Local particle refinement in terramechanical simulations
Pogulis M and Servin M. Local particle refinement in terramechanical simulations. Pogulis M and Servin M. Local particle refinement in terramechanical simulations. Journal of Terramechanics, 120:101083 (2025). doi:10.1016/j.jterra.2025.101083 (2025). [pdf]
The discrete element method (DEM) is a powerful tool for simulating granular soils, but its high computational demand often results in extended simulation times. While the effect of particle size has been extensively studied, the potential benefits of spatially scaling particle sizes are less explored. We systematically investigate a local particle refinement method’s impact on reducing computational effort while maintaining accuracy. We first conduct triaxial tests to verify that bulk mechanical properties are preserved under local particle refinement. Then, we perform pressure-sinkage and shear-displacement tests, comparing our method to control simulations with homogeneous particle size. We evaluate 36 different DEM beds with varying aggressiveness in particle refinement. Our results show that this approach, depending on refinement aggressiveness, can significantly reduce particle count by 2.3 to 25 times and simulation times by 3.1 to 43 times, with normalized errors ranging from 3.5% to 11.6% compared to high-resolution reference simulations. The approach maintains a high resolution at the soil surface, where interaction is high, while allowing larger particles below the surface. The results demonstrate that substantial computational savings can be achieved without significantly compromising simulation accuracy. This method can enhance the efficiency of DEM simulations in terramechanics applications.

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The present work has in part been funded by BAE Systems Hägglunds AB, Sweden, Mistra Digital Forest, Sweden Grant DIA 2017/14 #6, and Algoryx Simulation AB, which is gratefully appreciated. The computations were enabled by resources provided by the National Academic Infrastructure for Supercomputing in Sweden (NAISS), partially funded by the Swedish Research Council, Sweden through grant agreement no. 2022-06725.

UMIT Research Lab, Digital Physics