Dense velocity reconstruction from particle image velocimetry/particle tracking velocimetry using a physics-informed neural network

doi:10.1063/5.0078143

IMECH-IR > 非线性力学国家重点实验室

	Dense velocity reconstruction from particle image velocimetry/particle tracking velocimetry using a physics-informed neural network
	Wang HP(王洪平); Liu Y(刘毅); Wang SZ(王士召)
通讯作者	Wang, Shizhao(wangsz@lnm.imech.ac.cn)
发表期刊	PHYSICS OF FLUIDS
	2022
卷号	34 期号:1 页码:15
ISSN	1070-6631
摘要	The velocities measured by particle image velocimetry (PIV) and particle tracking velocimetry (PTV) commonly provide sparse information on flow motions. A dense velocity field with high resolution is indispensable for data visualization and analysis. In the present work, a physics-informed neural network (PINN) is proposed to reconstruct the dense velocity field from sparse experimental data. A PINN is a network-based data assimilation method. Within the PINN, both the velocity and pressure are approximated by minimizing a loss function consisting of the residuals of the data and the Navier-Stokes equations. Therefore, the PINN can not only improve the velocity resolution but also predict the pressure field. The performance of the PINN is investigated using two-dimensional (2D) Taylor's decaying vortices and turbulent channel flow with and without measurement noise. For the case of 2D Taylor's decaying vortices, the activation functions, optimization algorithms, and some parameters of the proposed method are assessed. For the case of turbulent channel flow, the ability of the PINN to reconstruct wall-bounded turbulence is explored. Finally, the PINN is applied to reconstruct dense velocity fields from the experimental tomographic PIV (Tomo-PIV) velocity in the three-dimensional wake flow of a hemisphere. The results indicate that the proposed PINN has great potential for extending the capabilities of PIV/PTV.
DOI	10.1063/5.0078143
收录类别	SCI ; EI
语种	英语
WOS记录号	WOS:000753213600005
关键词[WOS]	BOUNDARY-LAYER ; PRESSURE DETERMINATION ; 3-DIMENSIONAL FLOWS ; PIV ; REGION ; VORTICES ; NOISE
WOS研究方向	Mechanics ; Physics
WOS类目	Mechanics ; Physics, Fluids & Plasmas
资助项目	National Natural Science Foundation of China (NSFC) Basic Science Center Program[11988102] ; NSFC[12072348]
项目资助者	National Natural Science Foundation of China (NSFC) Basic Science Center Program ; NSFC
论文分区	一类/力学重要期刊
力学所作者排名	1
RpAuthor	Wang, Shizhao
引用统计	被引频次：57[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://dspace.imech.ac.cn/handle/311007/88591
专题	非线性力学国家重点实验室
推荐引用方式 GB/T 7714	Wang HP,Liu Y,Wang SZ. Dense velocity reconstruction from particle image velocimetry/particle tracking velocimetry using a physics-informed neural network[J]. PHYSICS OF FLUIDS,2022,34,1,:15.
APA	王洪平,刘毅,&王士召.(2022).Dense velocity reconstruction from particle image velocimetry/particle tracking velocimetry using a physics-informed neural network.PHYSICS OF FLUIDS,34(1),15.
MLA	王洪平,et al."Dense velocity reconstruction from particle image velocimetry/particle tracking velocimetry using a physics-informed neural network".PHYSICS OF FLUIDS 34.1(2022):15.

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