Subgrid-scale model for large-eddy simulation of isotropic turbulent flows using an artificial neural network

doi:10.1016/j.compfluid.2019.104319

CORC > 力学研究所 > 中国科学院力学研究所 > 非线性力学国家重点实验室

	Subgrid-scale model for large-eddy simulation of isotropic turbulent flows using an artificial neural network
	Zhou ZD(周志登); He GW(何国威); Wang SZ(王士召); Jin GD(晋国栋)
刊名	COMPUTERS & FLUIDS
	2019-12-15
卷号	195 页码:UNSP 104319
关键词	Machine learning Artificial neural network Subgrid-scale model Large-eddy simulation Isotropic turbulent flows
ISSN号	0045-7930
DOI	10.1016/j.compfluid.2019.104319
英文摘要	An artificial neural network (ANN) is used to establish the relation between the resolved-scale flow field and the subgrid-scale (SGS) stress tensor, to develop a new SGS model for large-eddy simulation (LES) of isotropic turbulent flows. The data required for training and testing of the ANN are provided by performing filtering operations on the flow fields from direct numerical simulations (DNSs) of isotropic turbulent flows. We use the velocity gradient tensor together with filter width as input features and the SGS stress tensor as the output labels for training the ANN. In the a priori test of the trained ANN model, the SGS stress tensors obtained from the ANN model and the DNS data are compared by computing the correlation coefficient and the relative error of the energy transfer rate. The correlation coefficients are mostly larger than 0.9, and the ANN model can accurately predict the energy transfer rate at different Reynolds numbers and filter widths, showing significant improvement over the conventional models, for example the gradient model, the Smagorinsky model and its dynamic version. A real LES using the trained ANN model is performed as the a posteriori validation. The energy spectrum computed by the improved ANN model is compared with several SGS models. The Lagrangian statistics of fluid particle pairs obtained from the improved ANN model almost approach those from the filtered DNS, better than the results from the Smagorinsky model and dynamic Smagorinsky model. (C) 2019 Elsevier Ltd. All rights reserved.
分类号	二类
WOS关键词	DIRECT NUMERICAL SIMULATIONS ; DATA-DRIVEN ; FORM UNCERTAINTIES ; STRESS TENSOR ; INVARIANCE
WOS研究方向	Computer Science, Interdisciplinary Applications ; Mechanics
语种	英语
WOS记录号	WOS:000498330700003
资助机构	National Natural Science Foundation of ChinaNational Natural Science Foundation of China [11988102, 11772337, 11572331] ; Science Challenge Program [TZ2016001] ; Strategic Priority Research Program, CAS [XDB22040104] ; Key Research Program of Frontier Sciences, CAS [QYZDJ-SSW-SYS002]
其他责任者	Jin, GD (reprint author)
内容类型	期刊论文
源URL	[http://dspace.imech.ac.cn/handle/311007/80781]
专题	力学研究所_非线性力学国家重点实验室
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	Zhou ZD,He GW,Wang SZ,et al. Subgrid-scale model for large-eddy simulation of isotropic turbulent flows using an artificial neural network[J]. COMPUTERS & FLUIDS,2019,195:UNSP 104319.
APA	周志登,何国威,王士召,&晋国栋.(2019).Subgrid-scale model for large-eddy simulation of isotropic turbulent flows using an artificial neural network.COMPUTERS & FLUIDS,195,UNSP 104319.
MLA	周志登,et al."Subgrid-scale model for large-eddy simulation of isotropic turbulent flows using an artificial neural network".COMPUTERS & FLUIDS 195(2019):UNSP 104319.