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, due to the limitation of neural networks in extrapolation, previous NN-based SGS models were unable to be applied to untrained flows. To overcome this drawback, we have devised a recursive NN-based SGS model. This model is trained with forced homogenous isotropic turbulent (HIT) flows only, yet it demonstrates satisfactory results in turbulence statistics when applied to decaying HIT flows. Additionally, a dynamic approach akin to that of Germano et al. (1991) is implemented in this recursive NN-based SGS model to ensure that the SGS stresses are adequately reduced in laminar and near-wall turbulent flows. To assess the performance of the present SGS model, LESs of 3D Taylor-Green vortex flow, turbulent channel flow at Re τ =178 and turbulent boundary layer flow at Re θ =1410 are conducted. The results show that our dynamic recursive NN-based SGS model can accurately predict the turbulence statistics of these flows, despite being trained solely with forced HIT flows.