    Figure  12.  Reconstruction results of four methods

    图  13  生成对抗网络结构

    Figure  13.  Generate countermeasure network structure

    图  14  速度场预测模型的CNN网络架构

    Figure  14.  CNN network architecture of velocity field prediction model

    图  15  超声速燃烧室流场重构MBFCNN模型架构

    Figure  15.  MBFCNN model architecture for flow field reconstruction in supersonic combustor

    16  不同雷诺数的CFD结果与模型预测值之间的瞬时流量比较

    16.  Comparison of instantaneous flow between CFD results of different Reynolds numbers and model predicted values

    16  不同雷诺数的CFD结果与模型预测值之间的瞬时流量比较(续)

    16.  Comparison of instantaneous flow between CFD results of different Reynolds numbers and model predicted values (continued)

    图  17  LSTM结构

    Figure  17.  LSTM structure

    图  18  通过上游压力场重建超声速叶栅下游压力场的ConvLSTM结构

    Figure  18.  Reconstruction of convlstm structure of downstream pressure field of supersonic cascade through upstream pressure field

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出版历程
  • 收稿日期:  2022-03-29
  • 录用日期:  2022-05-11
  • 网络出版日期:  2022-05-12
  • 刊出日期:  2022-09-18

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