Data augmented prediction of Reynolds stresses for flows around an axisymmetric body of revolution
文献类型：期刊论文
| 作者 | Liu, Yi ; Wang, Shizhao; Zhang, Xin-Lei2; He, Guowei2; He GW(何国威); Wang SZ(王士召)
|
| 刊名 | OCEAN ENGINEERING
 |
| 出版日期 | 2024-03-15 |
| 卷号 | 296页码:14 |
| 关键词 | Ensemble Kalman method Machine learning Turbulence modeling SUBOFF model |
| ISSN号 | 0029-8018 |
| DOI | 10.1016/j.oceaneng.2024.116717 |
| 通讯作者 | Zhang, Xin-Lei(zhangxinlei@imech.ac.cn) ; He, Guowei(hgw@lnm.imech.ac.cn) |
| 英文摘要 | This work presents a data -driven approach to improve the predictive accuracy of the Reynolds stresses for flows over an axisymmetric body of revolution. Sparse experimental data of velocity and Reynolds normal stress is used to learn a nonlinear eddy viscosity model represented by neural networks with the ensemble Kalman method. It was recently proposed in Zhang et al., (2023) to enhance the predictive capability of the model by introducing a neural network -based model correction in the turbulent kinetic energy transport equation. Here this newly developed technique is applied for the first time to three-dimensional engineering flows in which the complete set of tensor functions are used. The results show that the method can learn a model with good predictive capability in both velocity and turbulence kinetic energy. The predictive improvement is achieved by suppressing turbulence production in the boundary layer and wake -shear layer with the added correction term in the turbulent kinetic energy transport equation. Moreover, the learned model can improve the prediction of Reynolds normal stresses by capturing the Reynolds stress anisotropy with nonlinear tensor bases. The necessity of combining the nonlinear tensor functions and the correction in turbulence transport equations is highlighted for accurate prediction of the Reynolds stress. |
| WOS关键词 | LARGE-EDDY SIMULATION ; TURBULENCE MODELS ; NOISE |
| 资助项目 | NSFC Basic Science Center Program[11988102] ; CAS Project for Young Scientists in Basic Research[YSBR-087] ; National Natural Science Foundation of China[12102439] ; National Natural Science Foundation of China[12102435] ; China Postdoctoral Science Foundation[2021M703290] ; China Postdoctoral Science Foundation[2021M690154] |
| WOS研究方向 | Engineering ; Oceanography |
| 语种 | 英语 |
| WOS记录号 | WOS:001207409800001 |
| 资助机构 | NSFC Basic Science Center Program ; CAS Project for Young Scientists in Basic Research ; National Natural Science Foundation of China ; China Postdoctoral Science Foundation |
| 源URL | [http://dspace.imech.ac.cn/handle/311007/94987]  |
| 专题 | 力学研究所_非线性力学国家重点实验室 |
| 通讯作者 | Zhang, Xin-Lei; He, Guowei |
| 作者单位 | 1.Univ Chinese Acad Sci, Sch Engn Sci, Beijing 100049, Peoples R China 2.Chinese Acad Sci, LNM, Inst Mech, Beijing 100190, Peoples R China |
| 推荐引用方式 GB/T 7714 | Liu, Yi,Wang, Shizhao,Zhang, Xin-Lei,et al. Data augmented prediction of Reynolds stresses for flows around an axisymmetric body of revolution[J]. OCEAN ENGINEERING,2024,296:14. |
| APA | Liu, Yi,Wang, Shizhao,Zhang, Xin-Lei,He, Guowei,何国威,&王士召.(2024).Data augmented prediction of Reynolds stresses for flows around an axisymmetric body of revolution.OCEAN ENGINEERING,296,14. |
| MLA | Liu, Yi,et al."Data augmented prediction of Reynolds stresses for flows around an axisymmetric body of revolution".OCEAN ENGINEERING 296(2024):14. |
入库方式： OAI收割
来源：力学研究所
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