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An artificial neural network model for recovering small-scale velocity in large-eddy simulation of isotropic turbulent flows

文献类型：期刊论文

作者Tan, Jiangtao1,2; Jin, Guodong1,2; Jin GD(晋国栋); Tan JT(谭江涛)
刊名PHYSICS OF FLUIDS
出版日期2024-08-01
卷号36期号:8页码:16
ISSN号1070-6631
DOI10.1063/5.0221039
通讯作者Jin, Guodong(gdjin@lnm.imech.ac.cn)
英文摘要Small-scale motions in turbulent flows play a significant role in various small-scale processes, such as particle relative dispersion and collision, bubble or droplet deformation, and orientation dynamics of non-sphere particles. Recovering the small-scale flows that cannot be resolved in large eddy simulation (LES) is of great importance for such processes sensitive to the small-scale motions in turbulent flows. This study proposes a subgrid-scale model for recovering the small-scale turbulent velocity field based on the artificial neural network (ANN). The governing equations of small-scale turbulent velocity are linearized, and the pressure gradient and the nonlinear convection term are modeled with the aid of the ANN. Direct numerical simulation (DNS) and filtered direct numerical simulation (FDNS) provide the data required for training and validating the ANN. The large-scale velocity and velocity gradient tensor are selected as inputs for the ANN model. The linearized governing equations of small-scale turbulent velocity are numerically solved by coupling the large-scale flow field information. The results indicate that the model established by the ANN can accurately recover the small-scale velocity lost in FDNS due to filtering operation. With the ANN model, the flow fields at different Reynolds numbers agree well with the DNS results regarding velocity field statistics, flow field structures, turbulent energy spectra, and two-point, two-time Lagrangian correlation functions. This study demonstrates that the proposed ANN model can be applied to recovering the small-scale velocity field in the LES of isotropic turbulent flows at different Reynolds numbers.
WOS关键词TIME CORRELATIONS ; PARTICLES ; STRESS ; FLUX
资助项目National Natural Science Foundation of China10.13039/501100001809[11988102] ; NSFC Basic Science Center Program for Multiscale Problems in Nonlinear Mechanics[12272380] ; NSFC Program ; Aeronautical Science Foundation of China ; China Manned Space Engineering Program
WOS研究方向Mechanics ; Physics
语种英语
WOS记录号WOS:001294569100035
资助机构National Natural Science Foundation of China10.13039/501100001809 ; NSFC Basic Science Center Program for Multiscale Problems in Nonlinear Mechanics ; NSFC Program ; Aeronautical Science Foundation of China ; China Manned Space Engineering Program
源URL[http://dspace.imech.ac.cn/handle/311007/96566]  
专题力学研究所_非线性力学国家重点实验室
通讯作者Jin, Guodong
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		 Tan, Jiangtao,Jin, Guodong,Jin GD,et al. An artificial neural network model for recovering small-scale velocity in large-eddy simulation of isotropic turbulent flows[J]. PHYSICS OF FLUIDS,2024,36(8):16.
APA Tan, Jiangtao,Jin, Guodong,晋国栋,&谭江涛.(2024).An artificial neural network model for recovering small-scale velocity in large-eddy simulation of isotropic turbulent flows.PHYSICS OF FLUIDS,36(8),16.
MLA Tan, Jiangtao,et al."An artificial neural network model for recovering small-scale velocity in large-eddy simulation of isotropic turbulent flows".PHYSICS OF FLUIDS 36.8(2024):16.

入库方式： OAI收割

来源：力学研究所

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