Multi-scale physics-informed neural networks for solving high Reynolds number boundary layer flows based on matched asymptotic expansions
文献类型：期刊论文
| 作者 | Huang JL(黄剑霖)3,4; Qiu RD(丘润荻)3,4; Wang JZ(王静竹)1,2,4; Wang YW(王一伟)2,3,4
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| 刊名 | Theoretical and Applied Mechanics Letters
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| 出版日期 | 2024-03 |
| 卷号 | 14期号:2页码:100496 |
| ISSN号 | 2095-0349 |
| DOI | 10.1016/j.taml.2024.100496 |
| 英文摘要 | Multi-scale system remains a classical scientific problem in fluid dynamics, biology, etc. In the present study, a scheme of multi-scale Physics-informed neural networks (msPINNs) is proposed to solve the boundary layer flow at high Reynolds numbers without any data. The flow is divided into several regions with different scales based on Prandtl's boundary theory. Different regions are solved with governing equations in different scales. The method of matched asymptotic expansions is used to make the flow field continuously. A flow on a semi infinite flat plate at a high Reynolds number is considered a multi-scale problem because the boundary layer scale is much smaller than the outer flow scale. The results are compared with the reference numerical solutions, which show that the msPINNs can solve the multi-scale problem of the boundary layer in high Reynolds number flows. This scheme can be developed for more multi-scale problems in the future. © 2024 |
| 分类号 | 二类 |
| 语种 | 英语 |
| 其他责任者 | Wang, Yiwei (wangyw@imech.ac.cn) |
| 源URL | [http://dspace.imech.ac.cn/handle/311007/97574]  |
| 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.Guangdong Aerospace Research Academy, Guangzhou; 511458, China 2.School of Engineering Science, University of Chinese Academy of Sciences, Beijing; 100049, China; 3.School of Future Technology, University of Chinese Academy of Sciences, Beijing; 100049, China; 4.Key Laboratory for Mechanics in Fluid Solid Coupling Systems, Institute of Mechanics, Chinese Academy of Sciences, Beijing; 100190, China; |
| 推荐引用方式 GB/T 7714 | Huang JL,Qiu RD,Wang JZ,et al. Multi-scale physics-informed neural networks for solving high Reynolds number boundary layer flows based on matched asymptotic expansions[J]. Theoretical and Applied Mechanics Letters,2024,14(2):100496. |
| APA | 黄剑霖,丘润荻,王静竹,&王一伟.(2024).Multi-scale physics-informed neural networks for solving high Reynolds number boundary layer flows based on matched asymptotic expansions.Theoretical and Applied Mechanics Letters,14(2),100496. |
| MLA | 黄剑霖,et al."Multi-scale physics-informed neural networks for solving high Reynolds number boundary layer flows based on matched asymptotic expansions".Theoretical and Applied Mechanics Letters 14.2(2024):100496. |
入库方式： OAI收割
来源：力学研究所
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