Artificial neural network model for large-eddy simulation of compressible turbulence

XIE Chenyue; WANG Jianchun; WAN Minping; CHEN Shiyi

doi:10.7527/S1000-6893.2021.25723

ACTA AERONAUTICAET ASTRONAUTICA SINICA >

2021 , Vol. 42 >Issue 9: 625723 - 625723

DOI: https://doi.org/10.7527/S1000-6893.2021.25723

Special Topic of NNW Progress and Application

Artificial neural network model for large-eddy simulation of compressible turbulence

XIE Chenyue ,
WANG Jianchun ,
WAN Minping ,
CHEN Shiyi

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1. Department of Mechanics and Aerospace Engineering, College of Engineering, Southern University of Science and Technology, Shenzhen 518055, China;
2. Guangdong-Hong Kong-Macao Joint Laboratory for Data-Driven Fluid Mechanics and Engineering Applications, Southern University of Science and Technology, Shenzhen 518055, China

Received date: 2021-03-30

Revised date: 2021-04-27

Online published: 2021-05-20

Supported by

National Numerical Windtunnel Project; National Natural Science Foundation of China (91952104, 92052301, 91752201); Project of Department of Science and Technology of Guangdong Province (2020B1212030001); Project of Technology and Innovation Commission of Shenzhen Municipality (KQTD20180411143441009); Project of Center for Computational Science and Engineering of Southern University of Science and Technology

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Abstract

The Spatial Artificial Neural Network (SANN) model is applied to perform Large Eddy Simulations (LES) of highly compressible turbulence at high turbulent Mach numbers of 0.6, 0.8 and 1.0 under the National Numerical Windtunnel (NNW) Project. In our previous studies, we developed the SANN model for incompressible and weakly compressible turbulence based on multi-scale spatial structures of turbulence. However, generations of shock waves in highly compressible turbulence pose great challenges to LES. This paper discusses the applicability of the SANN models for LES of highly compressible turbulence. It has been demonstrated that the correlation coefficients of the SANN model can be larger than 0.995. The relative errors of the SANN model can be smaller than 11%, which are much smaller than those of the traditional gradient model and the approximate deconvolution model in an a priori analysis for highly compressible turbulence. In an a posteriori analysis, we make a comparison of the results of the SANN model, the Implicit Large Eddy Simulation (ILES), the Dynamic Smagorinsky Model (DSM) and the Dynamic Mixed Model (DMM). It is shown that the SANN model performs better in the prediction of spectra and statistical properties of velocity and temperature, and instantaneous flow structures for highly compressible turbulence. The artificial neural network model with consideration of spatial features can deepen our understanding of subgrid-scale modeling for LES of highly compressible turbulence. At the same time, the model can contribute to the construction of the turbulence models of the NNW Project.

Key words： compressible turbulence; large eddy simulation; artificial neural network; turbulence model; National Numerical Windtunnel (NNW) Project

Cite this article

XIE Chenyue , WANG Jianchun , WAN Minping , CHEN Shiyi . Artificial neural network model for large-eddy simulation of compressible turbulence[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2021 , 42(9) : 625723 -625723 . DOI: 10.7527/S1000-6893.2021.25723

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