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) | |
发表期刊 | PHYSICS OF FLUIDS |
2023-03 | |
卷号 | 35期号:3页码:37119 |
ISSN | 1070-6631 |
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. |
DOI | 10.1063/5.0138287 |
收录类别 | SCI ; EI |
语种 | 英语 |
WOS记录号 | WOS:000952382600006 |
WOS研究方向 | Mechanics ; Physics, Fluids & Plasmas |
WOS类目 | Mechanics ; Physics |
项目资助者 | Youth Innovation Promotion Association CAS [2019020] ; National Natural Science Foundation of China [52006232] |
论文分区 | 一类/力学重要期刊 |
力学所作者排名 | 1 |
RpAuthor | Sun, ZX (corresponding author), Chinese Acad Sci, Inst Mech, Key Lab Mech Fluid Solid Coupling Syst, Beijing 100190, Peoples R China. |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://dspace.imech.ac.cn/handle/311007/91863 |
专题 | 流固耦合系统力学重点实验室 |
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. |
条目包含的文件 | 下载所有文件 | |||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
Jp2023A230.pdf(11492KB) | 期刊论文 | 出版稿 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
个性服务 |
推荐该条目 |
保存到收藏夹 |
查看访问统计 |
导出为Endnote文件 |
Lanfanshu学术 |
Lanfanshu学术中相似的文章 |
[闫畅]的文章 |
[许盛峰]的文章 |
[孙振旭]的文章 |
百度学术 |
百度学术中相似的文章 |
[闫畅]的文章 |
[许盛峰]的文章 |
[孙振旭]的文章 |
必应学术 |
必应学术中相似的文章 |
[闫畅]的文章 |
[许盛峰]的文章 |
[孙振旭]的文章 |
相关权益政策 |
暂无数据 |
收藏/分享 |
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。
修改评论