Estimation of particle dynamics in 2-D fluidized beds using particle tracking velocimetry

作     者：Thomas Hagemeier Christoph Roloff Andreas Buck Evangelos Tsotsas 

作者机构：NaWiTecThermal Process EngineeringOtto-von-Guericke University MagdeburgUniversitaetsplatz 239106 MagdeburgGermany Fluid Dynamics and Technical FlowOtto-von-Guericke University MagdeburgUniversitaetsplatz 239106 MagdeburgGermany 

出 版 物：《Particuology》 (颗粒学报（英文版）)

年 卷 期：2015年第13卷第5期

页      面：39-51页

核心收录：

学科分类：081704[工学-应用化学] 08[工学] 0817[工学-化学工程与技术] 081701[工学-化学工程] 

基　　金：funding of this work by the German Federal Ministry of Science and Education(BMBF) as part of the InnoProfile-Transfer project NaWiTec(03IPT701X) 

主　　题：Particle dynamics Particle tracking velocimetry Pseudo-2D Fluidized bed 

摘      要：The experimental characterization of particle dynamics in fluidized beds is of great importance in fostering an understanding of solid phase motion and its effect on particle properties in granulation processes, Commonly used techniques such as particle image velocimetry rely on the cross-correlation of illumination intensity and averaging procedures. It is not possible to obtain single particle velocities with such techniques. Moreover, the estimated velocities may not accurately represent the local particle velocities in regions with high velocity gradients. Consequently, there is a need for devices and methods that are capable of acquiring individual particle velocities. This paper describes how particle tracking velocimetry can be adapted to dense particulate flows. The approach presented in this paper couples high-speed imaging with an innovative segmentation algorithm for particle detection, and employs the Voronoi method to solve the assignment problem usually encountered in densely seeded fows. Lagrangian particle tracks are obtained as primary information, and these serve as the basis for calculating sophisticated quantities such as the solid-phase flow field, granular temperature, and solid volume fraction. We show that the consistency of individual trajectories is sufficient to recognize collision events.