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基于深度学习的流场时程特征提取模型

战庆亮 葛耀君 白春锦

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基于深度学习的流场时程特征提取模型

战庆亮, 葛耀君, 白春锦

Flow feature extraction models based on deep learning

Zhan Qing-Liang, Ge Yao-Jun, Bai Chun-Jin
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  • 特征识别是流体力学的重要研究方向, 然而在中高雷诺数情况下物体的尾流流场复杂, 难以通过传统方法实现特征的提取与识别. 深度学习理论与技术的不断发展为复杂流场特征的识别提供了新方法. 基于流场时程数据的深度学习模型, 本文研究了4种模型对尾流场特征提取与识别的精度, 得到了针对流场时程特征提取的高精度新方法. 结果表明: 所提出的模型能够识别尾流物理时程的不同特征, 并通过流场时程实现了目标的外形识别, 验证了方法的可行性; 同时结果表明基于卷积运算的深度学习模型精度高, 适用于流场时程数据的特征分析; 深度学习网络结构更深、层间结构复杂的残差卷积网络识别精度最高, 是尾流时程分析的高精度算法. 本文所提方法从流场物理量时程的角度对流场特征进行了提取与识别, 证明了深度学习方法具有较高的识别精度, 是研究流场特征的重要途径.
    Extraction and recognition of the features of flow field is an important research area of fluid mechanics. However, the wake flow field of object immersed in fluid is complicated in the case of medium- and high-Reynolds number, thus it is difficult to extract and recognize the key features by using traditional physical models and mathematical methods. The continuous development of deep learning theory provides us with a new method of recognizing the complex flow features. A new method of extracting the features of the flow time history is proposed based on deep learning in this work. The accuracy of four deep learning model for feature recognition is studied. The results show that the proposed model can identify different characteristics of the wake time history and object shapes accurately. Some conclusions can be obtained below (i) The model based on convolutional layers has higher accuracy and is suitable for analyzing the features of flow time history data. (ii) The residual convolutional network, with a deeper structure and more complex inter-layer structure, has highest accuracy for feature recognition. (iii) The proposed method can extract and recognize the flow features from the perspective of physical quantities time history, which is a high-accuracy method, and it is an important new way to study the features of flow physical quantities.
      通信作者: 战庆亮, zhanqingliang@163.com
    • 基金项目: 国家自然科学基金(批准号: 51778495, 51978527)、桥梁结构抗风技术交通行业重点实验室(上海)开放课题(批准号: KLWRTBMC21-02)和辽宁教育厅研究计划(批准号: LJKZ0052)资助的课题
      Corresponding author: Zhan Qing-Liang, zhanqingliang@163.com
    • Funds: Project supported by the the National Natural Science Foundation of China (Grant Nos. 51778495, 51978527), the Open Project of Key Laboratory of Bridge Structure Wind Resistance Technology (Shanghai), China (Grant No. KLWRTBMC21-02), and the Research Project of the Education Department of Liaoning Province, China (Grant No. LJKZ0052)
    [1]

    叶舒然, 张珍, 王一伟, 黄晨光 2021 航空学报 42 185Google Scholar

    Ye S R, Zhang Z, Wang Y W, Huang C G 2021 Acta Aeronaut. Astronaut. Sin. 42 185Google Scholar

    [2]

    王义乾, 桂南 2019 水动力学研究与进展(A辑) 34 413Google Scholar

    Wang Y Q, Gui N 2019 J. Hydrodyn. 34 413Google Scholar

    [3]

    刘超群 2020 空气动力学学报 38 413Google Scholar

    Liu C Q 2020 Acta Aerodyn. Sin. 38 413Google Scholar

    [4]

    王怡星, 韩仁坤, 刘子扬, 张扬, 陈刚 2021 航空学报 42 231Google Scholar

    Wang Y X, Qian R K, Liu Z Y, Zhang Y, Chen G 2021 Acta Aeronaut. Astronaut. Sin. 42 231Google Scholar

    [5]

    任峰, 高传强, 唐辉 2021 航空学报 42 152Google Scholar

    Ren F, Gao C Q, Tang H 2021 Acta Aeronaut. Astronaut. Sin. 42 152Google Scholar

    [6]

    王年华, 鲁鹏, 常兴华, 张来平 2021 力学学报 53 740Google Scholar

    Wang N H, Lu P, Chang X H, Zhang L P 2021 Chinese Journal of Theoretical and Applied Mechanics 53 740Google Scholar

    [7]

    Ling J, Kurzawski A, Templeton J 2016 J. Fluid Mech. 807 155Google Scholar

    [8]

    Maulik R, San O, Jacob J D, Crick C 2019 J. Fluid Mech. 870 784Google Scholar

    [9]

    Ren F, Wang C, Tang H 2019 Phys. Fluids 31 093601Google Scholar

    [10]

    Ren F, Wang C, Tang H 2021 Phys. Fluids 33 093602Google Scholar

    [11]

    Huang J, Liu H, Cai W 2019 J. Fluid Mech 875 R2Google Scholar

    [12]

    Zhang Y, Azman A N, Xu K W, Kim H B 2020 Exp. Fluids 61 1Google Scholar

    [13]

    Han J, Tao J, Wang C 2018 IEEE Trans. Visual. Comput. Graphics 26 1732Google Scholar

    [14]

    Liu Y, Lu Y, Wang Y, Sun D, Deng L, Wang F, Lei Y 2019 Comput. Fluids 184 1Google Scholar

    [15]

    Zhang Y, Azman A N, Xu K W, Kang C, Kim H B 2020 Experiments in Fluids 61 1

    [16]

    Strfer C A M, Wu J, Xiao H, Paterson E 2018 Commun. Comput. Phys. 25 625Google Scholar

    [17]

    Murata T, Fukami K, Fukagata K 2020 J. Fluid Mech. 882 A13Google Scholar

    [18]

    Omata N, Shirayama S 2019 AIP Adv. 9 015006Google Scholar

    [19]

    Kai F, Nakamura T, Fukagata K 2020 Phys. Fluids 32 095110Google Scholar

    [20]

    He K, Zhang X, Ren S, Sun J 2016 European Conference on Computer Vision Amsterdam, Netherlands, October 11–14, 2016 630

    [21]

    Szegedy C, Ioffe S, Vanhoucke V, Alemi A 2017 Thirty-First AAAI Conference on Artificial Intelligence San Francisco, California, USA, February 4–9, 2017

    [22]

    刘芙伶 李伟红 龚卫国 2020 计算机辅助设计与图形学学报 32 150Google Scholar

    Liu F L, Li W H, Gong W G 2020 CAD & CG 32 150Google Scholar

    [23]

    郑天韵, 王圣业, 王光学, 邓小刚 2020 物理学报 69 204701Google Scholar

    Zheng T Y, Wang S Y, W ang G X, Deng X G 2020 Acta Phys. Sin. 69 204701Google Scholar

    [24]

    Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R 2014 J. Mach. Learn. Res. 15 1929Google Scholar

    [25]

    Krizhevsky A, Sutskever I, Hinton G E 2017 Commun. ACM 60 84Google Scholar

    [26]

    Wang Z, Yan W, Oates T 2017 International Joint Conference on Neural Networks (IJCNN) Anchorage, Alaska, USA, May 14–19, 2017 p1578

    [27]

    Ioffe S 2017 Proceedings of the 31st International Conference on Neural Information Processing Systems Long Beach, California, USA, December 4–9, 2017 p1942

    [28]

    He K, Zhang X, Ren S, Sun J 2016 Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Las Vegas, Nevada, USA, June 27–30, 2016 p770

    [29]

    战庆亮, 周志勇, 葛耀君 2015 哈尔滨工业大学学报 47 75Google Scholar

    Zhan Q L, Zhou Z Y, Ge Y J 2015 J. Harbin Inst. Technol. 47 75Google Scholar

  • 图 1  特征提取方法

    Fig. 1.  Feature extraction methodology.

    图 2  时间卷积网络结构示意图

    Fig. 2.  Structure of time convolution neural network.

    图 3  全卷积网络结构示意图

    Fig. 3.  Structure of Fully connected convolution neural network.

    图 4  残差卷积网络结构示意图

    Fig. 4.  Structure of residual convolution neural network.

    图 5  整体计算域及平面网格划分

    Fig. 5.  Global computational domain and plane grid settings.

    图 6  各形状棱柱的瞬态流场云图 (a) 三棱柱压力云图; (b) 方柱压力云图; (c) 六棱柱压力云图; (d) 三棱柱速度云图; (e) 方柱速度云图; (f) 六棱柱速度云图

    Fig. 6.  Transient wake contour of prisms with different shapes: (a) Pressure contour of triangular prism; (b) pressure contour of square cylinder; (c) pressure contour of hexagonal prism; (d) velocity contour of triangular prism; (e) velocity contour of square cylinder; (f) velocity contour of hexagonal prism.

    图 7  尾流监测点位置示意图

    Fig. 7.  Location of wake monitoring points.

    图 8  典型测点的流场参数时程 (a)三棱柱; (b) 方柱; (c) 六棱柱

    Fig. 8.  Time history of flow field parameters at typical measuring points: (a) Triangular prism; (b) square cylinder; (c) hexagonal prism

    图 9  训练集的模型损失值 (a) 压力; (b) 速度

    Fig. 9.  Loss function of different models on training set: (a) Pressure; (b) velocity.

    图 10  训练集的模型精度 (a) 压力; (b) 速度

    Fig. 10.  Accuracy curve of different models on training set: (a) Pressure; (b) velocity.

    图 11  验证集的最优模型结果 (a) 模型损失值; (b) 模型准确率

    Fig. 11.  Summary of best model on the validation set: (a) Model loss value; (b) model accuracy

    图 12  压力时程的识别结果散点图 (a1)—(a4) 分别为MLP, TCNN, FCNN, RCNN第一类结果; (b1)—(b4) 分别为MLP, TCNN, FCNN, RCNN第二类结果; (c1)—(c4) 分别为MLP, TCNN, FCNN, RCNN第三类结果

    Fig. 12.  Identification results of pressure time history: (a1)–(a4) MLP, TCNN, FCNN, RCNN results of class1; (b1)–(b4) MLP, TCNN, FCNN, RCNN results of class2; (c1)–(c4) MLP, TCNN, FCNN, RCNN results of class1.

    图 13  速度时程的识别结果散点图 (a1)—(a4) 分别为MLP, TCNN, FCNN, RCNN第一类结果; (b1)—(b4) 分别为MLP, TCNN, FCNN, RCNN第二类结果; (c1)—(c4) 分别为MLP, TCNN, FCNN, RCNN第三类结果

    Fig. 13.  Identification results of velocity time history: (a1)–(a4) MLP, TCNN, FCNN, RCNN results of class1; (b1)–(b4) MLP, TCNN, FCNN, RCNN results of class2; (c1)–(c4) MLP, TCNN, FCNN, RCNN results of class1.

    表 1  一维卷积神经网络模型参数

    Table 1.  Structural parameters of capillary of different kind of fluid.

    名称特征提取运算加速收敛方法网络层数模型参数个数
    MLP全连接层Dropout10662501
    TCNN卷积层局部池化8961
    FCNN卷积层归一化层13264833
    RCNN卷积层残差直连层43504129
    下载: 导出CSV
  • [1]

    叶舒然, 张珍, 王一伟, 黄晨光 2021 航空学报 42 185Google Scholar

    Ye S R, Zhang Z, Wang Y W, Huang C G 2021 Acta Aeronaut. Astronaut. Sin. 42 185Google Scholar

    [2]

    王义乾, 桂南 2019 水动力学研究与进展(A辑) 34 413Google Scholar

    Wang Y Q, Gui N 2019 J. Hydrodyn. 34 413Google Scholar

    [3]

    刘超群 2020 空气动力学学报 38 413Google Scholar

    Liu C Q 2020 Acta Aerodyn. Sin. 38 413Google Scholar

    [4]

    王怡星, 韩仁坤, 刘子扬, 张扬, 陈刚 2021 航空学报 42 231Google Scholar

    Wang Y X, Qian R K, Liu Z Y, Zhang Y, Chen G 2021 Acta Aeronaut. Astronaut. Sin. 42 231Google Scholar

    [5]

    任峰, 高传强, 唐辉 2021 航空学报 42 152Google Scholar

    Ren F, Gao C Q, Tang H 2021 Acta Aeronaut. Astronaut. Sin. 42 152Google Scholar

    [6]

    王年华, 鲁鹏, 常兴华, 张来平 2021 力学学报 53 740Google Scholar

    Wang N H, Lu P, Chang X H, Zhang L P 2021 Chinese Journal of Theoretical and Applied Mechanics 53 740Google Scholar

    [7]

    Ling J, Kurzawski A, Templeton J 2016 J. Fluid Mech. 807 155Google Scholar

    [8]

    Maulik R, San O, Jacob J D, Crick C 2019 J. Fluid Mech. 870 784Google Scholar

    [9]

    Ren F, Wang C, Tang H 2019 Phys. Fluids 31 093601Google Scholar

    [10]

    Ren F, Wang C, Tang H 2021 Phys. Fluids 33 093602Google Scholar

    [11]

    Huang J, Liu H, Cai W 2019 J. Fluid Mech 875 R2Google Scholar

    [12]

    Zhang Y, Azman A N, Xu K W, Kim H B 2020 Exp. Fluids 61 1Google Scholar

    [13]

    Han J, Tao J, Wang C 2018 IEEE Trans. Visual. Comput. Graphics 26 1732Google Scholar

    [14]

    Liu Y, Lu Y, Wang Y, Sun D, Deng L, Wang F, Lei Y 2019 Comput. Fluids 184 1Google Scholar

    [15]

    Zhang Y, Azman A N, Xu K W, Kang C, Kim H B 2020 Experiments in Fluids 61 1

    [16]

    Strfer C A M, Wu J, Xiao H, Paterson E 2018 Commun. Comput. Phys. 25 625Google Scholar

    [17]

    Murata T, Fukami K, Fukagata K 2020 J. Fluid Mech. 882 A13Google Scholar

    [18]

    Omata N, Shirayama S 2019 AIP Adv. 9 015006Google Scholar

    [19]

    Kai F, Nakamura T, Fukagata K 2020 Phys. Fluids 32 095110Google Scholar

    [20]

    He K, Zhang X, Ren S, Sun J 2016 European Conference on Computer Vision Amsterdam, Netherlands, October 11–14, 2016 630

    [21]

    Szegedy C, Ioffe S, Vanhoucke V, Alemi A 2017 Thirty-First AAAI Conference on Artificial Intelligence San Francisco, California, USA, February 4–9, 2017

    [22]

    刘芙伶 李伟红 龚卫国 2020 计算机辅助设计与图形学学报 32 150Google Scholar

    Liu F L, Li W H, Gong W G 2020 CAD & CG 32 150Google Scholar

    [23]

    郑天韵, 王圣业, 王光学, 邓小刚 2020 物理学报 69 204701Google Scholar

    Zheng T Y, Wang S Y, W ang G X, Deng X G 2020 Acta Phys. Sin. 69 204701Google Scholar

    [24]

    Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R 2014 J. Mach. Learn. Res. 15 1929Google Scholar

    [25]

    Krizhevsky A, Sutskever I, Hinton G E 2017 Commun. ACM 60 84Google Scholar

    [26]

    Wang Z, Yan W, Oates T 2017 International Joint Conference on Neural Networks (IJCNN) Anchorage, Alaska, USA, May 14–19, 2017 p1578

    [27]

    Ioffe S 2017 Proceedings of the 31st International Conference on Neural Information Processing Systems Long Beach, California, USA, December 4–9, 2017 p1942

    [28]

    He K, Zhang X, Ren S, Sun J 2016 Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Las Vegas, Nevada, USA, June 27–30, 2016 p770

    [29]

    战庆亮, 周志勇, 葛耀君 2015 哈尔滨工业大学学报 47 75Google Scholar

    Zhan Q L, Zhou Z Y, Ge Y J 2015 J. Harbin Inst. Technol. 47 75Google Scholar

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出版历程
  • 收稿日期:  2021-07-25
  • 修回日期:  2021-12-08
  • 上网日期:  2022-01-26
  • 刊出日期:  2022-04-05

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