Exploring hidden flow structures from sparse data through deep learning strengthened proper orthogonal decomposition
文献类型：期刊论文
| 作者 | 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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| 刊名 | PHYSICS OF FLUIDS
 |
| 出版日期 | 2023-03 |
| 卷号 | 35期号:3页码:37119 |
| ISSN号 | 1070-6631 |
| DOI | 10.1063/5.0138287 |
| 英文摘要 | 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. |
| 分类号 | 一类/力学重要期刊 |
| WOS研究方向 | Mechanics ; Physics, Fluids & Plasmas |
| 语种 | 英语 |
| WOS记录号 | WOS:000952382600006 |
| 资助机构 | Youth Innovation Promotion Association CAS [2019020] ; National Natural Science Foundation of China [52006232] |
| 其他责任者 | Sun, ZX (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/311007/91863]  |
| 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 Future Technol, Beijing 100049, Peoples R China 3.Univ Chinese Acad Sci, Sch Engn Sci, Beijing 100049, Peoples R China |
| 推荐引用方式 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. |
| 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. |
入库方式： OAI收割
来源：力学研究所
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