A GPU-based discrete element modeling code and its application in die filling

Abstract In this study, parallelization of a Discrete Element Method (DEM) code titled Trubal was carried out based on the CPU–GPU heterogeneous architecture where both two- and three-dimensional cases were assessed. In Trubal, the particle–particle/wall interaction rules are governed by the theoretical contact mechanics which enable the direct use of real physical material properties in the calculation. We reconstructed Trubal in two steps: reconstruction of the static storage structure; essential parallelism on the relative newer version code. Numerical simulations were implemented to present the benefits of this research. Firstly, two simulations of die filling with a moving shoe involving 6000 and 60,000 two-dimensional particles were conducted under (i) NVIDIA Tesla C2050 card together with Intel Core-Duo 2.93 GHz CPU and (ii) NVIDIA Tesla K40c card along with Intel Xeon 3.00 GHz CPU. Average speedups of (i) 4.69 and 12.78 as well as (ii) 6.52 and 18.60 in computational time were obtained, respectively. Then, a simulation of die filling with a stationary shoe containing 20,000 three-dimensional particles was carried out under the same conditions where average speedups of (i) 12.90 and (ii) 19.66 in computational time were obtained, respectively. It is shown that the final version parallel code gave a substantial acceleration on the original Trubal.

[1]  J. Kuipers,et al.  Discrete particle simulation of bubble and slug formation in a two-dimensional gas-fluidised bed: A hard-sphere approach. , 1996 .

[2]  C. Thornton,et al.  A theoretical model for the stick/bounce behaviour of adhesive, elastic-plastic spheres , 1998 .

[3]  Hao Zhang,et al.  A Numerical Simulation of Motion of Particles under the Wafer in CMP , 2008, 2008 International Conference on Computer Science and Software Engineering.

[4]  F. Xavier Trias,et al.  PIBM: Particulate immersed boundary method for fluid–particle interaction problems , 2014, 1407.6704.

[5]  Chuan-Yu Wu,et al.  Modelling of the flow of cohesive powders during pharmaceutical tabletting , 2010 .

[6]  Colin Thornton,et al.  The effects of air and particle density difference on segregation of powder mixtures during die filling , 2011 .

[7]  Niels G. Deen,et al.  Parallelization of an Euler-Lagrange model using mixed domain decomposition and a mirror domain technique: Application to dispersed gas-liquid two-phase flow , 2006, J. Comput. Phys..

[8]  Aibing Yu,et al.  Linking discrete particle simulation to continuum process modelling for granular matter: Theory and application , 2011 .

[9]  Kenli Li,et al.  Accelerating solidification process simulation for large-sized system of liquid metal atoms using GPU with CUDA , 2014, J. Comput. Phys..

[10]  Jay N. Meegoda,et al.  Micro‐mechanical simulation of geotechnical problems using massively parallel computers , 2003 .

[11]  Chuan-Yu Wu,et al.  DEM simulations of die filling during pharmaceutical tabletting , 2008 .

[12]  Colin Thornton,et al.  Impact of elastic spheres with and without adhesion , 1991 .

[13]  Hao Zhang,et al.  Parallelization of a DEM Code Based on CPU-GPU Heterogeneous Architecture , 2013, ParCo 2013.

[14]  R. D. Mindlin.  Elastic Spheres in Contact Under Varying Oblique Forces , 1953 .

[15]  Mikio Sakai,et al.  Parallel computing of discrete element method on multi-core processors , 2011 .

[16]  Kenli Li,et al.  Entropic Lattice Boltzmann Method based high Reynolds number flow simulation using CUDA on GPU , 2013 .

[17]  Colin Thornton,et al.  Parallelization of a Lagrangian–Eulerian DEM/CFD code for application to fluidized beds , 2011 .

[18]  F. X. Trias,et al.  Numerical investigation of the location of maximum erosive wear damage in elbow: Effect of slurry velocity, bend orientation and angle of elbow , 2012 .

[19]  Chun Feng,et al.  A GPU Accelerated Continuous-Based Discrete Element Method for Elastodynamics Analysis , 2011 .

[20]  Kenli Li,et al.  Parallel computation of Entropic Lattice Boltzmann method on hybrid CPU–GPU accelerated system , 2015 .

[21]  Yuanqiang Tan,et al.  Numerical simulation of concrete pumping process and investigation of wear mechanism of the piping wall , 2011 .

[22]  F. X. Trias,et al.  Numerical investigation on the role of discrete element method in combined LBM–IBM–DEM modeling , 2014 .

[23]  Asensio Oliva Llena,et al.  Parallelization of a DEM/CFD code for the numerical simulation of particle-laden turbulent flows , 2011 .

[24]  Jinchao Xu,et al.  Numerical Study of Geometric Multigrid Methods on CPU--GPU Heterogeneous Computers , 2012, ArXiv.

[25]  A. Oliva,et al.  Effect of collisions on the particle behavior in a turbulent square duct flow , 2015 .

[26]  Yuanqiang Tan,et al.  Numerical study of concrete mixing transport process and mixing mechanism of truck mixer , 2015 .

[27]  Algirdas Maknickas,et al.  Parallel DEM Software for Simulation of Granular Media , 2006, Informatica.

[28]  Jian Ye,et al.  Modeling and Rendering of Real-time Large-scale Granular Flow Scene on GPU , 2011 .

[29]  P. Cundall.  A computer model for simulating progressive, large-scale movements in blocky rock systems , 1971 .

[30]  Colin Thornton,et al.  3D DEM/CFD analysis of size-induced segregation during die filling , 2011 .

[31]  Chuan-Yu Wu,et al.  Numerical modelling of suction filling using DEM/CFD , 2012 .

[32]  Benjamin J. Glasser,et al.  Large-scale powder mixer simulations using massively parallel GPUarchitectures , 2010 .

[33]  Wei Ge,et al.  GPU-based discrete element simulation on a tote blender for performance improvement , 2013 .