Sparse identification of multiphase turbulence closures for strongly-coupled gas-particle flows

In this talk, we will present a data-driven framework for model closure of the multiphase Reynolds Average Navier—Stokes (RANS) equations. To date, the majority of RANS closures are based on extensions of single-phase turbulence models, which fail to capture complex two-phase flow dynamics across dilute and dense regimes, especially when two-way coupling between the phases is important. This eliminates the augmentation of existing models as an option for solving the multiphase closure problem. We will focus on gas-solid flows at moderate volume fractions and Reynolds numbers, such that strong coupling between the phases gives rise to large-scale heterogeneity (clusters) that drive the underlying turbulence. Data generated from highly resolved simulations are used in a sparse regression method for model closure that ensures form invariance. We will demonstrate how the sparse regression methodology identifies compact, algebraic models from large-scale simulation data.