Deconvolutional artificial neural network models for large eddy simulation of turbulence

Yuan，Zelong; Xie，Chenyue; Wang，Jianchun

2020-11-01

10.1063/5.0027146

PHYSICS OF FLUIDS   影响因子和分区

1070-6631

1089-7666

Deconvolutional artificial neural network (DANN) models are developed for subgrid-scale (SGS) stress in large eddy simulation (LES) of turbulence. The filtered velocities at different spatial points are used as input features of the DANN models to reconstruct the unfiltered velocity. The grid width of the DANN models is chosen to be smaller than the filter width in order to accurately model the effects of SGS dynamics. The DANN models can predict the SGS stress more accurately than the conventional approximate deconvolution method and velocity gradient model in the a priori study: the correlation coefficients can be made larger than 99% and the relative errors can be made less than 15% for the DANN model. In an a posteriori study, a comprehensive comparison of the DANN model, the implicit LES (ILES), the dynamic Smagorinsky model (DSM), and the dynamic mixed model (DMM) shows that the DANN model is superior to the ILES, DSM, and DMM models in the prediction of the velocity spectrum, various statistics of velocity, and the instantaneous coherent structures without increasing the considerable computational cost; the time for the DANN model to calculate the SGS stress is about 1.3 times that of the DMM model. In addition, the trained DANN models without any fine-tuning can predict the velocity statistics well for different filter widths. These results indicate that the DANN framework with the consideration of SGS spatial features is a promising approach to develop advanced SGS models in the LES of turbulence.

SCI ; EI

英语

第一 ; 通讯

National Numerical Windtunnel Project[NNW2019ZT1-A04] ; National Natural Science Foundation of China (NSFC)[91952104][11702127][91752201] ; Shenzhen Science and Technology Program[KQTD20180411143441009][JCYJ20170412151759222] ; Department of Science and Technology of Guangdong Province[2019B21203001] ; Young Elite Scientist Sponsorship Program by CAST[2016QNRC001]

Mechanics ; Physics

Mechanics ; Physics, Fluids & Plasmas

WOS:000589660100002

AMER INST PHYSICS

20204609489322

Forecasting ; Turbulence ; Neural networks ; Velocity

Fluid Flow:631 ; Mathematics:921

PHYSICS

2-s2.0-85095865661

Scopus

Guangdong Provincial Key Laboratory of Turbulence Research and Applications,Center for Complex Flows and Soft Matter Research,Department of Mechanics and Aerospace Engineering,Southern University of Science and Technology,Shenzhen,518055,China

Yuan，Zelong,Xie，Chenyue,Wang，Jianchun. Deconvolutional artificial neural network models for large eddy simulation of turbulence[J]. PHYSICS OF FLUIDS,2020,32(11).

Yuan，Zelong,Xie，Chenyue,&Wang，Jianchun.(2020).Deconvolutional artificial neural network models for large eddy simulation of turbulence.PHYSICS OF FLUIDS,32(11).

Yuan，Zelong,et al."Deconvolutional artificial neural network models for large eddy simulation of turbulence".PHYSICS OF FLUIDS 32.11(2020).