| A practical approach to flow field reconstruction with sparse or incomplete data through physics informed neural network | |
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)
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| Source Publication | ACTA MECHANICA SINICA
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| 2023-03 | |
| Volume | 39Issue:3Pages:322302 |
| ISSN | 0567-7718 |
| Abstract | 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. |
| Keyword | Physics informed neural network Flow field reconstruction Particle image velocimetry Cosine annealing algorithm Experimental fluid dynamics |
| DOI | 10.1007/s10409-022-22302-x |
| Indexed By | SCI ; EI ; CSCD |
| Language | 英语 |
| WOS ID | WOS:000931280400007 |
| WOS Research Area | Engineering, Mechanical ; Mechanics |
| WOS Subject | Engineering ; Mechanics |
| Funding Organization | National Natural Science Foundation of China [52006232] ; Youth Innovation Promotion Association of Chinese Academy of Sciences [2019020] |
| Classification | 二类 |
| Ranking | 1 |
| Contributor | Sun, ZX ; Huang, RF (corresponding author), Chinese Acad Sci, Inst Mech, Key Lab Mech Fluid Solid Coupling Syst, Beijing 100190, Peoples R China. |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://dspace.imech.ac.cn/handle/311007/91831 |
| Collection | 流固耦合系统力学重点实验室 |
| Affiliation | 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 |
| Recommended Citation 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.Rp_Au:Sun, ZX, Huang, RF (corresponding author), Chinese Acad Sci, Inst Mech, Key Lab Mech Fluid Solid Coupling Syst, Beijing 100190, Peoples R China. |
| 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. |
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| Jp2023A228.pdf（4793KB） | 期刊论文 | 出版稿 | 开放获取 | CC BY-NC-SA | View Download | |
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