Stochastic separated flow models with applications in numerical computations of supersonic particle-laden turbulent flows

Abstract

In this article, three stochastic separated flow models were applied to predict the dispersion of inertial fuel particles in the supersonic turbulent flows. The flow field of continuous phase was simulated by means of Reynolds-averaged Navier–Stokes method with a two-equation turbulence model. Clift’s expression was used to modify the drag force on the particle considering the compressibility effects. The particle-phase statistics were obtained by a secondary-order time-weighed Eulerian method. The ability of those stochastic separated flow models was then compared for predicting the mean particle velocity and the particle dispersion. For obtaining a statistically stationary solution, the stochastic separated flow model required the largest number of computational particles, whereas the improved stochastic separated flow model was found to need the least. The time-series stochastic separation flow model lay in-between. Compared with the other two models, the particle dispersion was over-predicted by the stochastic separated flow model in the supersonic particle-laden boundary layer flow, while the improved stochastic separated flow model was less predictable for the particle spatial distribution in the particle-laden strut-injection flow. Three models could well predict the mean velocities of the particle phase. This study is valuable for selecting a validated model used for predicting the particle dispersion in supersonic turbulent flows.