Despite well-known limitations of Reynolds-averaged Navier–Stokes (RANS) simulations, this methodology remains the most widely used tool for predicting many turbulent flows due to computational efficiency. Machine learning is a promising approach improve accuracy RANS simulations. One major area improvement using machine models represent complex relationship between mean flow field gradients an...

Extensive experimental evidence highlights that scalar turbulence exhibits anomalous diffusion and stronger intermittency levels at small scales compared to in fluid turbulence. This renders the corresponding subgrid-scale dynamics modeling for a greater challenge date. We develop new large eddy simulation (LES) paradigm efficiently dynamically nonlocal LES of To this end, we formulate underlyi...

Abstract Data-driven turbulence modeling is experiencing a surge in interest following algorithmic and hardware developments the data sciences. We discuss an approach using differentiable physics paradigm that combines known with machine learning to develop closure models for Burgers’ turbulence. consider one-dimensional Burgers system as prototypical test problem unresolved terms advection-dom...