Subgrid-scale stress model for large-eddy simulation of turbulence using an artificial neural network

Subgrid-scale (SGS) stress modeling based on filtered variables is one of the crucial scientific challenges in large-eddy simulation. With the rapid development of machine learning technologies in recent years, data-driven turbulence modeling methods have gained its popularity. In this study, an SGS stress model based on artificial neural network (ANN), with strain-rate tensor and modified Leonard tensor as inputs, is developed for incompressible isotropic homogeneous turbulence. The proposed ANN model demonstrates a substantial enhancement in the prediction of the SGS stress. Also, the ANN model could provide better predictions of turbulence statistics, as compared to the traditional models. It is suggested that the ANN methods exhibit obvious advantages and considerable potentials for the development of turbulence models with high accuracy.