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

doi:10.1063/5.0078143

CORC > 力学研究所 > 中国科学院力学研究所 > 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, Hongping 1,2; Liu, Yi 1,2; Wang, Shizhao 1,2
刊名	PHYSICS OF FLUIDS
	2022
卷号	34 期号:1 页码:15
ISSN号	1070-6631
DOI	10.1063/5.0078143
通讯作者	Wang, Shizhao(wangsz@lnm.imech.ac.cn)
英文摘要	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.
资助项目	National Natural Science Foundation of China (NSFC) Basic Science Center Program[11988102] ; NSFC[12072348]
WOS关键词	BOUNDARY-LAYER ; PRESSURE DETERMINATION ; 3-DIMENSIONAL FLOWS ; PIV ; REGION ; VORTICES ; NOISE
WOS研究方向	Mechanics ; Physics
语种	英语
WOS记录号	WOS:000753213600005
资助机构	National Natural Science Foundation of China (NSFC) Basic Science Center Program ; NSFC
内容类型	期刊论文
源URL	[http://dspace.imech.ac.cn/handle/311007/88590]
专题	力学研究所_非线性力学国家重点实验室
通讯作者	Wang, Shizhao
作者单位	1.Univ Chinese Acad Sci, Sch Engn Sci, Beijing 100049, Peoples R China 2.Chinese Acad Sci, Inst Mech, State Key Lab Nonlinear Mech, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Wang, Hongping,Liu, Yi,Wang, Shizhao. 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	Wang, Hongping,Liu, Yi,&Wang, Shizhao.(2022).Dense velocity reconstruction from particle image velocimetry/particle tracking velocimetry using a physics-informed neural network.PHYSICS OF FLUIDS,34(1),15.
MLA	Wang, Hongping,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.