The multiscale-based data-driven subgrid-scale model with physics constraints for enhanced prediction of unresolved scales in turbulent flow

Recent advances in data-driven subgrid-scale (SGS) models are paving the way to capture subfilter-scale fluctuations through deep neural networks (DNN). In this study, we introduce a multi-scale convolutional NN (CNN) -based SGS model that leverages the multi-scale nature of turbulence vortices. This model progressively encodes the features from coarser to finer scales incorporating the energy transfer process between scales. We aim to determine the model’s effectiveness in extracting features of complex turbulent fields to resolve residual stress (Τ_ij ). To enhance predictions, we integrate a physics-constrained DNN. We apply our data-driven SGS model to the large-eddy simulation (LES) while the object of analysis is the turbulence channel. The result of a priori test demonstrates that this model outperforms the conventional CNN-based model in predicting, achieving high correlation coefficients against the label data within different regions: the viscous sublayer, buffer layer, and outer layer. The results highlight the model's proficiency and robustness in resolving scales of motion and residual stress, showcasing its effectiveness in mimicking the energy transfer process of turbulence by this model. The comprehensive a posteriori test will be presented at the conference, highlighting the advancements of this model in actual flow simulations