On the prediction of particle collision behavior in coarse-grained and resolved systems

Abstract

The discrete element method (DEM) simulates granular processes and detects inter-particle collisions during the simulation. Detection of collision helps researchers to study the occurrence of particulate mechanisms such as aggregation, breakage, etc. DEM demands high computational costs in simulating industrial-level systems, as it involves an enormous number of particles. DEM coarse-grained model can help to overcome this high computational cost issue. However, the frequency and probability of collisions for different particle size classes may change when coarser particles are introduced. This study introduces a new mathematical formulation, namely the collision dependency function (CDF), which predicts the probability of collisions between different particle classes for systems containing resolved and coarse-grained particles. The CDF is extracted by executing one DEM simulation consisting of number-based uniformly distributed particles. Furthermore, a new optimized scheme is used inside the DEM to store the collision data efficiently. Finally, the collision probabilities between size classes obtained from DEM simulations are compared successfully against their counterparts calculated from the developed model for verification.

Keywords:

• Discrete element method
• coarse grain
• collision detection
• collision probability