A Fast Algorithm for Particle Stacking

Received: 20 March 2019 Accepted: 25 June 2019 The purpose of this study was to investigate the generation of non-overlapping particle sets quickly and accurately that meet specified conditions in the preparatory stage of discrete element simulation. A fast-geometric particle stackinfg algorithm based on geometric and grid search method was proposed for the generation of particle stacking, which is used to generate large particles, then generated small particles and fill them. The results indicated when the velocity of particle generation was too slow, the new position of two or three contacts should be adjusted to accommodate more particles in the current particle diameter range. Moreover, the calculation example showed that under the condition of a specified particle gradation, particle sets with different void ratios could be generated according to the need for discrete element simulation. The time to generate the particle sets is linear with the number of particles, and the algorithm takes less time. The findings of this study may serve as the generation of non-overlapping particle sets for meeting specified conditions in discrete element simulation.

[1]  Stefan Pirker,et al.  Efficient implementation of superquadric particles in Discrete Element Method within an open-source framework , 2017, CPM 2017.

[2]  Katalin Bagi,et al.  An algorithm to generate random dense arrangements for discrete element simulations of granular assemblies , 2005 .

[3]  J. Roux,et al.  3D numerical simulation study of quasistatic grinding process on a model granular material , 2013 .

[4]  Hari S. Viswanathan,et al.  A non-locking composite tetrahedron element for the combined finite discrete element method , 2016 .

[5]  Katalin Bagi,et al.  A quasi-static numerical model for micro-level analysis of granular assemblies , 1993 .

[6]  David R. Owen,et al.  Filling domains with disks: an advancing front approach , 2003 .

[7]  Shengqiang Jiang,et al.  Discrete element simulation of particle motion in ball mills based on similarity , 2018, Powder Technology.

[8]  T. Ng,et al.  A three-dimensional discrete element model using arrays of ellipsoids , 1997 .

[9]  Liu Zhen-yu Tan Jian-rong Cheng Feng-bei Fang Xi-wu.  Algorithm with hybrid method based for sphere packing in two-dimensional region , 2011 .

[10]  Jian Lu,et al.  Geometrical modeling of granular structures in two and three dimensions. Application to nanostructures , 2009 .

[11]  E. Vincens,et al.  Merging criteria for defining pores and constrictions in numerical packing of spheres , 2018, Granular Matter.

[12]  Jian Lu,et al.  Numerical modeling of nanostructured materials , 2010 .

[14]  David R. Owen,et al.  Sphere packing with a geometric based compression algorithm , 2005 .

[15]  Istvan Oldal,et al.  Discrete element modeling of vibrating tillage tools , 2015 .

[16]  J. Roux,et al.  Numerical simulation study of quasistatic grinding process on a model granular material , 2013 .

[17]  A. Munjiza,et al.  NBS contact detection algorithm for bodies of similar size , 1998 .

[18]  Roberto Roselló Valera,et al.  General advancing front packing algorithm for the discrete element method , 2016, Computational Particle Mechanics.

[19]  Random particle packing with large particle size variations using reduced-dimension algorithms , 2006, cond-mat/0606249.

[20]  Valentina Salomoni,et al.  Numerical modelling of ellipsoidal inclusions , 2018 .

[21]  Aibing Yu,et al.  Dynamic Simulation of the Packing of Ellipsoidal Particles , 2011 .

[22]  Amvrossios C. Bagtzoglou,et al.  Three-dimensional discontinuous deformation analysis (3-D DDA) method for particulate media applications , 2012 .

[23]  Hong Zheng,et al.  Dual form of discontinuous deformation analysis , 2016 .

[24]  Thierry Gensane,et al.  Producing dense packings of cubes , 2008, Discret. Math..