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基于超分辨率重构方法的湍流流场重构

江昊 王伯福 庄启亮 卢志明

江昊,王伯福,庄启亮,等. 基于超分辨率重构方法的湍流流场重构[J]. 实验流体力学,2022,36(3):1-8 doi: 10.11729/syltlx20210185
引用本文: 江昊,王伯福,庄启亮,等. 基于超分辨率重构方法的湍流流场重构[J]. 实验流体力学,2022,36(3):1-8 doi: 10.11729/syltlx20210185
JIANG H,WANG B F,WANG Q L,et al. Reconstruction of turbulent fields based on super-resolution reconstruction method[J]. Journal of Experiments in Fluid Mechanics, 2022,36(3):1-8. doi: 10.11729/syltlx20210185
Citation: JIANG H,WANG B F,WANG Q L,et al. Reconstruction of turbulent fields based on super-resolution reconstruction method[J]. Journal of Experiments in Fluid Mechanics, 2022,36(3):1-8. doi: 10.11729/syltlx20210185

基于超分辨率重构方法的湍流流场重构

doi: 10.11729/syltlx20210185
基金项目: 国家自然科学基金(12072185,11732010,11972220)
详细信息
    作者简介:

    江昊:(1997—),男,安徽黄山人,博士研究生。研究方向:机器学习在湍流中的应用,多相流。通信地址:上海市静安区延长路149号上海大学延长校区力学与工程科学学院上海市应用数学和力学研究所(200072)。E-mail:jiangH@shu.edu.cn

    通讯作者:

    E-mail:王伯福,bofuwang@shu.edu.cn

    庄启亮,klchong@shu.edu.cn

  • 中图分类号: O357

Reconstruction of turbulent fields based on super-resolution reconstruction method

  • 摘要: 从低分辨率流场数据中获取精细流场信息具有重要的研究意义。基于卷积神经网络的超分辨率重构方法是近年来发展的一种较为有效的精细流场重构方法。本文采用高效亚像素卷积神经网络(Efficient Sub-Pixel Convolutional Neural Network,ESPCN),对Rayleigh-Bénard(RB)对流的数值模拟数据和湍流边界层(Turbulent Boundary Layer,TBL)的实验测量数据进行了超分辨率重构,并与双三次插值方法(Bicubic Interpolation)的重构结果进行对比。对比结果表明:在较小的下采样比下,ESPCN方法和Bicubic方法的重构精度相当;在较大的下采样比下,ESPCN方法的重构精度明显优于Bicubic方法。此外,ESPCN方法对数据梯度较大区域的超分辨率重构效果优于Bicubic方法。
  • 图  1  ESPCN模型示意图

    Figure  1.  Sketch of ESPCN

    图  2  下采样比为3的RB系统温度场数据的超分辨率重构结果

    Figure  2.  The super-resolution reconstruction result of the temperature field data of the RB system with rus=3

    图  4  RB系统中心线温度数据剖面的超分辨率重构结果

    Figure  4.  Comparison of the results of the temperature profile of the horizontal centerline of the RB system temperature field data

    图  5  RB系统瞬时温度场数据PDF分布的超分辨率重构结果

    Figure  5.  Comparison of PDF results of instantaneous temperature field data of the RB system temperature field data

    图  3  下采样比为30的RB系统温度场数据的超分辨率重构结果

    Figure  3.  The super-resolution reconstruction result of the temperature field data of the RB system with rus=30

    图  6  湍流边界层实验装置图[30]

    Figure  6.  Sketch of turbulent boundary layer experimental device[30]

    图  7  下采样比为4的TBL流向速度数据的超分辨率重构结果

    Figure  7.  The super-resolution reconstruction result of the TBL streamwise velocity with the down-sampling rus =4

    图  8  下采样比为8的TBL流向速度数据的超分辨率重构结果

    Figure  8.  The super-resolution reconstruction result of the TBL streamwise velocity with the down-sampling rus =8

    图  9  TBL流向速度数据的PDF分布结果对比

    Figure  9.  Comparison of PDF results of TBL streamwise velocity

    表  1  不同下采样比的MSE

    Table  1.   MSE with different down-sampling rus

    rus =3rus =10rus =20rus =30
    ESPCN4.02×10–64.58×10–62.41×10–53.08×10–5
    Bicubic6.97×10–61.95×10–47.11×10–41.39×10–3
    下载: 导出CSV

    表  2  不同下采样比的MSE

    Table  2.   MSE with different down-sampling rus

    rus =2rus =4rus =8
    ESPCN1.63×10–44.32×10–47.00×10–4
    Bicubic1.60×10–47.16×10–43.62×10–3
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-11-29
  • 录用日期:  2022-03-08
  • 修回日期:  2022-03-04
  • 网络出版日期:  2022-04-21

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