Adaptive model reduction for nonsmooth discrete element simulation

TL;DR

This paper introduces an adaptive model order reduction method for nonsmooth discrete element simulations, significantly improving computational efficiency while maintaining accuracy by dynamically simplifying regions of the granular media.

Contribution

It develops a novel adaptive reduction technique that identifies rigid body regions and supports merging particles with multibody systems, with error-based refinement strategies.

Findings

Computational performance increased by 5-50 times.

Reduction levels between 70-95% achieved.

Effective refinement methods validated through experiments.

Abstract

A method for adaptive model order reduction for nonsmooth discrete element simulation is developed and analysed in numerical experiments. Regions of the granular media that collectively move as rigid bodies are substituted with rigid bodies of the corresponding shape and mass distribution. The method also support particles merging with articulated multibody systems. A model approximation error is defined and used to derive conditions for when and where to apply reduction and refinement back into particles and smaller rigid bodies. Three methods for refinement are proposed and tested: prediction from contact events, trial solutions computed in the background and using split sensors. The computational performance can be increased by 5 - 50 times for model reduction level between 70 - 95%.