Fluidized bed CFD using simplified solid-phase coupling.

Abstract

The performance of a bubbling fluidized bed largely depends on the dynamics of solids. Therefore, it is very important to predict the bubble size and shape for modelling the degree of mixing of solid particles. The complicated non-linear characteristics of a bubbling fluidized bed make it difficult to study and model the hydrodynamic behaviour. Hence, designing of a bubbling fluidized bed is often limited to the calculation of mass balance and estimation of bed pressure drop. The aim of the present work is to evaluate a new simplified multiphase Computational Fluid Dynamics (CFD) approach for studying the hydrodynamics of a gas-solid bubbling fluidized bed. In this work, the viability of a simplified coupling of solid-phase with the gas phase over the traditional multiphase approach has been demonstrated. The available traditional multiphase CFD technique is not suitable for capturing the particulate phase in the dense stagnant regions of fluidized bed. This is because, under the traditional CFD multiphase process, solid phase is treated like a fluid. Shear stress is higher when fluid moves and zero when it is stagnant. In contrast, friction force is maximum when solid particles are stagnant. Therefore, a suitable approach is necessary to model the stagnant particulate phase in the calculation domain. In the present simplified coupling process, mass fluxes for the solid phase are obtained explicitly from the solid phase momentum equation and solid pressure is calculated from the solid phase continuity equation. These solid phase flow parameters are incorporated into the commercial CFD software single-phase gas flow by writing user-defined subroutines. The simplified coupling approach is validated against the available experimental digital images and the traditional CFD modelling results of the fluidized bed. The comparison shows, the available traditional multiphase model predicts less realistic or sometimes unrealistic results while the present numerical model captures more realistic results.