Consistent Subgrid-scale Model Development for Large-eddy Simulation with Data-driven Sparse Identification

This study aims to model the subgrid-scale (SGS) stress and numerical error terms for large-eddy simulations (LES) applied to forced isotropic turbulence. Training data for the SGS and numerical error terms are obtained using a hybrid approach combining direct numerical simulation (DNS) and LES, known as DNS-aided LES. The model utilizes sparse regression to represent the SGS term as a linear combination of invariant tensors. To enhance accuracy, a neural network is trained on the residuals, including the numerical error term, compensating for filtering, discretization, and commutation errors. Results from both a priori and a posteriori testing of the model are presented to validate its efficacy.