Artificial neural-network-based subgrid-scale model for large-eddy simulation of isotropic turbulence

This study is concerned with accurately predicting the subgrid-scale (SGS) stress using an artificial neural network (ANN) with a linear eddy-viscosity term and a nonlinear term as the input variables. A priori and a posteriori tests are conducted to examine the prediction performance of the ANN-based SGS stress model in decaying homogeneous isotropic turbulence. In a priori test, the present ANN-based SGS model shows high correlation coefficients between the true and predicted SGS stresses, and excellent predictions of the SGS stress and dissipation. In a posteriori test, it is found that the ANN-based SGS model can predict the turbulence statistics more accurately than the traditional dynamic SGS models. The generalization capabilities of the model to untrained flow conditions and unstrained types of turbulent flow have been evaluated. It is found that the proposed ANN-based model can provide an accurate prediction of the SGS stress under different Reynolds numbers and flow types. A comparison among several existing ANN-based models with different input variables is presented, demonstrating a significant advantage of the present model.