A dynamic recursive neural-network-based subgrid-scale model for large eddy simulation

One approach to developing a subgrid-scale (SGS) model for large eddy simulation involves obtaining the SGS stresses and resolved flow variables from filtered direct numerical simulation (fDNS) data and inserting them into a neural network (NN). However, neural networks suffer from the problem of making arbitrary predictions, when the input data locate outside the scope of the training data (extrapolation issue). As a result, a trained NN-based SGS model shows poor performance, when it is applied to trained flow at much higher Reynolds number or with different grid sizes, and to untrained flows having different flow topologies. To overcome these difficulties, we first develop a recursive procedure to simulate high Reynolds number flow and then adopt a dynamic approach to accommodate different flow topologies. The present neural network is trained only from forced homogenous isotropic turbulence. We apply the dynamic recursive NN-based SGS model to turbulent channel flow and other complex flows. The results are comparable to those of traditional SGS models.