| Comparative assessment for pressure field reconstruction based on physics-informed neural network | |
| Fan, Di; Xu, Yang; Wang HP(王洪平); Wang, Jinjun | |
| Source Publication | PHYSICS OF FLUIDS
 |
| 2023-07-01 | |
| Volume | 35Issue:7Pages:77116 |
| ISSN | 1070-6631 |
| Abstract | In this paper, a physics-informed neural network (PINN) is used to determine pressure fields from the experimentally measured velocity data. As a novel method of data assimilation, PINN can simultaneously optimize velocity and solve pressure by embedding the Navier-Stokes equations into the loss function. The PINN method is compared with two traditional pressure reconstruction algorithms, i.e., spectral decomposition-based fast pressure integration and irrotation correction on pressure gradient and orthogonal-path integration, and its performance is numerically assessed using two kinds of flow motions, namely, Taylor's decaying vortices and forced isotropic turbulence. In the case of two-dimensional decaying vortices, critical parameters of PINN have been investigated with and without considering measurement errors. Regarding the forced isotropic turbulence, the influence of spatial resolution and out-of-plane motion on pressure reconstruction is assessed. Finally, in an experimental case of a synthetic jet impinging on a solid wall, the PINN is used to determine the pressure from the velocity fields obtained by the planar particle image velocimetry. All results show that the PINN-based pressure reconstruction is superior to other methods even if the velocity fields are significantly contaminated by the measurement errors. |
| DOI | 10.1063/5.0157753 |
| Indexed By | SCI ; EI |
| Language | 英语 |
| WOS ID | WOS:001036412800017 |
| WOS Research Area | Mechanics ; Physics |
| WOS Subject | Mechanics ; Physics, Fluids & Plasmas |
| Funding Organization | National Natural Science Foundation of China (NSFC) [11902019, 12172030, 12072348] ; Fundamental Research Funds for the Central Universities |
| Classification | 一类/力学重要期刊 |
| Ranking | 1 |
| Contributor | Wang, HP (corresponding author), Chinese Acad Sci, Inst Mech, State Key Lab Nonlinear Mech, Beijing 100190, Peoples R China. |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://dspace.imech.ac.cn/handle/311007/92592 |
| Collection | 非线性力学国家重点实验室 |
| Affiliation | 1.{Fan, Di, Xu, Yang, Wang, Jinjun} Beijing Univ Aeronaut & Astronaut, Fluid Mech Key Lab Educ Minist, Beijing 100191, Peoples R China 2.{Wang, Hongping} Chinese Acad Sci, Inst Mech, State Key Lab Nonlinear Mech, Beijing 100190, Peoples R China |
| Recommended Citation GB/T 7714 | Fan, Di,Xu, Yang,Wang HP,et al. Comparative assessment for pressure field reconstruction based on physics-informed neural network[J]. PHYSICS OF FLUIDS,2023,35,7,:77116.Rp_Au:Wang, HP (corresponding author), Chinese Acad Sci, Inst Mech, State Key Lab Nonlinear Mech, Beijing 100190, Peoples R China. |
| APA | Fan, Di,Xu, Yang,王洪平,&Wang, Jinjun.(2023).Comparative assessment for pressure field reconstruction based on physics-informed neural network.PHYSICS OF FLUIDS,35(7),77116. |
| MLA | Fan, Di,et al."Comparative assessment for pressure field reconstruction based on physics-informed neural network".PHYSICS OF FLUIDS 35.7(2023):77116. |
| Files in This Item: | Download All | |||||
| File Name/Size | DocType | Version | Access | License | ||
| Jp2023A017.pdf（5156KB） | 期刊论文 | 出版稿 | 开放获取 | CC BY-NC-SA | View Download | |
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