A practical approach to flow field reconstruction with sparse or incomplete data through physics informed neural network
文献类型：期刊论文
| 作者 | Xu SF(许盛峰); Sun ZX(孙振旭) ; Huang RF(黄仁芳); Guo DL(郭迪龙); Yang GW(杨国伟); Ju SJ(鞠胜军)
|
| 刊名 | ACTA MECHANICA SINICA
 |
| 出版日期 | 2023-03 |
| 卷号 | 39期号:3页码:322302 |
| 关键词 | Physics informed neural network Flow field reconstruction Particle image velocimetry Cosine annealing algorithm Experimental fluid dynamics |
| ISSN号 | 0567-7718 |
| DOI | 10.1007/s10409-022-22302-x |
| 英文摘要 | High resolution flow field reconstruction is prevalently recognized as a difficult task in the field of experimental fluid mechanics, since the measured data are usually sparse and incomplete in time and space. Specifically, due to the limitations of experimental equipment or measurement techniques, the expected data cannot be measured in some key areas. In this paper, a practical approach is proposed to reconstruct flow field with imperfect data based on the physics informed neural network (PINN), which integrates those known data with the physical principles. The wake flow past a circular cylinder is taken as the test case. Two kinds of the training set are investigated, one is the velocity data with different sparsity, and the other is the velocity data missing in different regions. To accelerate training convergence, the learning rate schedule is discussed, and the cosine annealing algorithm shows excellent performance. Results reveal that the proposed approach not only can reconstruct the true velocity field with high accuracy, but also can predict the pressure field precisely, even when the data sparsity reaches 1% or the core flow area data are truncated away. This study provides encouraging insights that the PINN can serve as a promising data assimilation method for experimental fluid mechanics. |
| 分类号 | 二类 |
| WOS研究方向 | Engineering, Mechanical ; Mechanics |
| 语种 | 英语 |
| WOS记录号 | WOS:000931280400007 |
| 资助机构 | National Natural Science Foundation of China [52006232] ; Youth Innovation Promotion Association of Chinese Academy of Sciences [2019020] |
| 其他责任者 | Sun, ZX ; Huang, RF (corresponding author), Chinese Acad Sci, Inst Mech, Key Lab Mech Fluid Solid Coupling Syst, Beijing 100190, Peoples R China. |
| 源URL | [http://dspace.imech.ac.cn/handle/311007/91831]  |
| 专题 | 力学研究所_流固耦合系统力学重点实验室(2012-) |
| 作者单位 | 1.Chinese Acad Sci, Inst Mech, Key Lab Mech Fluid Solid Coupling Syst, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Engn Sci, Beijing 100049, Peoples R China |
| 推荐引用方式 GB/T 7714 | Xu SF,Sun ZX,Huang RF,et al. A practical approach to flow field reconstruction with sparse or incomplete data through physics informed neural network[J]. ACTA MECHANICA SINICA,2023,39(3):322302. |
| APA | 许盛峰,孙振旭,黄仁芳,郭迪龙,杨国伟,&鞠胜军.(2023).A practical approach to flow field reconstruction with sparse or incomplete data through physics informed neural network.ACTA MECHANICA SINICA,39(3),322302. |
| MLA | 许盛峰,et al."A practical approach to flow field reconstruction with sparse or incomplete data through physics informed neural network".ACTA MECHANICA SINICA 39.3(2023):322302. |
入库方式： OAI收割
来源：力学研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。