A novel hybrid contact detection algorithm for 2D FDEM: Balancing efficiency and memory consumption

In this paper, a novel hybrid contact detection algorithm, AGS (Adaptive grid-based search) & GJK (Gilbert-Johnson-Keerthi), is proposed to accelerate 2D FDEM (Combined finite-discrete element method) simulations. AGS algorithm maintains computational efficiency comparable to traditional broad search methods based on uniform grid decomposition, while significantly reducing memory consumption by utilizing only effective grid cells in identifying potential contact pairs. Additionally, a modified GJK algorithm with specific initial search direction is employed for contact resolution, which is not only easy to implement but also more efficient than SAT (Separation Axis Theorem). Compared·to existing FDEM algorithms like NBS (Non-binary Search) and GGS (Global Grid-based Search) & SAT (Separation Axis Theorem), the proposed algorithm effectively overcomes their limitations, offering high computational efficiency, low memory consumption, and reduced sensitivity to mesh size. The effectiveness of the proposed algorithm is validated through multiple numerical cases, demonstrating its suitability for both quasi-static and dynamic simulations. Notably, in the numerical cases examined, the algorithm achieves a speed-up ratio of 2.76 at case scale compared to NBS, and saves 37.8 % in memory consumption compared to GGS & SAT. Furthermore, the algorithm is highly versatile and can potentially be extended to DEM (Discrete Element Method) applications involving other convex-shaped particles. © 2025 Elsevier Ltd