| Ensemble Kalman method for learning turbulence models from indirect observation data | |
Zhang XL(张鑫磊); Xiao, Heng2; Luo, Xiaodong3; He GW(何国威)
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| Source Publication | JOURNAL OF FLUID MECHANICS
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| 2022-09 | |
| Volume | 949 |
| ISSN | 0022-1120 |
| Abstract | In this work, we propose using an ensemble Kalman method to learn a nonlinear eddy viscosity model, represented as a tensor basis neural network, from velocity data. Data-driven turbulence models have emerged as a promising alternative to traditional models for providing closure mapping from the mean velocities to Reynolds stresses. Most data-driven models in this category need full-field Reynolds stress data for training, which not only places stringent demand on the data generation but also makes the trained model ill-conditioned and lacks robustness. This difficulty can be alleviated by incorporating the Reynolds-averaged Navier-Stokes (RANS) solver in the training process. However, this would necessitate developing adjoint solvers of the RANS model, which requires extra effort in code development and maintenance. Given this difficulty, we present an ensemble Kalman method with an adaptive step size to train a neural-network-based turbulence model by using indirect observation data. To our knowledge, this is the first such attempt in turbulence modelling. The ensemble method is first verified on the flow in a square duct, where it correctly learns the underlying turbulence models from velocity data. Then the generalizability of the learned model is evaluated on a family of separated flows over periodic hills. It is demonstrated that the turbulence model learned in one flow can predict flows in similar configurations with varying slopes. |
| Keyword | turbulence modelling machine learning |
| Subject Area | Mechanics ; Physics, Fluids & Plasmas |
| DOI | 10.1017/jfm.2022.744 |
| Indexed By | SCI ; EI |
| Language | 英语 |
| WOS ID | WOS:000861459400001 |
| Funding Organization | NSFC Basic Science Center Program for `Multiscale Problems in Nonlinear Mechanics' [11988102] ; National Natural Science Foundation of China [12102435] ; China Postdoctoral Science Foundation [2021M690154] ; National Centre for Sustainable Subsurface Utilization of the Norwegian Continental Shelf, Norway [NCS2030] |
| Classification | 一类/力学重要期刊 |
| Ranking | 1 |
| Contributor | He, GW (corresponding author), Chinese Acad Sci, Inst Mech, State Key Lab Nonlinear Mech, Beijing 100049, Peoples R China. ; He, GW (corresponding author), Univ Chinese Acad Sci, Sch Engn Sci, Beijing 100049, Peoples R China. ; Xiao, H (corresponding author), Virginia Tech, Kevin T Crofton Dept Aerosp & Ocean Engn, Blacksburg, VA 24060 USA. |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://dspace.imech.ac.cn/handle/311007/90181 |
| Collection | 非线性力学国家重点实验室 |
| Affiliation | 1.Chinese Acad Sci, Inst Mech, State Key Lab Nonlinear Mech, Beijing 100049, Peoples R China 2.Univ Chinese Acad Sci, Sch Engn Sci, Beijing 100049, Peoples R China 3.Virginia Tech, Kevin T Crofton Dept Aerosp & Ocean Engn, Blacksburg, VA 24060 USA 4.Norwegian Res Ctr NORCE, Nygardsgaten 112, N-5008 Bergen, Norway |
| Recommended Citation GB/T 7714 | Zhang XL,Xiao, Heng,Luo, Xiaodong,et al. Ensemble Kalman method for learning turbulence models from indirect observation data[J]. JOURNAL OF FLUID MECHANICS,2022,949.Rp_Au:He, GW (corresponding author), Chinese Acad Sci, Inst Mech, State Key Lab Nonlinear Mech, Beijing 100049, Peoples R China., He, GW (corresponding author), Univ Chinese Acad Sci, Sch Engn Sci, Beijing 100049, Peoples R China., Xiao, H (corresponding author), Virginia Tech, Kevin T Crofton Dept Aerosp & Ocean Engn, Blacksburg, VA 24060 USA. |
| APA | 张鑫磊,Xiao, Heng,Luo, Xiaodong,&何国威.(2022).Ensemble Kalman method for learning turbulence models from indirect observation data.JOURNAL OF FLUID MECHANICS,949. |
| MLA | 张鑫磊,et al."Ensemble Kalman method for learning turbulence models from indirect observation data".JOURNAL OF FLUID MECHANICS 949(2022). |
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| Jp2022FA174_2022_Ens（1302KB） | 期刊论文 | 出版稿 | 开放获取 | CC BY-NC-SA | View Download | |
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