Artificial neural network-based subgrid-scale models for LES of compressible turbulent channel flow

Meng，Qingjia1; Jiang，Zhou1; Wang，Jianchun2

2022

10.1016/j.taml.2022.100399

Theoretical and Applied Mechanics Letters   Impact Factor & Quartile

2095-0349

2095-0349

Fully connected neural networks (FCNNs) have been developed for the closure of subgrid-scale (SGS) stress and SGS heat flux in large-eddy simulations of compressible turbulent channel flow. The FCNN-based SGS model trained using data with Mach number Ma=3.0 and Reynolds number Re=3000 was applied to situations with different Mach numbers and Reynolds numbers. The input variables of the neural network model were the filtered velocity gradients and temperature gradients at a single spatial grid point. The a priori test showed that the FCNN model had a correlation coefficient larger than 0.91 and a relative error smaller than 0.43, with much better reconstructions of SGS unclosed terms than the dynamic Smagorinsky model (DSM). In a posteriori test, the behavior of the FCNN model was marginally better than that of the DSM in predicting the mean velocity profiles, mean temperature profiles, turbulent intensities, total Reynolds stress, total Reynolds heat flux, and mean SGS flux of kinetic energy, and outperformed the Smagorinsky model.

Compressible turbulent channel flow Fully connected neural network model Large eddy simulation

ESCI

English

Others

National Natural Science Foundation of China[11702042];National Natural Science Foundation of China[91952104];

Mechanics

Mechanics

WOS:000933047000001

ELSEVIER

2-s2.0-85142857021

Scopus

1.College of Aerospace Engineering,Chongqing University,Chongqing,174 Shazheng Street,400044,China
2.Department of Mechanics and Aerospace Engineering,Southern University of Science and Technology,Shenzhen,Guangdong,518055,China

Meng，Qingjia,Jiang，Zhou,Wang，Jianchun. Artificial neural network-based subgrid-scale models for LES of compressible turbulent channel flow[J]. Theoretical and Applied Mechanics Letters,2022,13(1).

Meng，Qingjia,Jiang，Zhou,&Wang，Jianchun.(2022).Artificial neural network-based subgrid-scale models for LES of compressible turbulent channel flow.Theoretical and Applied Mechanics Letters,13(1).

Meng，Qingjia,et al."Artificial neural network-based subgrid-scale models for LES of compressible turbulent channel flow".Theoretical and Applied Mechanics Letters 13.1(2022).