Comparative assessment for pressure field reconstruction based on physics-informed neural network
文献类型：期刊论文
| 作者 | Fan, Di; Xu, Yang; Wang HP(王洪平); Wang, Jinjun |
| 刊名 | PHYSICS OF FLUIDS
 |
| 出版日期 | 2023-07-01 |
| 卷号 | 35期号:7页码:77116 |
| ISSN号 | 1070-6631 |
| DOI | 10.1063/5.0157753 |
| 英文摘要 | 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. |
| 分类号 | 一类/力学重要期刊 |
| WOS研究方向 | Mechanics ; Physics |
| 语种 | 英语 |
| WOS记录号 | WOS:001036412800017 |
| 资助机构 | National Natural Science Foundation of China (NSFC) [11902019, 12172030, 12072348] ; Fundamental Research Funds for the Central Universities |
| 其他责任者 | Wang, HP (corresponding author), Chinese Acad Sci, Inst Mech, State Key Lab Nonlinear Mech, Beijing 100190, Peoples R China. |
| 源URL | [http://dspace.imech.ac.cn/handle/311007/92592]  |
| 专题 | 力学研究所_非线性力学国家重点实验室 |
| 作者单位 | 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 |
| 推荐引用方式 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. |
| 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. |
入库方式： OAI收割
来源：力学研究所
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