Machine Learning in 3D DEM Revolution!

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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.


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