Computing with non-convex polyhedra on the GPU

We recently introduced Blaze-DEMGPU, a GPU based computing framework for convex polyhedral shaped particles (Govender et al. Appl. Math. Comp. 267, 810–829, 2015). The computing framework was validated against numerous industrial applications that include particulate discharge and estimating power draw for a ball mill in comminution applications. In this study we extend the computing capabilities of the convex polyhedral Blaze-DEMGPU computing platform to include non-convex polyhedral particles. We follow a similar philosophy to the well known clumping, clustering or fusing of spheres (Chong et al. Gran. Mat. 17, 377–387, 2015), but instead we fuse convex polyhedral particles. This allows for fused or super polyhedral particles that constitute effective physical properties for the fused particle e.g. the inertia tensor. The major benefit of fused polyhedral particles as opposed to clustered spherical particles is that the number of particles required to fuse fairly complex particle shapes is small. In addition, numerous decompositions exist to exactly decompose a non-convex particle in a number of convex particles. The main complexity of non-convex polyhedral particles is to resolve contact effectively and efficiently on the GPU. In this paper we outline our approach.