Coupling particle image velocimetry and digital image analysis to characterize cluster dynamics in a fast fluidized bed

Abstract

Particle clusters affect interphase interaction and dominate the overall performance of gas-solid fluidized beds. In this work, the particle image velocimetry, particle tracking velocimetry and digital image analysis were coupled to characterize clustering dynamics in a lab-scale riser. The clusters were identified by the Otsu algorithm based on grayscale distribution, and the cluster properties such as equivalent size, solids concentration and velocity distribution were extracted and analyzed under various operating conditions. It was found macroscopic operating conditions affect cluster characteristics significantly whereas the effect of particle composition is secondary. A bimodal or skewed probability density function was observed for not only the time-averaged hydrodynamic velocity but also the particle velocity around the cluster interface, indicating the coexistence of dilute and dense phases and the breaking of equilibrium assumption. The nonuniform distributions for laminar and Reynolds-stress-like granular temperatures were further discussed, which show remarkable anisotropy at both the microscopic and mesoscopic scales.