journal article·2019-10

Discrete element simulation of cylindrical particles using super-ellipsoids

Yongzhi Zhao, Lei Xu, Paul B. Umbanhowar, Richard M. Lueptow

Particuology· Volume 46
Publisher page

Abstract

Abstract A discrete element model based on super-ellipsoids was used to simulate cylindrical particle flow. The model can describe a cylindrical particle accurately provided the shape indices of the super-ellipsoids are set to appropriate values. To achieve more rapid calculations, we implemented an “oriented bounding box algorithm” (OBBA) for the initial contact detection of cylindrical particles. Several types of simulations were performed to validate the super-ellipsoid model and the contact-detection algorithm. First, the effect of shape index of the super-ellipsoids on model accuracy was investigated through three simulations: impact of a cylindrical particle on a flat wall, flow of cylindrical particles in a rotating tumbler, and segregation of cylindrical particles of different length flowing down a bounded heap. The simulation results show that the super-ellipsoids describe cylindrical particles accurately when the shape index that specifies the sharpness of the cylinder edges is sufficiently large. The efficiency of the OBBA is measured by simulations in which a box is filled with cylindrical particles and a tumbler that contains cylindrical particles is rotated. The simulation results show that the OBBA can accelerate the calculations significantly. The effect of particle shape (such as aspect ratio and shape index) on the calculation speed was obtained. The simulation of rod-like particles tended to take more calculation time than that of disk-like particles, and the simulation time increased with an increasing particle-shape index.
Source: openaire · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: Granular flow and fluidized beds · Particle Dynamics in Fluid Flows · Fluid Dynamics Simulations and Interactions

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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