Machine-learning-enabled discrete element method: The extension to three dimensions and computational issues

The detection and resolution of contacts among irregular-shaped particles pose significant challenges in the discrete element method (DEM) and recent advancements have introduced a machine learning-enabled approach specifically tailored for contact detection and resolution in two dimensions. Building upon this progress, this paper extends the application of machine learning-enabled DEM to encompass the more complex and realistic three-dimensional (3D) scenario. Particles are modeled using a polyhedral representation with arbitrary shapes, and contact behavior is governed by an energy-conserving contact model based on contact volumes. The efficacy of the machine learning-enabled 3D DEM is evaluated through comparative analyses of computational time and simulation results across individual contact as well as whole DEM simulations against those obtained from the conventional DEM. The findings indicate that the machine learning-enabled approach adeptly identifies and resolves contacts among 3D irregular-shaped particles while accurately reproducing the mechanical characteristics of densely contacting particle assemblies. The computational issues and challenges associated with the machine learning-enabled DEM are also discussed. The study highlights that the machine learning-enabled approach significantly enhances computational efficiency, showcasing its potential to advance complex DEM simulations in a more efficient manner.