A neural-network-based mixed model of the subgrid-scale stress for large-eddy simulation of forced isotropic turbulence

An artificial neural network (ANN) is employed to develop an accurate subgrid-scale (SGS) stress model for large-eddy simulation (LES) of forced homogeneous isotropic turbulence. The input variables considered include the filtered strain-rate tensor, the modified Leonard stress tensor, and a combination of them. The data for training the ANN are obtained from a direct numerical simulation of three-dimensional incompressible isotropic turbulence with linear forcing. Both a priori analysis and a posteriori calculation are conducted to evaluate the performance of ANN-based SGS models. It is demonstrated that incorporating the modified Leonard stress tensor into the network architecture significantly improves the predictive performance of ANN-based models. Moreover, the proposed ANN-based mixed SGS model is shown to outperform the traditional dynamic models, such as the dynamic Smagorinsky model, the dynamic Clark model, and the dynamic two-parameter mixed model. In addition, the developed ANN-based mixed model trained using only the database of forced isotropic turbulence performs well in LESs of the transient decaying turbulent flow and the Taylor–Green vortex flow with various Reynolds numbers.