CFD-DEM study on mixing and segregation characteristics for binary column-shape particles in a liquid-solid fluidized bed

In this paper, the Computational Fluid Dynamics-Discrete Element Method (CFD-DEM) is applied to investigate the mixing and segregation of binary cylinders with different densities in a threedimensional liquid-solid fluidized bed. The column-shape particles are constructed with a superquadric model. The simulation results are in good agreement with the previous experimental data. The influences of aspect ratio and density ratio of binary particles on the mixing quality and final segregation extent are examined. Simulation results show that light particles are inclined to occupy at the top of the granular bed, whereas heavy ones concentrate in the bottom. As particle aspect ratio deviates from unity, i.e. disc-like or rod-like particles, the mixing index is increased and segregation degree is decreased. Particle translation is the main movement mode, however, disc- or rod-like particles are more easily to rotate and need more energy to be driven, comparing with approximately spherical particles. The force analysis in terms of the contact force and drag force indicates that particle shape influences collision probability, resulting in greater contact forces in terms of the disc-like and rod-like particles. Compared to disc-like and rod-like particles, approximately spherical particles experience the least drag force, thereby inhibiting mixing between binary particles. The particle orientation of disc-like and rodlike particles is inclined to the vertical direction in the fluidized state. Furthermore, particle dispersion coefficients are in the range of 10-3 to 10-2 m2 /s, whose vertical direction is one order of magnitude greater than the horizontal directions. Smaller density ratios lead to larger particle dispersion coefficients. (c) 2025 The Society of Powder Technology Japan. Published by Elsevier BV and The Society of Powder Technology Japan. All rights are reserved, including those for text and data mining, AI training, and