A high-resolution velocity field predicted method in near wall boundary layer by CNN

WANG Shaofei; PAN Chong; QI Zhongyang

doi:10.11729/syltlx20210142

Volume 36 Issue 3

Jul. 2022

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Article Navigation > Journal of Experiments in Fluid Mechanics > 2022 > 36(3): 110-117

WANG S F，PAN C，QI Z Y. A high-resolution velocity field predicted method in near wall boundary layer by CNN[J]. Journal of Experiments in Fluid Mechanics, 2022，36（3）：110-117. doi: 10.11729/syltlx20210142

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A high-resolution velocity field predicted method in near wall boundary layer by CNN

doi: 10.11729/syltlx20210142

WANG Shaofei^1
,,
PAN Chong^{2, 1
,
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QI Zhongyang¹

1.
Aircraft and Propulsion Laboratory, Ningbo Institute of Technology, Beihang University, Ningbo　315100, China
2.
Key Laboratory of Fluid Mechanics of Ministry of Education, Beihang University, Beijing　100191, China

Received Date: 2021-11-16
Accepted Date: 2022-03-11
Rev Recd Date: 2022-03-07

Available Online: 2022-07-04

Publish Date: 2022-07-04

Abstract

Abstract

A high-resolution velocity field prediction method in the boundary layer based on CNN-PTV is designed and verified in this paper. This method includes two stages, training process and prediction process. In the training process, a CNN model is trained by the particle image pairs. By optimizing the parameters, the exact ensemble particle movements are predicted by the CNN model. In the prediction process, a synthetic image including just a single particle is imported to the CNN model to estimate the flow information at this particular pixel space. Thus, a high-resolution velocity field is predicted. Comparing with the single-pixel ensemble correlation method, the CNN-PTV method has a higher precision. And the results of CNN-PTV method is insensitive to the frame numbers and particle density.
- particle images,
- CNN,
- boundary layer

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References(13)

References

[1]	WINTER K G. An outline of the techniques available for the measurement of skin friction in turbulent boundary layers[J]. Progress in Aerospace Sciences,1979,18:1-57. doi: 10.1016/0376-0421(77)90002-1
[2]	赵荣娟,吕治国,黄军,等. 基于压电敏感元件的摩阻天平设计[J]. 空气动力学学报,2018,36(4):555-560. ZHAO R J,LYU Z G,HUANG J,et al. Design of skin friction balance based on piezoelectric ceramics[J]. Acta Aerodynamica Sinica,2018,36(4):555-560.
[3]	NGUYEN C V,WELLS J C. Direct measurement of fluid velocity gradients at a wall by PIV image processing with stereo reconstruction[J]. Journal of Visualization,2006,9(2):199-208. doi: 10.1007/BF03181763
[4]	WILLERT C E. High-speed particle image velocimetry for the efficient measurement of turbulence statistics[J]. Experiments in Fluids,2015,56(1):1-17. doi: 10.1007/s00348-014-1892-4
[5]	申俊琦,王建杰,潘翀. 平板湍流边界层瞬时摩擦阻力的光学测量和统计分析[J]. 气体物理,2020,5(5):13-23. SHEN J Q,WANG J J,PAN C. Optical measurement and statistical analysis of instantaneous wall-shear stress in a turbulent boundary layer[J]. Physics of Gases,2020,5(5):13-23.
[6]	SHEN J Q,PAN C,WANG J J. Accurate measurement of wall skin friction by single-pixel ensemble correlation[J]. Science China Physics, Mechanics & Astronomy,2014,57(7):1352-1362. doi: 10.1007/s11433-014-5462-9
[7]	NIE M Y,PAN C,WANG J J,et al. A hybrid 3D particle matching algorithm based on ant colony optimization[J]. Experiments in Fluids,2021,62(4):1-17. doi: 10.1007/s00348-021-03160-4
[8]	DOSOVITSKIY A, FISCHER P, ILG E, et al. FlowNet: learning optical flow with convolutional networks[C]//Proc of the 2015 IEEE International Conference on Computer Vision. 2015: 2758-2766. doi: 10.1109/ICCV.2015.316
[9]	ILG E, MAYER N, SAIKIA T, et al. FlowNet 2.0: evolution of optical flow estimation with deep networks[C]//Proc of the 2017 IEEE Conference on Computer Vision and Pattern Recognition. 2017: 1647-1655. doi: 10.1109/CVPR.2017.179
[10]	CAI S Z,ZHOU S C,XU C,et al. Dense motion estimation of particle images via a convolutional neural network[J]. Experiments in Fluids,2019,60(4):1-16. doi: 10.1007/s00348-019-2717-2
[11]	LAGEMANN C,LAGEMANN K,MUKHERJEE S,et al. Deep recurrent optical flow learning for particle image velocimetry data[J]. Nature Machine Intelligence,2021,3(7):641-651. doi: 10.1038/s42256-021-00369-0
[12]	LECORDIER B, WESTERWEEL J. The EUROPIV synthetic image generator （S. I. G.）[C]//Proc of Proceedings of the Particle Image Velocimetry: Recent Improvements. 2004.
[13]	WANG L W,PAN C,LIU J H,et al. Ratio-cut background removal method and its application in near-wall PTV measurement of a turbulent boundary layer[J]. Measurement Science and Technology,2021,32(2):025302. doi: 10.1088/1361-6501/abb483