Spatially multi-scale artificial neural network model for large eddy simulation of compressible isotropic turbulence

Xie，Chenyue1,2; Wang，Jianchun1; Li，Hui2; Wan，Minping1; Chen，Shiyi1,3

2020

10.1063/1.5138681

AIP Advances   影响因子和分区

21583226

2158-3226

In this work, subgrid-scale (SGS) stress and SGS heat flux of compressible isotropic turbulence are reconstructed by a spatially multi-scale artificial neural network (SMSANN). The input features of the SMSANN model are based on the first order derivatives of the primary and secondary filtered variables at different spatial locations. The SMSANN model performs much better than the gradient model in the a priori test, including the correlation coefficients and relative errors. Specifically, the correlation coefficients of the SGS stress and SGS heat flux can be larger than 0.997 and the relative errors of the SGS stress and SGS heat flux can be smaller than 0.08 for the SMSANN model. In an a posteriori analysis, the performance of the SMSANN model has been evaluated by a detailed comparison of the results of the SMSANN model and the dynamic mixed model (DMM) at a grid resolution of 64 with the Taylor Reynolds number Re ranging from 180 to 250. The SMSANN model shows an advantage over the DMM in the prediction of the spectra of velocity and temperature. Besides, the SMSANN model can accurately reconstruct the statistical properties of velocity and temperature and the instantaneous flow structures. An artificial neural network with consideration of spatial multiscale can deepen our understanding of large eddy simulation modeling.

EI ; SCI

英语

第一 ; 通讯

Southern University of Science and Technology[] ; National Natural Science Foundation of China[91752201] ; China Academy of Space Technology[2016QNR C001] ; National Natural Science Foundation of China[] ; Science, Technology and Innovation Commission of Shenzhen Municipality[]

Science & Technology - Other Topics ; Materials Science ; Physics

Nanoscience & Nanotechnology ; Materials Science, Multidisciplinary ; Physics, Applied

WOS:000519589500002

American Institute of Physics Inc.

20200508109002

Large eddy simulation ; Neural networks ; Reynolds equation ; Reynolds number ; Turbulence ; Turbulent flow

Fluid Flow:631 ; Fluid Flow, General:631.1 ; Heat Transfer:641.2 ; Mathematics:921

2-s2.0-85078675340

Scopus

1.Shenzhen Key Laboratory of Complex Aerospace Flows,Center for Complex Flows and Soft Matter Research,Department of Mechanics and Aerospace Engineering,Southern University of Science and Technology,Shenzhen,518055,China
2.School of Power and Mechanical Engineering,Wuhan University,Wuhan,430072,China
3.State Key Laboratory of Turbulence and Complex Systems,Peking University,Beijing,100871,China

Xie，Chenyue,Wang，Jianchun,Li，Hui,et al. Spatially multi-scale artificial neural network model for large eddy simulation of compressible isotropic turbulence[J]. AIP Advances,2020,10(1).

Xie，Chenyue,Wang，Jianchun,Li，Hui,Wan，Minping,&Chen，Shiyi.(2020).Spatially multi-scale artificial neural network model for large eddy simulation of compressible isotropic turbulence.AIP Advances,10(1).

Xie，Chenyue,et al."Spatially multi-scale artificial neural network model for large eddy simulation of compressible isotropic turbulence".AIP Advances 10.1(2020).