Ensemble variational method with adaptive covariance inflation for learning neural network-based turbulence models
文献类型：期刊论文
| 作者 | Luo QY(罗清勇)1,2; Zhang XL(张鑫磊)1,2; He GW(何国威)1,2
|
| 刊名 | PHYSICS OF FLUIDS
 |
| 出版日期 | 2024-03-01 |
| 卷号 | 36期号:3页码:21 |
| ISSN号 | 1070-6631 |
| DOI | 10.1063/5.0199175 |
| 通讯作者 | Zhang, Xin-Lei(zhangxinlei@imech.ac.cn) ; He, Guowei(hgw@lnm.imech.ac.cn) |
| 英文摘要 | This work introduces an ensemble variational method with adaptive covariance inflation for learning nonlinear eddy viscosity turbulence models where the Reynolds stress anisotropy is represented with tensor-basis neural networks. The ensemble-based method has emerged as an important alternative to data-driven turbulence modeling due to its merit of non-derivativeness. However, the training accuracy of the ensemble method can be affected by the linearization assumption and sample collapse issue. Given these difficulties, we introduce the hybrid ensemble variational method, which inherits the merits of the ensemble method in non-derivativeness and the variational method in nonlinear analysis. Moreover, a covariance inflation scheme is proposed based on convergence states to alleviate the detrimental effects of sample collapse. The capability of the ensemble variational method in model learning is tested for flows in a square duct, flows over periodic hills, and flows around the S809 airfoil, with increasing complexity in the training data from direct observation to sparse indirect observation. Our results show that the ensemble variational method can learn relatively accurate neural network-based turbulence models in scenarios of small ensemble size and sample variances, compared to the ensemble Kalman method. It highlights the superiority of the ensemble variational method in practical applications, since small ensemble sizes can reduce computational costs, and small sample variance can ensure the training robustness by avoiding nonphysical samples of Reynolds stresses. |
| 分类号 | 一类/力学重要期刊 |
| WOS关键词 | IMPLEMENTATION ; OPTIMIZATION ; SCHEME ; FLOWS |
| 资助项目 | National Natural Science Foundation of China10.13039/501100001809[11988102] ; NSFC Basic Science Center Program[12102435] ; National Natural Science Foundation of China[2021M690154] ; China Postdoctoral Science Foundation[2022QNRC001] ; Young Elite Scientists Sponsorship Program by CAST |
| WOS研究方向 | Mechanics ; Physics |
| 语种 | 英语 |
| WOS记录号 | WOS:001190437000014 |
| 资助机构 | National Natural Science Foundation of China10.13039/501100001809 ; NSFC Basic Science Center Program ; National Natural Science Foundation of China ; China Postdoctoral Science Foundation ; Young Elite Scientists Sponsorship Program by CAST |
| 其他责任者 | Zhang, Xin-Lei ; He, Guowei |
| 源URL | [http://dspace.imech.ac.cn/handle/311007/94893]  |
| 专题 | 力学研究所_非线性力学国家重点实验室 |
| 作者单位 | 1.Univ Chinese Acad Sci, Sch Engn Sci, Beijing 100049, Peoples R China 2.Chinese Acad Sci, Inst Mech, State Key Lab Nonlinear Mech, Beijing 100190, Peoples R China; |
| 推荐引用方式 GB/T 7714 | Luo QY,Zhang XL,He GW. Ensemble variational method with adaptive covariance inflation for learning neural network-based turbulence models[J]. PHYSICS OF FLUIDS,2024,36(3):21. |
| APA | 罗清勇,张鑫磊,&何国威.(2024).Ensemble variational method with adaptive covariance inflation for learning neural network-based turbulence models.PHYSICS OF FLUIDS,36(3),21. |
| MLA | 罗清勇,et al."Ensemble variational method with adaptive covariance inflation for learning neural network-based turbulence models".PHYSICS OF FLUIDS 36.3(2024):21. |
入库方式： OAI收割
来源：力学研究所
浏览0
下载0
收藏0
其他版本
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。