Warm starting the projected Gauss-Seidel algorithm for granular matter simulation
Da Wang, Martin Servin, Tomas Berglund
TL;DR
This paper investigates how warm starting the projected Gauss-Seidel solver improves convergence in granular matter simulations, significantly enhancing computational efficiency.
Contribution
It demonstrates that warm starting can increase the convergence speed of the solver by a factor of 2 to 5 in granular matter simulations.
Findings
Warm starting improves convergence speed by 2 to 5 times.
Enhanced computational performance in granular simulations.
Potential for more efficient discrete element modeling.
Abstract
The effect on the convergence of warm starting the projected Gauss-Seidel solver for nonsmooth discrete element simulation of granular matter are investigated. It is found that the computational performance can be increased by a factor 2 to 5.
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Taxonomy
TopicsGeotechnical and Geomechanical Engineering · Dynamics and Control of Mechanical Systems · Granular flow and fluidized beds