A robust super-resolution reconstruction model of turbulent flow data based on deep learning

doi:10.1016/j.compfluid.2022.105382

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

	A robust super-resolution reconstruction model of turbulent flow data based on deep learning
	Zhou, Zhideng ; Li, Binglin ; Yang, Xiaolei ; Yang, Zixuan 2
刊名	COMPUTERS & FLUIDS
	2022-05-15
卷号	239 页码:15
关键词	Super-resolution model Direct numerical simulation Large-Eddy simulation Isotropic turbulence Unresolved scales
ISSN号	0045-7930
DOI	10.1016/j.compfluid.2022.105382
通讯作者	Yang, Zixuan(yangzx@imech.ac.cn)
英文摘要	A new super-resolution model, namely the turbulence volumetric super-resolution (TVSR) model, is developed based on convolutional neural network (CNN) to reconstruct three-dimensional high-resolution turbulent flow field data from low-resolution data. Direct numerical simulation (DNS) and corresponding filtered DNS (FDNS) data of homogeneous isotropic turbulence at various Reynolds numbers are used to train the TVSR model. The proposed model is a modification of Liu et al. (2020), aiming to provide an improved generalization capability of the super-resolution model. For this purpose, we propose a patchwise training strategy in consideration of the property of turbulence that the velocity correlation between two points diminishes as the separation becomes sufficiently large. Furthermore, data at various Reynolds numbers are combined together to train the model. In comparison with existing models, the present TVSR model shows a better generalization capability in two aspects. First, the TVSR model trained using data at low Reynolds numbers is found robust and accurate in the super-resolution reconstructions of flow fields at higher Reynolds numbers. Second, although only DNS data are used for training, the TVSR model is also robust in reconstructing high-resolution flow fields from low-resolution data obtained from large-eddy simulation (LES). This feature of the TVSR model provides a new access to obtain turbulent motions at unresolved scales in LES studies of turbulent flows.
资助项目	National Natural Science Foundation of China (NSFC)[11988102] ; NSFC project[11972038] ; NSFC project[12002345] ; National Key Project[GJXM92579] ; Strategic Priority Research Program[XDB22040104]
WOS关键词	LARGE-EDDY SIMULATIONS ; ISOTROPIC TURBULENCE ; TIME CORRELATIONS ; DECONVOLUTION ; ENRICHMENT ; SCALES
WOS研究方向	Computer Science ; Mechanics
语种	英语
WOS记录号	WOS:000793060900001
资助机构	National Natural Science Foundation of China (NSFC) ; NSFC project ; National Key Project ; Strategic Priority Research Program
内容类型	期刊论文
源URL	[http://dspace.imech.ac.cn/handle/311007/89315]
专题	力学研究所_非线性力学国家重点实验室
通讯作者	Yang, Zixuan
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, Zhideng,Li, Binglin,Yang, Xiaolei,et al. A robust super-resolution reconstruction model of turbulent flow data based on deep learning[J]. COMPUTERS & FLUIDS,2022,239:15.
APA	Zhou, Zhideng,Li, Binglin,Yang, Xiaolei,&Yang, Zixuan.(2022).A robust super-resolution reconstruction model of turbulent flow data based on deep learning.COMPUTERS & FLUIDS,239,15.
MLA	Zhou, Zhideng,et al."A robust super-resolution reconstruction model of turbulent flow data based on deep learning".COMPUTERS & FLUIDS 239(2022):15.