Artificial neural network approach to large-eddy simulation of compressible isotropic turbulence

Xie, Chenyue1; Wang, Jianchun1; Li, Ke2; Ma, Chao3

2019-05-21

10.1103/PhysRevE.99.053113

PHYSICAL REVIEW E   影响因子和分区

2470-0045

2470-0053

A subgrid-scale (SGS) model for large-eddy simulation (LES) of compressible isotropic turbulence is constructed by using a data-driven framework. An artificial neural network (ANN) based on local stencil geometry is employed to predict the unclosed SGS terms. The input features are based on the first-order and second-order derivatives of filtered velocity and temperature which appear in the second-order Taylor approximation of the SGS stress and heat flux. It is shown that the proposed ANN-7 model performs better than the gradient model in the a priori test. The correlation coefficient is larger and the relative error is smaller for ANN-7 model as compared to those of the gradient model in the a priori test. In an a posteriori analysis, the performance of ANN-7 model shows advantage over the dynamic Smagorinsky model and dynamic mixed model in the prediction of spectra and structure functions of velocity and temperature, and instantaneous flow structures. Artificial neural network is a promising tool for understanding the physical fundamentals of SGS unclosed terms with further improvement.

SCI ; EI

英语

第一 ; 通讯

Young Elite Scientist Sponsorship Program by CAST[2016QNRC001]

Physics

Physics, Fluids & Plasmas ; Physics, Mathematical

WOS:000469027500006

AMER PHYSICAL SOC

20210509867435

Heat flux ; Large eddy simulation ; Turbulence

Fluid Flow:631 ; Heat Transfer:641.2 ; Mathematics:921

PHYSICS

Web of Science

1.Southern Univ Sci & Technol, Dept Mech & Aerosp Engn, Shenzhen 518055, Peoples R China
2.Chinese Acad Sci, Inst Computat Math & Sci Engn Comp, Beijing 100190, Peoples R China
3.Princeton Univ, Program Appl & Computat Math, Princeton, NJ 08544 USA

Xie, Chenyue,Wang, Jianchun,Li, Ke,et al. Artificial neural network approach to large-eddy simulation of compressible isotropic turbulence[J]. PHYSICAL REVIEW E,2019,99(5).

Xie, Chenyue,Wang, Jianchun,Li, Ke,&Ma, Chao.(2019).Artificial neural network approach to large-eddy simulation of compressible isotropic turbulence.PHYSICAL REVIEW E,99(5).

Xie, Chenyue,et al."Artificial neural network approach to large-eddy simulation of compressible isotropic turbulence".PHYSICAL REVIEW E 99.5(2019).