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Knowledge-integrated additive learning for consistent near-wall modelling of turbulent flows

文献类型：期刊论文

作者Zhang, Fengshun1,2; Zhou, Zhideng1,2; Yang, Xiaolei1,2; He, Guowei1,2
刊名JOURNAL OF FLUID MECHANICS
出版日期2025-05-13
卷号1011页码:13
关键词machine learning turbulence modelling
ISSN号0022-1120
DOI10.1017/jfm.2025.361
通讯作者Yang, Xiaolei(xyang@imech.ac.cn)
英文摘要Developing a consistent near-wall turbulence model remains an unsolved problem. The machine learning method has the potential to become the workhorse for turbulence modelling. However, the learned model suffers from limited generalisability, especially for flows without similarity laws (e.g. separated flows). In this work, we propose a knowledge-integrated additive (KIA) learning approach for learning wall models in large-eddy simulations. The proposed approach integrates the knowledge in the simplified thin-boundary-layer equation with a data-driven forcing term for the non-equilibrium effects induced by pressure gradients and flow separations. The capability learned from each flow dataset is encapsulated using basis functions with the corresponding weights approximated using neural networks. The fusion of capabilities learned from various datasets is enabled using a distance function, in a way that the learned capability is preserved and the generalisability to other cases is allowed. The additive learning capability is demonstrated via training the model sequentially using the data of the flow with pressure gradient but no separation, and the separated flow data. The capability of the learned model to preserve previously learned capabilities is tested using turbulent channel flow cases. The periodic hill and the 2-D Gaussian bump cases showcase the generalisability of the model to flows with different surface curvatures and different Reynolds numbers. Good agreements with the references are obtained for all the test cases.
WOS关键词LARGE-EDDY SIMULATION ; ADVERSE-PRESSURE-GRADIENT ; CHANNEL FLOW
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[12172360] ; National Natural Science Foundation of China[12002345] ; 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:001485923700001
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 ; CAS Project for Young Scientists in Basic Research
源URL[http://dspace.imech.ac.cn/handle/311007/101555]  
专题力学研究所_非线性力学国家重点实验室
通讯作者Yang, Xiaolei
作者单位1.Chinese Acad Sci, Inst Mech, State Key Lab Nonlinear Mech, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Sch Engn Sci, Beijing 100049, Peoples R China
推荐引用方式
GB/T 7714		 Zhang, Fengshun,Zhou, Zhideng,Yang, Xiaolei,et al. Knowledge-integrated additive learning for consistent near-wall modelling of turbulent flows[J]. JOURNAL OF FLUID MECHANICS,2025,1011:13.
APA Zhang, Fengshun,Zhou, Zhideng,Yang, Xiaolei,&He, Guowei.(2025).Knowledge-integrated additive learning for consistent near-wall modelling of turbulent flows.JOURNAL OF FLUID MECHANICS,1011,13.
MLA Zhang, Fengshun,et al."Knowledge-integrated additive learning for consistent near-wall modelling of turbulent flows".JOURNAL OF FLUID MECHANICS 1011(2025):13.

入库方式： OAI收割

来源：力学研究所

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