| 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)
| |
| Corresponding Author | Wang, Shizhao([email protected]) |
| Source Publication | PHYSICS OF FLUIDS
 |
| 2022 | |
| Volume | 34Issue:1Pages:15 |
| ISSN | 1070-6631 |
| Abstract | 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 |
| Indexed By | SCI ; EI |
| Language | 英语 |
| WOS ID | WOS:000753213600005 |
| WOS Keyword | BOUNDARY-LAYER ; PRESSURE DETERMINATION ; 3-DIMENSIONAL FLOWS ; PIV ; REGION ; VORTICES ; NOISE |
| WOS Research Area | Mechanics ; Physics |
| WOS Subject | Mechanics ; Physics, Fluids & Plasmas |
| Funding Project | National Natural Science Foundation of China (NSFC) Basic Science Center Program[11988102] ; NSFC[12072348] |
| Funding Organization | National Natural Science Foundation of China (NSFC) Basic Science Center Program ; NSFC |
| Classification | 一类/力学重要期刊 |
| Ranking | 1 |
| Contributor | Wang, Shizhao |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://dspace.imech.ac.cn/handle/311007/88591 |
| Collection | 非线性力学国家重点实验室 |
| Recommended Citation 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.Rp_Au:Wang, Shizhao |
| 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. |
| Files in This Item: | Download All | |||||
| File Name/Size | DocType | Version | Access | License | ||
| Jp2022FA564_2022_Den（8518KB） | 期刊论文 | 出版稿 | 开放获取 | CC BY-NC-SA | View Download | |
Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.
Edit Comment