Predicting the behavior of granules of complex shapes using coarse-grained particles and artificial neural networks

The quality of discrete element simulations is connected to the right choice of material contact parameters. In this work, the usage of artificial neural networks in combination with numerical simulations using coarse-grained shapes to obtain the material contact parameters that replicate the behavior of particles of complex shapes is proposed. Dynamic angle of repose and void fraction are input parameters for the training of an artificial neural network using static and rolling friction as output parameters. The frictional parameters are combined with a coarse-grained shape and successfully replicate the experimental static angle of repose obtained for octahedral and cubic shape particles. The static angle of repose is not involved in the training process. This work demonstrates the capabilities of the artificial neural network to predict contact-equivalent properties for coarse-grained shape in discrete element method, which is commonly adopted in molecular dynamics, but not yet reported for granular media.