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基于尾流时程目标识别的流场参数选择研究

战庆亮 葛耀君 白春锦

战庆亮, 葛耀君, 白春锦. 基于尾流时程目标识别的流场参数选择研究. 力学学报, 2021, 53(10): 2692-2702 doi: 10.6052/0459-1879-21-332
引用本文: 战庆亮, 葛耀君, 白春锦. 基于尾流时程目标识别的流场参数选择研究. 力学学报, 2021, 53(10): 2692-2702 doi: 10.6052/0459-1879-21-332
Zhan Qingliang, Ge Yaojun, Bai Chunjin. Study on flow field parameters of wake time history target recognition. Chinese Journal of Theoretical and Applied Mechanics, 2021, 53(10): 2692-2702 doi: 10.6052/0459-1879-21-332
Citation: Zhan Qingliang, Ge Yaojun, Bai Chunjin. Study on flow field parameters of wake time history target recognition. Chinese Journal of Theoretical and Applied Mechanics, 2021, 53(10): 2692-2702 doi: 10.6052/0459-1879-21-332

基于尾流时程目标识别的流场参数选择研究

doi: 10.6052/0459-1879-21-332
基金项目: 国家自然科学基金(51778495, 51978527), 辽宁省教育厅研究计划(984210012-LJKZ0052)和交通行业重点实验室课题(KLWRTBMC21-02)资助项目
详细信息
    作者简介:

    战庆亮, 博士, 主要研究方向: 计算流体力学、深度学习. E-mail: zhanqingliang@163.com

  • 中图分类号: O375

STUDY ON FLOW FIELD PARAMETERS OF WAKE TIME HISTORY TARGET RECOGNITION

  • 摘要: 浸入流场中的固体壁面会形成高度复杂且具有一定特征的尾流流场, 利用尾流所包含的信息对物体的外形特征进行识别具有重要的应用价值. 然而, 在较高雷诺数情况下尾流流场形态及其时序特征复杂, 难以通过传统的数学物理方法对流场信号进行特征的识别与提取. 本文提出了基于尾流时程数据深度学习的流场特征提取与分析方法, 实现了基于一点的物理量时程进行流场中物体外形的识别; 同时, 对流场中不同物理参数时程的识别精度与识别结果进行分析与研究, 得到适用于目标识别的最优物理量参数. 通过对圆柱和方柱的尾流数据研究结果表明, 本文提出的基于卷积神经网络的模型具有好的训练收敛性和高的预测精度, 能够识别并提取得到时程数据中包含的流场特征, 采用流场横向速度时程作为物体外形识别信号的模型准确率高. 证明了本方法用于浸入流场中物体外形识别的可行性, 是一种目标识别的高精度方法.

     

  • 图  1  全连接网络(MLP)结构示意图

    Figure  1.  Structure of multi perceptron layer neural network

    图  2  整体计算域

    Figure  2.  Computational domain

    图  3  棱柱附近的网格划分

    Figure  3.  Mesh in the vicinity of prisms

    图  4  尾流监测点位置示意图

    Figure  4.  Location of wake monitoring points

    图  5  典型测点的流场参数时程

    Figure  5.  Time history of flow field parameters at typical measuring points

    图  6  FCN结构示意图

    Figure  6.  Structure of fully convolutional neural network

    图  7  不同参数识别精度比较

    Figure  7.  Loss and accuracy curve of two models

    图  8  最优模型的结果汇总

    Figure  8.  Summary of best model of different cases

    图  9  不同流场位置的预测结果分布

    Figure  9.  Distribution of predicted results at different position

    表  1  样本与标签设置

    Table  1.   Sample and label settings

    LabelShapeNumber of parametersNumber of samples
    0 circular cylinder 6 21600
    1 square cylinder 6 21600
    下载: 导出CSV

    表  2  完全卷积神经网络模型参数

    Table  2.   Time convolution neural network model parameters

    LayerKernel numberSize of kernelActivation functionOutput size
    input------300
    convolution1288Relu300, 128
    convolution2565Relu300, 256
    convolution1283Relu300, 128
    pooling------128
    FCL------6
    下载: 导出CSV

    表  3  全连接网络模型参数

    Table  3.   Time convolution neural network model parameters

    LayerDropout parameterNumber of neuralTrainable parameter
    input0
    FCL 10.1500150500
    FCL 20.2500250500
    FCL 30.3500250500
    output0.321002
    下载: 导出CSV

    表  4  算例设置

    Table  4.   Parameters of cases

    CaseModelParameterstraining datasettesting dataset
    F1FCNPressure P43252875
    M1MLPPressure P43112889
    F2FCNU-velocity43162884
    M2MLPU-velocity43282872
    F3FCNV-velocity43202880
    M3MLPV-velocity43212879
    F4FCNW-velocity43262874
    M4MLPW-velocity43082892
    F5FCNV-Magnitude43232877
    M5MLPV-Magnitude43282872
    F6FCNVorticity43072893
    M6MLPVorticity43172883
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-07-12
  • 录用日期:  2021-09-28
  • 网络出版日期:  2021-09-29

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