Parallel DEM Software for Simulation of Granular Media

Abstract

The paper describes the development and performance of parallel algorithms for the discrete element method (DEM) software. Spatial domain decomposition strategy and message passing inter-processor communication have been implemented in the DEMMAT code for simulation of visco-elastic frictional granular media. The novel algorithm combining link-cells for contact detection, the static domain decomposition for parallelization and MPI data transfer for processors exchanging particles has been developed for distributed memory PC clusters. The parallel software DEMMAT_PAR has been applied to model compacting of spherical particles in the rectangular box. Two benchmark problems with different numbers of particles have been solved in order to measure parallel efficiency of the code. The inter-processor communication has been examined in order to improve domain decomposition topology and to achieve better load balancing. The speed-up equal to 11 has been obtained on 16 processors. The parallel performance study has been performed on the PC cluster

Investigation of Normal Contact Interaction Between Two Bonded Spherical Particles With Interface Layer

“…The finite element results are explored to evaluate approximate analytical models. Regarding very fine mesh and three dimensional models we assume that FE results are very accurate and maybe used for evaluation of approximate analytical expressions (4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14). The FE calculations are restricted to the mostly heterogeneous case, where bond between particles is much lower than particles.…”

“…Review of the earlier developments is presented by Džiugys and Peters [2]. Various models of the normal contact are presented by Kruggel-Emdem et al [3], Tomas [4], Maknickas et al [5].…”

Three‐dimensional discrete element method parallel computation of Cauchy stress distribution over granular materials

“…There has been considerable interest in developing and utilizing parallel DEM codes in recent years. [34][35][36][37] For example, Vedachalam and Virdee 38 used LAMMPS (large-scale atomic and molecular massively parallel simulator, developed by Sandia National Laboratories) and LIGGGHTS (LAMMPS improved for general granular and granular heat transfer simulations) to study the motion of snow particles, wherein the snow grains are assumed to be spherical particles 5 mm in diameter. An empirical coefficient of restitution (ratio of rebound velocity to impact velocity) is adopted rather than the strict Hertzian nonlinear contact model, while Mindlin's history-dependent shear model is not considered.…”

Discrete element simulation of particle motion in ball mills based on similarity

“…Powell et al [24] simulated a short slice of the mill thus the number of balls was reduced from 4.5 million to 110 thousand resulting in a reduction of simulation time by at least 50-fold. From the above reference survey, it is found that most of the DEM simulations are very different to the real mill systems and thus it is difficult to use DEM to deal with all the particles in a real mill, even with graphics processing unit (GPU) computing [25][26][27], parallel computing [28,29], continuum approximation [30,31], etc. Modern GPUs are very efficient at manipulating computer graphics and image processing, and their highly parallel structure makes them more efficient than general-purpose CPUs for algorithms where the processing of large blocks of data is done in parallel.…”