Estimation of particle dynamics in 2-D fluidized beds using particle tracking velocimetry |
| |
| Affiliation: | 1. Key Laboratory of Energy Thermal Conversion and Control of Ministry of Education, College of Energy and Environmental Engineering, Southeast University, Nanjing, 210096, China;2. Laboratory for Simulation and Modelling of Particulate Systems, Department of Chemical Engineering, Monash University, Clayton, VIC 3800, Australia;3. National Engineering Laboratory for MTO, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China;1. Oak Ridge Institute for Science and Education, 3610 Collins Ferry Road, Morgantown, WV 26505, United States;2. National Energy Technology Laboratory, U.S. Department of Energy, 3610 Collins Ferry Road, Morgantown, WV 26505, United States;1. Institute of Particle Technology, University of Erlangen-Nuremberg, Cauerstraße 4, D-91058 Erlangen, Germany;2. Institute for Multiscale Simulation, University of Erlangen-Nuremberg, Nägelsbachstraße 49b, D-91052 Erlangen, Germany;1. Chalmers University of Technology, Dept. of Energy and Environment, SE-412 96 Göteborg, Sweden;2. Acro Swedish ICT AB, Arvid Hedvalls backe 4, SE-411 33 Göteborg, Sweden |
| |
| Abstract: | 
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 flows. 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. |
| |
| Keywords: | Particle dynamics Particle tracking velocimetry Pseudo-2D Fluidized bed |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
