A GPU-based DEM for modelling large scale powder compaction with wide size distributions

Abstract In the present study, we developed a GPU-based discrete element method (DEM) to tackle the challenges associated with modelling powder compaction, in particular large scale systems with wide size distributions. In the model, a multi-grid searching method specifically designed within the GPU architecture was proposed for particle neighbour searching. A memory layout was designed to ensure coalesced memory access for neighbour list and associated contact history. The proposed GPU implementation was able to achieve a three-level parallelism, from single GPU to GPUs within a computing node and to GPUs across nodes. The model was applied to powder compaction and the simulation results showed significant gain in computational efficiency and reliable prediction of the compaction behaviour.

[1]  A. Yu,et al.  Discrete particle simulation of particulate systems: Theoretical developments , 2007 .

[2]  A. Soroush,et al.  DEM simulation of reverse faulting through sands with the aid of GPU computing , 2015 .

[3]  Moncho Gómez-Gesteira,et al.  New multi-GPU implementation for smoothed particle hydrodynamics on heterogeneous clusters , 2013, Comput. Phys. Commun..

[4]  T. Iwai,et al.  FAST PARTICLE PAIR DETECTION ALGORITHMS FOR PARTICLE SIMULATIONS , 1999 .

[5]  Christine M. Hrenya,et al.  Challenges of DEM: II. Wide particle size distributions , 2014 .

[6]  Paul Zulli,et al.  Coordination number of binary mixtures of spheres , 1998 .

[7]  J. D. BERNAL,et al.  Packing of Spheres: Co-ordination of Randomly Packed Spheres , 1960, Nature.

[8]  Jin Y. Ooi,et al.  Micromechanical analysis of cohesive granular materials using the discrete element method with an adhesive elasto-plastic contact model , 2014, Granular Matter.

[9]  Jingwei Zheng,et al.  GPU-based parallel algorithm for particle contact detection and its application in self-compacting concrete flow simulations , 2012 .

[10]  Jin Y. Ooi,et al.  DEM modeling of cone penetration and unconfined compression in cohesive solids , 2016 .

[11]  Shubo Chen,et al.  Parallel multilayer particle collision detection method based on performance estimation , 2018, Cluster Computing.

[12]  K. Malone,et al.  Determination of contact parameters for discrete element method simulations of granular systems , 2008 .

[13]  J. Ooi,et al.  Experiments and simulations of direct shear tests: porosity, contact friction and bulk friction , 2008 .

[14]  Runyu Yang,et al.  Computer simulation of the packing of fine particles , 2000, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[15]  Colin Thornton,et al.  Numerical studies of uniaxial powder compaction process by 3D DEM , 2004 .

[16]  T Pöschel,et al.  Scaling properties of granular materials. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

[17]  Yoshiyuki Shirakawa,et al.  Optimum Cell Condition for Contact Detection Having a Large Particle Size Ratio in the Discrete Element Method , 2006 .

[18]  M. Molenda,et al.  Representative elementary volume analysis of polydisperse granular packings using discrete element method , 2016 .

[19]  Kejing He,et al.  Multigrid contact detection method. , 2007, Physical review. E, Statistical, nonlinear, and soft matter physics.

[20]  Xin Huang,et al.  Effect of sample size on the response of DEM samples with a realistic grading , 2014 .

[21]  Jin Y. Ooi,et al.  A bond model for DEM simulation of cementitious materials and deformable structures , 2014 .

[22]  Vitaliy Ogarko,et al.  A fast multilevel algorithm for contact detection of arbitrarily polydisperse objects , 2012, Comput. Phys. Commun..

[23]  Daniel N. Wilke,et al.  Discrete element simulation of mill charge in 3D using the BLAZE-DEM GPU framework , 2015 .

[24]  S. Thakur,et al.  An experimental and numerical study of packing, compression, and caking behaviour of detergent powders , 2014 .

[25]  Wei Ge,et al.  Quasi-real-time simulation of rotating drum using discrete element method with parallel GPU computing , 2011 .

[26]  Chaofeng Hou,et al.  GPU-accelerated molecular dynamics simulation of solid covalent crystals , 2012 .

[27]  Torsten Kraft,et al.  Three-dimensional discrete element models for the granular statics and dynamics of powders in cavity filling , 2009 .

[28]  Wei Ge,et al.  A two-fluid smoothed particle hydrodynamics (TF-SPH) method for gas–solid fluidization , 2013 .

[29]  Vitaliy Ogarko,et al.  Optimal parameters for a hierarchical grid data structure for contact detection in arbitrarily polydisperse particle systems , 2014, CPM 2014.

[30]  Runyu Yang,et al.  Discrete particle simulation of particulate systems: A review of major applications and findings , 2008 .

[31]  Runyu Yang,et al.  DEM study of the mechanical strength of iron ore compacts , 2015 .

[32]  Aibing Yu,et al.  The packing of spheres in a cylindrical container: the thickness effect , 1995 .

[33]  Runyu Yang,et al.  DEM investigation of the role of friction in mechanical response of powder compact , 2017 .

[34]  Bruno C. Hancock,et al.  Numerical and experimental investigation of capping mechanisms during pharmaceutical tablet compaction , 2008 .

[35]  Aibing Yu,et al.  A GPU-based DEM approach for modelling of particulate systems , 2016 .

[36]  Christine M. Hrenya,et al.  Challenges of DEM: I. Competing bottlenecks in parallelization of gas–solid flows , 2014 .

[37]  Jin Y. Ooi,et al.  SCALING OF DISCRETE ELEMENT MODEL PARAMETERS FOR COHESIONLESS AND COHESIVE SOLID , 2015, 1506.00439.

[38]  Johannes Khinast,et al.  Large-scale CFD–DEM simulations of fluidized granular systems , 2013 .

[39]  Christian Obrecht,et al.  LBM based flow simulation using GPU computing processor , 2010, Comput. Math. Appl..