Sparse identification of multiphase turbulence closures for coupled fluid-particle flows

In this work, model closures of the multiphase Reynolds-averaged Navier-Stokes (RANS) equations are developed for homogeneous, fully developed gas-particle flows. 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. In the present study, particles settle under gravity in an unbounded viscous fluid. At sufficient mass loadings, interphase momentum exchange between the phases results in the spontaneous generation of particle clusters that sustain velocity fluctuations in the fluid. Data generated from Eulerian-Lagrangian simulations are used in a sparse regression method for model closure that ensures form invariance. Particular attention is paid to modelling the unclosed terms unique to the multiphase RANS equations (drag production, drag exchange, pressure strain and viscous dissipation). A minimal set of tensors is presented that serve as the basis for modelling. It is found that sparse regression identifies compact, algebraic models that are accurate across flow conditions and robust to sparse training data.

Automated summary

In this study, model closures for multiphase Reynolds-averaged Navier-Stokes (RANS) equations are developed using sparse regression and Eulerian-Lagrangian simulations to ensure accuracy and robustness of the models across different flow conditions. The focus is on capturing the dynamics of gas-particle flows, particularly the generation of particle clusters and interphase momentum exchange, in a compact and algebraic manner.