A machine learning approach for second moment closure modeling of stably stratified turbulence

We use machine learning (ML) for closure modeling of the Reynolds Averaged Navier Stokes (RANS) equations applied to stably stratified turbulence (SST). SST is strongly affected by fine balances between forces and, in decaying cases, becomes more anisotropic in time. Moreover, there is a limited understanding of the physical phenomena described by some of the terms in the RANS equations. Rather than attempting to model each term separately, it is attractive to see the capability of machine learning to model groups of terms, i.e., to directly model the force balances. We consider decaying SST that is homogeneous and stably stratified by a uniform density gradient, enabling dimensionality reduction. Training data is from Massive Scale Direct Numerical Simulations (MSDNS) with up to 3 trillion grid points in order to span the largest range of buoyancy Reynolds number (Gn) and Froude number (Fr) possible on existing computers. The effectiveness of ML to model flow evolution parameterized in the Gn-Fr space is shown. Furthermore, we demonstrate the capability of the latest high-performance computing architectures utilizing GPUs to distributedly deploy the ML models.