Deep learning method for the super-resolution reconstruction of small-scale motions in large-eddy simulation

doi:10.1063/5.0127808

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

	Deep learning method for the super-resolution reconstruction of small-scale motions in large-eddy simulation
	Zhao QY(赵庆义)1,2; Jin GD(晋国栋)1,2; Zhou ZD(周志登)1,2
刊名	AIP ADVANCES
	2022-12-01
卷号	12 期号:12 页码:9
DOI	10.1063/5.0127808
通讯作者	Jin, Guodong(gdjin@lnm.imech.ac.cn)
英文摘要	A super-resolution reconstruction model for the subgrid scale (SGS) turbulent flow field in large-eddy simulation (LES) is proposed, and it is called the meta-learning deep convolutional neural network (MLDCNN). Direct numerical simulation (DNS) data of isotropic turbulence are used as the dataset of the model. The MLDCNN is an unsupervised learning model, which only includes high-resolution DNS data without manually inputting preprocessed low-resolution data. In this model, the training process adopts the meta-learning method. First, in the a priori test, the SGS turbulent flow motions in the filtered DNS (FDNS) flow field are reconstructed, and the energy spectrum and probability density function of the velocity gradient of the DNS flow field are reconstructed with high accuracy. Then, in the a posteriori test, the super-resolution reconstruction of the LES flow field is carried out. The difficulty of LES flow field reconstruction is that it contains filtering loss and subgrid model errors relative to the DNS flow field. The super-resolution reconstruction of the LES flow field achieves good results through this unsupervised learning model. The proposed model makes a good prediction of small-scale motions in the LES flow field. This work improves the prediction accuracy of LES, which is crucial for the phenomena dominated by small-scale motions, such as relative motions of particles suspended in turbulent flows. (c) 2022 Author(s).
分类号	Q3
资助项目	NSFC Basic Science Center Program for Multiscale Problems in Nonlinear Mechanics[11988102] ; NSFC Program[12272380] ; National Key Project[GJXM92579]
WOS关键词	CIRCULAR-CYLINDER ; TURBULENCE ; FLOW ; DECONVOLUTION ; NETWORK ; MODEL
WOS研究方向	Science & Technology - Other Topics ; Materials Science ; Physics
语种	英语
WOS记录号	WOS:000894508800006
资助机构	NSFC Basic Science Center Program for Multiscale Problems in Nonlinear Mechanics ; NSFC Program ; National Key Project
其他责任者	Jin, Guodong
内容类型	期刊论文
源URL	[http://dspace.imech.ac.cn/handle/311007/91341]
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	Zhao QY,Jin GD,Zhou ZD. Deep learning method for the super-resolution reconstruction of small-scale motions in large-eddy simulation[J]. AIP ADVANCES,2022,12(12):9.
APA	赵庆义,晋国栋,&周志登.(2022).Deep learning method for the super-resolution reconstruction of small-scale motions in large-eddy simulation.AIP ADVANCES,12(12),9.
MLA	赵庆义,et al."Deep learning method for the super-resolution reconstruction of small-scale motions in large-eddy simulation".AIP ADVANCES 12.12(2022):9.