A wall model for separated flows: embedded learning to improve a posteriori performance
文献类型：期刊论文
| 作者 | Zhou ZD(周志登)1,2; Zhang XL(张鑫磊)1,2; He GW(何国威)1,2 ; Yang XL(杨晓雷)1,2
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| 刊名 | JOURNAL OF FLUID MECHANICS
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| 出版日期 | 2024-12-23 |
| 卷号 | 1002页码:42 |
| 关键词 | turbulence modelling machine learning boundary layer separation |
| ISSN号 | 0022-1120 |
| DOI | 10.1017/jfm.2024.1127 |
| 通讯作者 | Yang, Xiaolei(xyang@imech.ac.cn) |
| 英文摘要 | Developing large-eddy simulation (LES) wall models for separated flows is challenging. We propose to leverage the significance of separated flow data, for which existing theories are not applicable, and the existing knowledge of wall-bounded flows (such as the law of the wall) along with embedded learning to address this issue. The proposed so-called features-embedded-learning (FEL) wall model comprises two submodels: one for predicting the wall shear stress and another for calculating the eddy viscosity at the first off-wall grid nodes. We train the former using the wall-resolved LES (WRLES) data of the periodic hill flow and the law of the wall. For the latter, we propose a modified mixing length model, with the model coefficient trained using the ensemble Kalman method. The proposed FEL model is assessed using the separated flows with different flow configurations, grid resolutions and Reynolds numbers. Overall good a posteriori performance is observed for predicting the statistics of the recirculation bubble, wall stresses and turbulence characteristics. The statistics of the modelled subgrid-scale (SGS) stresses at the first off-wall grids are compared with those calculated using the WRLES data. The comparison shows that the amplitude and distribution of the SGS stresses and energy transfer obtained using the proposed model agree better with the reference data when compared with the conventional SGS model. |
| 分类号 | 一类/力学重要期刊 |
| WOS关键词 | LARGE-EDDY SIMULATION ; SUBGRID-SCALE MODEL ; CHANNEL |
| 资助项目 | NSFC Basic Science Center Program for 'Multiscale Problems in Nonlinear Mechanics'[11988102] ; Strategic Priority Research Program of Chinese Academy of Sciences (CAS)[XDB0620102] ; National Natural Science Foundation of China[12002345] ; National Natural Science Foundation of China[12172360] ; Joint Funds of CAS[8091A120203] ; CAS Project for Young Scientists in Basic Research[YSBR-087] |
| WOS研究方向 | Mechanics ; Physics |
| 语种 | 英语 |
| WOS记录号 | WOS:001381150400001 |
| 资助机构 | NSFC Basic Science Center Program for 'Multiscale Problems in Nonlinear Mechanics' ; Strategic Priority Research Program of Chinese Academy of Sciences (CAS) ; National Natural Science Foundation of China ; Joint Funds of CAS ; CAS Project for Young Scientists in Basic Research |
| 其他责任者 | Yang, Xiaolei |
| 源URL | [http://dspace.imech.ac.cn/handle/311007/98012]  |
| 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 | Zhou ZD,Zhang XL,He GW,et al. A wall model for separated flows: embedded learning to improve a posteriori performance[J]. JOURNAL OF FLUID MECHANICS,2024,1002:42. |
| APA | 周志登,张鑫磊,何国威,&杨晓雷.(2024).A wall model for separated flows: embedded learning to improve a posteriori performance.JOURNAL OF FLUID MECHANICS,1002,42. |
| MLA | 周志登,et al."A wall model for separated flows: embedded learning to improve a posteriori performance".JOURNAL OF FLUID MECHANICS 1002(2024):42. |
入库方式： OAI收割
来源：力学研究所
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