Wall model based on neural networks for LES of turbulent flows over periodic hills

doi:10.1103/PhysRevFluids.6.054610

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

	Wall model based on neural networks for LES of turbulent flows over periodic hills
	Zhou ZD(周志登)2; He GW(何国威)2; Yang XL(杨晓雷)2
刊名	PHYSICAL REVIEW FLUIDS
	2021-05-27
卷号	6 期号:5 页码:30
ISSN号	2469-990X
DOI	10.1103/PhysRevFluids.6.054610
通讯作者	Yang, Xiaolei(xyang@imech.ac.cn)
英文摘要	In this work, a data-driven wall model for turbulent flows over periodic hills is developed using the feedforward neural network (FNN) and data from wall-resolved large-eddy simulation (WRLES). To develop a wall model applicable to different flow regimes, the flow data in the near-wall region at all streamwise locations are grouped together as the training data set. In the developed FNN wall models, we employ the wall-normal distance, near-wall velocities, and pressure gradients as input features and the wall shear stresses as output labels, respectively. A priori tests on the prediction accuracy and generalization capacity of the trained FNN wall model are carried out by comparing the predicted wall shear stresses with the WRLES data from the same cases for model training and the cases with different Reynolds numbers and hill geometries. For the instantaneous wall shear stress, the FNN predictions show an overall good agreement with the WRLES data with some discrepancies observed at locations near the crest of the hill. The correlation coefficients between the FNN predictions and WRLES predictions are larger than 0.7 at most streamwise locations. For the mean wall shear stress, the FNN predictions agree very well with WRLES data. A posteriori test is also carried out. A good performance is observed for the turbulent channel flow case. Discrepancies between the predictions from the wall-modeled LES and the WRLES are observed for the periodic hill case.
分类号	二类
资助项目	NSFC Basic Science Center Program for Multiscale Problems in Nonlinear Mechanics[11988102] ; National Natural Science Foundation of China[12002345] ; China Postdoctoral Science Foundation[2020M680027]
WOS关键词	LARGE-EDDY SIMULATION ; DIRECT NUMERICAL-SIMULATION ; SEPARATED FLOW ; LAYER MODELS ; CHANNEL FLOW ; TURBINE
WOS研究方向	Physics
语种	英语
WOS记录号	WOS:000655982000001
资助机构	NSFC Basic Science Center Program for Multiscale Problems in Nonlinear Mechanics ; National Natural Science Foundation of China ; China Postdoctoral Science Foundation
其他责任者	Yang, Xiaolei
内容类型	期刊论文
源URL	[http://dspace.imech.ac.cn/handle/311007/86892]
专题	力学研究所_非线性力学国家重点实验室
作者单位	1.Univ Chinese Acad Sci, Sch Engn Sci, Beijing 100049, Peoples R China 2.Chinese Acad Sci, Inst Mech, State Key Lab Nonlinear Mech, Beijing 100190, Peoples R China;
推荐引用方式 GB/T 7714	Zhou ZD,He GW,Yang XL. Wall model based on neural networks for LES of turbulent flows over periodic hills[J]. PHYSICAL REVIEW FLUIDS,2021,6(5):30.
APA	周志登,何国威,&杨晓雷.(2021).Wall model based on neural networks for LES of turbulent flows over periodic hills.PHYSICAL REVIEW FLUIDS,6(5),30.
MLA	周志登,et al."Wall model based on neural networks for LES of turbulent flows over periodic hills".PHYSICAL REVIEW FLUIDS 6.5(2021):30.