| Exploring hidden flow structures from sparse data through deep learning strengthened proper orthogonal decomposition | |
Yan C(闫畅); Xu SF(许盛峰); Sun ZX(孙振旭) ; Guo DL(郭迪龙); Ju SJ(鞠胜军); Huang RF(黄仁芳); Yang GW(杨国伟)
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| Source Publication | PHYSICS OF FLUIDS
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| 2023-03 | |
| Volume | 35Issue:3Pages:37119 |
| ISSN | 1070-6631 |
| Abstract | Proper orthogonal decomposition (POD) enables complex flow fields to be decomposed into linear modes according to their energy, allowing the key features of the flow to be extracted. However, traditional POD requires high quality inputs, namely, high resolution spatiotemporal data. To alleviate the dependence of traditional POD on the quality and quantity of data, this paper presents a POD method that is strengthened by a physics informed neural network (PINN) with an overlapping domain decomposition strategy. The loss function and convergence of modes are considered simultaneously to determine the convergence of the PINN POD model. The proposed framework is applied to the flow past a two dimensional circular cylinder at Reynolds numbers ranging from 100 to 10 000 and achieves accurate and robust extraction of flow structures from spatially sparse observation data. The spatial structures and dominant frequency can also be extracted under high level noise. These results demonstrate that the proposed PINN POD method is a reliable tool for extracting the key features from sparse observation data of flow fields, potentially shedding light on the data driven discovery of hidden fluid dynamics. |
| DOI | 10.1063/5.0138287 |
| Indexed By | SCI ; EI |
| Language | 英语 |
| WOS ID | WOS:000952382600006 |
| WOS Research Area | Mechanics ; Physics, Fluids & Plasmas |
| WOS Subject | Mechanics ; Physics |
| Funding Organization | Youth Innovation Promotion Association CAS [2019020] ; National Natural Science Foundation of China [52006232] |
| Classification | 一类/力学重要期刊 |
| Ranking | 1 |
| Contributor | Sun, ZX (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/311007/91863 |
| 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 Future Technol, Beijing 100049, Peoples R China 3.Univ Chinese Acad Sci, Sch Engn Sci, Beijing 100049, Peoples R China |
| Recommended Citation GB/T 7714 | Yan C,Xu SF,Sun ZX,et al. Exploring hidden flow structures from sparse data through deep learning strengthened proper orthogonal decomposition[J]. PHYSICS OF FLUIDS,2023,35,3,:37119.Rp_Au:Sun, ZX (corresponding author), Chinese Acad Sci, Inst Mech, Key Lab Mech Fluid Solid Coupling Syst, Beijing 100190, Peoples R China. |
| APA | 闫畅.,许盛峰.,孙振旭.,郭迪龙.,鞠胜军.,...&杨国伟.(2023).Exploring hidden flow structures from sparse data through deep learning strengthened proper orthogonal decomposition.PHYSICS OF FLUIDS,35(3),37119. |
| MLA | 闫畅,et al."Exploring hidden flow structures from sparse data through deep learning strengthened proper orthogonal decomposition".PHYSICS OF FLUIDS 35.3(2023):37119. |
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| Jp2023A230.pdf（11492KB） | 期刊论文 | 出版稿 | 开放获取 | CC BY-NC-SA | View Download | |
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