SudoDEM: Unleashing the predictive power of the discrete element method on simulation for non-spherical granular particles

Published: 9 November 2020| Version 1 | DOI: 10.17632/brpk4g28zn.1


This paper presents a novel open-source discrete element code, SudoDEM, for efficient modeling of both 2D and 3D non-spherical particles under a GPL v3 or later license. Built upon a popular open-source code YADE, our code inherits the core of a classic DEM framework empowered by OpenMP acceleration, and further offers unique features of a rich library of prime particle shapes, including poly-superellipsoids, superellipsoids, cylinders, cones, polyhedrons for 3D and disks and superellipses for 2D. Unlimited choices of more complex particle shapes can be readily generated by clumping these prime shapes. Efficient modeling of complex shaped particles hinges on contact detection. In SudoDEM, we have developed three generic and efficient contact detection algorithms, the parametric common normal (PCN) algorithm, the Gilbert–Johnson–Keerthi (GJK) algorithm, and the hybrid PCN–GJK algorithm, to handle contacts among complex-shaped particles during a typical DEM simulation. The new DEM code is validated and further showcased by multiple examples, including granular packing, triaxial compression, and landslide, its robustness, efficiency and versatility in providing realistic solutions to granular mechanics problems. The project is hosted at an open-source page at, while the source codes are freely available at a GitHub repository ( We foresee a great capability and potential for SudoDEM in advancing future progress in granular physics and granular mechanics and in fostering advanced simulations of critical engineering and industrial processes pertaining to granular media.



Computational Physics, Granular Material, Discrete Element Method, Polyhedron