| Physical interpretation of neural network-based nonlinear eddy viscosity models | |
Zhang XL(张鑫磊); Xiao, Heng; Jee, Solkeun; He GW(何国威)
| |
| Corresponding Author | Jee, Solkeun([email protected]) |
| Source Publication | AEROSPACE SCIENCE AND TECHNOLOGY
 |
| 2023-11-01 | |
| Volume | 142Pages:13 |
| ISSN | 1270-9638 |
| Abstract | Neural network-based turbulence modeling has gained significant success in improving turbulence predictions by incorporating high fidelity data. However, the interpretability of the learned model is often not fully analyzed, which has been one of the main criticisms of neural network-based turbulence modeling. Therefore, it is increasingly demanding to provide physical interpretation of the trained model, which is of significant interest for guiding the development of interpretable and unified turbulence models. The present work aims to interpret the predictive improvement of turbulence flows based on the behavior of the learned model, represented with tensor basis neural networks. The ensemble Kalman method is used for model learning from sparse observation data due to its ease of implementation and high training efficiency. Two cases, i.e., flow over the S809 airfoil and flow in a square duct, are used to demonstrate the physical interpretation of the ensemble-based turbulence modeling. For the flow over the S809 airfoil, our results show that the ensemble Kalman method learns an optimal linear eddy viscosity model, which improves the prediction of the aerodynamic lift by reducing the eddy viscosity in the upstream boundary layer and promoting the early onset of flow separation. For the square duct case, the method provides a nonlinear eddy viscosity model, which predicts well secondary flows by capturing the imbalance of the Reynolds normal stresses. The flexibility of the ensemble-based method is highlighted to capture characteristics of the flow separation and secondary flow by adjusting the nonlinearity of the turbulence model.(c) 2023 Elsevier Masson SAS. All rights reserved. |
| Keyword | Machine learning Turbulence modeling Ensemble Kalman inversion Physical interpretability |
| DOI | 10.1016/j.ast.2023.108632 |
| Indexed By | SCI ; EI |
| Language | 英语 |
| WOS ID | WOS:001086005400001 |
| WOS Keyword | TURBULENCE ; FLOWS |
| WOS Research Area | Engineering |
| WOS Subject | Engineering, Aerospace |
| Funding Project | 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 Research Foundation of Korea[NRF-2021H1D3A2A01096296] |
| Funding Organization | NSFC Basic Science Center Program for Multiscale Problems in Nonlinear Mechanics ; National Natural Science Foundation of China ; China Postdoctoral Science Foundation ; National Research Foundation of Korea |
| Classification | 一类 |
| Ranking | 1 |
| Contributor | Jee, Solkeun |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://dspace.imech.ac.cn/handle/311007/93238 |
| Collection | 非线性力学国家重点实验室 |
| Recommended Citation GB/T 7714 | Zhang XL,Xiao, Heng,Jee, Solkeun,et al. Physical interpretation of neural network-based nonlinear eddy viscosity models[J]. AEROSPACE SCIENCE AND TECHNOLOGY,2023,142:13.Rp_Au:Jee, Solkeun |
| APA | 张鑫磊,Xiao, Heng,Jee, Solkeun,&何国威.(2023).Physical interpretation of neural network-based nonlinear eddy viscosity models.AEROSPACE SCIENCE AND TECHNOLOGY,142,13. |
| MLA | 张鑫磊,et al."Physical interpretation of neural network-based nonlinear eddy viscosity models".AEROSPACE SCIENCE AND TECHNOLOGY 142(2023):13. |
| Files in This Item: | Download All | |||||
| File Name/Size | DocType | Version | Access | License | ||
| Jp2023A246.pdf（5699KB） | 期刊论文 | 出版稿 | 开放获取 | CC BY-NC-SA | View Download | |
Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.
Edit Comment