Modeling fluid–particle interaction in dilute-phase turbulent liquid–particle flow simulation--颗粒学报PARTICUOLOGY

Hadinoto, K., & Chew, J. W. (2010). Modeling fluid–particle interaction in dilute-phase turbulent liquid–particle flow simulation. Particuology, 8(2), 150-160. https://doi.org/10.1016/j.partic.2009.07.005

Modeling fluid–particle interaction in dilute-phase turbulent liquid–particle flow simulation

Kunn Hadinoto ^a *, Jia Wei Chew ^b

a School of Chemical and Biomedical Engineering, Nanyang Technological University, Singapore 637459, Singapore
b Department of Chemical and Biological Engineering, University of Colorado, Boulder, CO 80309, USA

10.1016/j.partic.2009.07.005

Volume 8, Issue 2, April 2010, Pages 150-160

Received 6 April 2009, Accepted 3 July 2009, Available online 21 December 2009.

E-mail: kunnong@ntu.edu.sg

Highlights

Abstract

The present work examines the predictive capability of a two-fluid CFD model that is based on the kinetic theory of granular flow in simulating dilute-phase turbulent liquid–particle pipe flows in which the interstitial fluid effect on the particle fluctuating motion is significant. The impacts of employing different drag correlations and turbulence closure models to describe the fluid–particle interactions (i.e. drag force and long-range interaction) are examined at both the mean and fluctuating velocity levels. The model predictions are validated using experimental data of turbulent liquid–particle flows in a vertical pipe at different particle Reynolds numbers (Re_P > 400 and Re_P < 400), which characterize the importance of the vortex shedding phenomenon in the fluid-phase turbulence modulation. The results indicate that (1) the fluctuating velocity level predictions at different Re_P are highly sensitive to the drag correlation selection and (2) different turbulence closure models must be employed to accurately describe the long-range fluid–particle interaction in each phase. In general, good agreement is found between the model predictions and the experimental data at both the mean and fluctuating velocity levels provided that appropriate combinations of the drag correlation and the turbulence closure model are selected depending on Re_P.

Graphical abstract

Keywords

Slurry flow; Liquid–particle flow; Kinetic theory of granular flow; Drag correlation; Turbulence

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