Flow feature extraction models based on deep learning

Zhan Qing-Liang; Ge Yao-Jun; Bai Chun-Jin

doi:10.7498/aps.71.20211373

Abstract

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.

Keywords:

Authors and contacts

1.
College of Transportation and Engineering, Dalian Maritime University, Dalian 116026, China
2.
State Key Laboratory for Disaster Reduction in Civil Engineering, Tongji University, Shanghai 200092, China

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)

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References

[1]	叶舒然, 张珍, 王一伟, 黄晨光 2021 航空学报 42 185 Google Scholar Ye S R, Zhang Z, Wang Y W, Huang C G 2021 Acta Aeronaut. Astronaut. Sin. 42 185 Google Scholar
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图 1 特征提取方法

Figure 1. Feature extraction methodology.

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图 2 时间卷积网络结构示意图

Figure 2. Structure of time convolution neural network.

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图 3 全卷积网络结构示意图

Figure 3. Structure of Fully connected convolution neural network.

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图 4 残差卷积网络结构示意图

Figure 4. Structure of residual convolution neural network.

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图 5 整体计算域及平面网格划分

Figure 5. Global computational domain and plane grid settings.

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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

Figure 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.

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图 7 尾流监测点位置示意图

Figure 7. Location of wake monitoring points.

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

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

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图 9 训练集的模型损失值　(a) 压力; (b) 速度

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

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图 10 训练集的模型精度　(a) 压力; (b) 速度

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

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图 11 验证集的最优模型结果　(a) 模型损失值; (b) 模型准确率

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

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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

Figure 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.

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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

Figure 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.

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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	Dropout	10	662501
TCNN	卷积层	局部池化	8	961
FCNN	卷积层	归一化层	13	264833
RCNN	卷积层	残差直连层	43	504129

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[1]	叶舒然, 张珍, 王一伟, 黄晨光 2021 航空学报 42 185 Google Scholar Ye S R, Zhang Z, Wang Y W, Huang C G 2021 Acta Aeronaut. Astronaut. Sin. 42 185 Google Scholar
[2]	王义乾, 桂南 2019 水动力学研究与进展(A辑) 34 413 Google Scholar Wang Y Q, Gui N 2019 J. Hydrodyn. 34 413 Google Scholar
[3]	刘超群 2020 空气动力学学报 38 413 Google Scholar Liu C Q 2020 Acta Aerodyn. Sin. 38 413 Google Scholar
[4]	王怡星, 韩仁坤, 刘子扬, 张扬, 陈刚 2021 航空学报 42 231 Google Scholar Wang Y X, Qian R K, Liu Z Y, Zhang Y, Chen G 2021 Acta Aeronaut. Astronaut. Sin. 42 231 Google Scholar
[5]	任峰, 高传强, 唐辉 2021 航空学报 42 152 Google Scholar Ren F, Gao C Q, Tang H 2021 Acta Aeronaut. Astronaut. Sin. 42 152 Google Scholar
[6]	王年华, 鲁鹏, 常兴华, 张来平 2021 力学学报 53 740 Google Scholar Wang N H, Lu P, Chang X H, Zhang L P 2021 Chinese Journal of Theoretical and Applied Mechanics 53 740 Google Scholar
[7]	Ling J, Kurzawski A, Templeton J 2016 J. Fluid Mech. 807 155 Google Scholar
[8]	Maulik R, San O, Jacob J D, Crick C 2019 J. Fluid Mech. 870 784 Google Scholar
[9]	Ren F, Wang C, Tang H 2019 Phys. Fluids 31 093601 Google Scholar
[10]	Ren F, Wang C, Tang H 2021 Phys. Fluids 33 093602 Google Scholar
[11]	Huang J, Liu H, Cai W 2019 J. Fluid Mech 875 R2 Google Scholar
[12]	Zhang Y, Azman A N, Xu K W, Kim H B 2020 Exp. Fluids 61 1 Google Scholar
[13]	Han J, Tao J, Wang C 2018 IEEE Trans. Visual. Comput. Graphics 26 1732 Google Scholar
[14]	Liu Y, Lu Y, Wang Y, Sun D, Deng L, Wang F, Lei Y 2019 Comput. Fluids 184 1 Google 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. 25 625 Google Scholar
[17]	Murata T, Fukami K, Fukagata K 2020 J. Fluid Mech. 882 A13 Google Scholar
[18]	Omata N, Shirayama S 2019 AIP Adv. 9 015006 Google Scholar
[19]	Kai F, Nakamura T, Fukagata K 2020 Phys. Fluids 32 095110 Google 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 32 150 Google Scholar Liu F L, Li W H, Gong W G 2020 CAD & CG 32 150 Google Scholar
[23]	郑天韵, 王圣业, 王光学, 邓小刚 2020 69 204701 Google Scholar Zheng T Y, Wang S Y, W ang G X, Deng X G 2020 Acta Phys. Sin. 69 204701 Google Scholar
[24]	Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R 2014 J. Mach. Learn. Res. 15 1929 Google Scholar
[25]	Krizhevsky A, Sutskever I, Hinton G E 2017 Commun. ACM 60 84 Google 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 75 Google Scholar Zhan Q L, Zhou Z Y, Ge Y J 2015 J. Harbin Inst. Technol. 47 75 Google Scholar

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Vol. 71, Issue 7 - 2022-04-05

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