Predicting turbulence modulations at different Reynolds numbers in dilute-phase turbulent liquid–particle flow simulations

The present work examines the predictive capability of two-fluid CFD model based on the kinetic theory of granular flow in capturing the Reynolds number (Re) dependence of fluid-phase turbulence modulations in dilute-phase turbulent liquid–particle flows. The model predictions are examined using turbulent liquid–particle flow data in a vertical pipe at Re=17,000, 48,000, 65,000, and 76,000 in the particle concentration range of between 0.5% and 4.0% (v/v). The experimental data indicate that the fluid-phase turbulence intensities are enhanced with respect to the single-phase flow at Re≤48,000 but are attenuated at Re≥65,000. The simulation results indicate that the CFD model can successfully predict the turbulence modulations at Re=17,000, 65,000, and 76,000 both qualitatively and quantitatively, but not at the intermediate Re of 48,000. In this regard, (1) different drag correlations to describe the fluctuating drag force are needed to accurately predict the trends in the turbulence modulations as a function of Re, and (2) appropriate combinations of the drag correlations and turbulence closure models to describe the long-range fluid–particle interactions must be identified in each phase at different Re in order to accurately predict the turbulence modulation, granular temperature, and particle radial concentration profile.