Physical interpretation of neural network-based nonlinear eddy viscosity models

doi:10.1016/j.ast.2023.108632

IMECH-IR > 非线性力学国家重点实验室

	Physical interpretation of neural network-based nonlinear eddy viscosity models
	Zhang XL(张鑫磊); Xiao, Heng; Jee, Solkeun; He GW(何国威)
通讯作者	Jee, Solkeun(sjee@gist.ac.kr)
发表期刊	AEROSPACE SCIENCE AND TECHNOLOGY
	2023-11-01
卷号	142 页码:13
ISSN	1270-9638
摘要	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.
关键词	Machine learning Turbulence modeling Ensemble Kalman inversion Physical interpretability
DOI	10.1016/j.ast.2023.108632
收录类别	SCI ; EI
语种	英语
WOS记录号	WOS:001086005400001
关键词[WOS]	TURBULENCE ; FLOWS
WOS研究方向	Engineering
WOS类目	Engineering, Aerospace
资助项目	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]
项目资助者	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
论文分区	一类
力学所作者排名	1
RpAuthor	Jee, Solkeun
引用统计	被引频次：2[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://dspace.imech.ac.cn/handle/311007/93238
专题	非线性力学国家重点实验室
推荐引用方式 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.
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.

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