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基于深度神经网络的流场时空重构方法

韩仁坤 刘子扬 钱炜祺 王文正 陈刚

韩仁坤,刘子扬,钱炜祺,等. 基于深度神经网络的流场时空重构方法[J]. 实验流体力学,2022,36(3):118-126 doi: 10.11729/syltlx20210124
引用本文: 韩仁坤,刘子扬,钱炜祺,等. 基于深度神经网络的流场时空重构方法[J]. 实验流体力学,2022,36(3):118-126 doi: 10.11729/syltlx20210124
HAN R K,LIU Z Y,QIAN W Q,et al. Spatio-temporal reconstruction method of flow field based on deep neural network[J]. Journal of Experiments in Fluid Mechanics, 2022,36(3):118-126. doi: 10.11729/syltlx20210124
Citation: HAN R K,LIU Z Y,QIAN W Q,et al. Spatio-temporal reconstruction method of flow field based on deep neural network[J]. Journal of Experiments in Fluid Mechanics, 2022,36(3):118-126. doi: 10.11729/syltlx20210124

基于深度神经网络的流场时空重构方法

doi: 10.11729/syltlx20210124
详细信息
    作者简介:

    韩仁坤:(1995—),男,河南商丘人,博士研究生。研究方向:非线性流固耦合系统深度学习降阶模型。通信地址:陕西省西安市咸宁西路28号西安交通大学兴庆校区航天航空学院(710049)。E-mail:hanrenkun@stu.xjtu.edu.cn

    通讯作者:

    E-mail: aachengang@mail.xjtu.edu.cn

  • 中图分类号: V211.79

Spatio-temporal reconstruction method of flow field based on deep neural network

  • 摘要: 针对流场粒子图像测速实验中时间和空间高分辨率测量代价高的问题,研究了数据驱动的流场时空重构方法。为了对实验测得的低分辨率数据进行时空高分辨率重构,提出了一种基于深度神经网络的流场时空重构方法,并构建了一种基于卷积神经网络和长短时记忆神经网络的混合深度神经网络。该混合深度神经网络能够学习流场的时空演化特征,训练完成后可实现对实验数据的时空高分辨率重构。测试结果表明:只进行流场空间高分辨率重构时,重构出的流场与真实流场之间的均方根误差为0.0065左右,流场数据点数是原来的51倍;同时进行流场时间和空间高分辨率重构时,重构出的流场与真实流场之间的均方根误差可保持在0.065左右,流场时间维度的密度是原来的5倍,可极大提高实验效率,节约实验成本。
  • 图  1  空间重构深度神经网络结构示意图

    Figure  1.  Structure of deep neural network for spatial reconstruction

    图  2  长短时记忆神经网络的神经元结构图

    Figure  2.  Sketch of neuron structure in LSTM

    图  3  流场时空重构数据流动示意图

    Figure  3.  Sketch of data flow in flow field spatio-temporal reconstruction method

    图  4  深度神经网络结构示意图

    Figure  4.  Sketch of deep neural network structure

    图  5  结构化网格

    Figure  5.  Structured grid

    图  6  空间高分辨率流场重构结果与数值模拟结果对比

    Figure  6.  Comparison of spatial high-resolution reconstruction results and numerical simulation results

    图  7  流场重构值与真实值对比散点图

    Figure  7.  Scatter of comparison between reconstructed flow field value and ground truth flow field value

    图  8  无校正时不同时间步流场重构结果与数值模拟结果对比

    Figure  8.  Comparison of flow field reconstruction results without correction and numerical simulation results at different time steps

    图  9  不同校正方法持续预测结果均方根误差对比

    Figure  9.  Comparison of the RMSE between different correction methods

    图  10  使用混合数据校正时不同时间步流场重构结果与数值模拟结果对比

    Figure  10.  Comparison of flow field reconstruction results corrected with proportional mixing data and numerical simulation results at different time steps

    图  11  特定位置流场变量演化对比

    Figure  11.  Comparison of evolution of flow field variables at specific locations

    表  1  深度神经网络结构参数

    Table  1.   Details of the structure parameters in the hybrid deep neural network

    层名卷积核尺寸/滑动步长输出尺寸
    Conv 13×3/13×14×14×3
    Conv 23×3/13×14×14×8
    Conv 33×3/13×14×14×16
    Conv 43×3/23×7×7×32
    Conv 53×3/13×7×7×64
    Conv 63×3/13×7×7×64
    LSTM3×3/11×7×7×64
    DeConv 13×3/11×7×7×64
    DeConv 23×3/21×13×13×64
    DeConv 33×3/11×13×13×32
    DeConv 43×3/21×25×25×32
    DeConv 53×3/11×25×25×16
    DeConv 63×3/21×50×50×16
    DeConv 73×3/11×50×50×8
    DeConv 83×3/21×100×100×8
    DeConv 93×3/11×100×100×3
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-11-10
  • 修回日期:  2022-01-12
  • 录用日期:  2022-03-18
  • 网络出版日期:  2022-07-12
  • 刊出日期:  2022-07-04

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