Modeling subgrid-scale forces by spatial artificial neural networks in large eddy simulation of turbulence

Xie, Chenyue1; Wang, Jianchun1; E, Weinan2

2020-05-18

10.1103/PhysRevFluids.5.054606

Physical Review Fluids   影响因子和分区

2469-990X

Spatial artificial neural network (ANN) models are developed for subgrid-scale (SGS) forces in the large eddy simulation (LES) of turbulence. The input features are based on the first-order derivatives of the filtered velocity field at different spatial locations. The correlation coefficients of SGS forces predicted by the spatial artifical neural network (SANN) models with reasonable spatial stencil geometry can be made larger than 0.99 in an a priori analysis, and the relative error of SGS forces can be made smaller than 15%, much smaller than that of the traditional gradient model. In a posteriori analysis, a detailed comparison is made on the results of LES using the SANN model, implicit large eddy simulation (ILES), the dynamic Smagorinsky model (DSM), and the dynamic mixed model (DMM) at grid resolution of 64(3). It is shown that the SANN model performs better than the ILES, DSM, and DMM models in the prediction of the spectrum and other statistical properties of the velocity field, as well as the instantaneous flow structures. These results suggest that artificial neural network with consideration of spatial characteristics is a very effective tool for developing advanced SGS models in LES of turbulence.

SCI ; EI

英语

第一 ; 通讯

National Natural Science Foundation of China (NSFC)[91952104][11702127][91752201] ; Technology and Innovation Commission of Shenzhen Municipality[KQTD20180411143441009][JCYJ20170412151759222][ZDSYS201802081843517] ; Young Elite Scientist Sponsorship Program by CAST[2016QNRC001]

Physics

Physics, Fluids & Plasmas

WOS:000533504900004

AMER PHYSICAL SOC

20202908942429

Velocity ; Neural network models ; Turbulence

Fluid Flow:631 ; Artificial Intelligence:723.4 ; Mathematics:921

Web of Science

1.Southern Univ Sci & Technol, Shenzhen Key Lab Complex Aerosp Flows, Ctr Complex Flows & Soft Matter Res, Dept Mech & Aerosp Engn, Shenzhen 518055, Peoples R China
2.Princeton Univ, Dept Math, Program Appl & Computat Math, Princeton, NJ 08544 USA

Xie, Chenyue,Wang, Jianchun,E, Weinan. Modeling subgrid-scale forces by spatial artificial neural networks in large eddy simulation of turbulence[J]. Physical Review Fluids,2020,5(5).

Xie, Chenyue,Wang, Jianchun,&E, Weinan.(2020).Modeling subgrid-scale forces by spatial artificial neural networks in large eddy simulation of turbulence.Physical Review Fluids,5(5).

Xie, Chenyue,et al."Modeling subgrid-scale forces by spatial artificial neural networks in large eddy simulation of turbulence".Physical Review Fluids 5.5(2020).