A novel GPGPU-parallelized contact detection algorithm for combined finite-discrete element method

Abstract This paper presents a novel algorithm for contact detection for the three-dimensional combined finite-discrete element method (3D FDEM). The contact detection consists of two phases, neighbor search and fine search. Both phases are fully parallelized with Compute Unified Device Architecture (CUDA). In contrast to the non-binary search (NBS) algorithm, the proposed neighbor search algorithm is not sensitive to the element size distribution. To avoid the algorithm being performed at every step, an efficient GPGPU (general purpose graphic processing unit) -parallelized velocity reduction algorithm is employed. For the fine search, the concepts of “positive faces” and “positive edges” are proposed, and the process is considerably simplified and runs faster than the 3D separation axis algorithm. Numerical tests are performed to validate the efficiency and effectiveness of the proposed algorithms. The result shows that for both phases, the computation time is linearly proportional to the number of potential contact pairs regardless of the element size distribution. Furthermore, by using NVIDIA Telsa V100, the resultant overall speed-up ratios of the proposed contact detection algorithms relative to the original Y3D version for uniform and non-uniform element size distributions can reach up to 1982.6 and 13,894.7, respectively. For both quasi-static and dynamic problems, the simulated fracture patterns are in good agreement with the results generated by the NBS algorithm. The proposed methodology can be also employed for 2D FDEM and discrete element method (DEM).