five

Local particle refinement in terramechanical simulations

收藏
Figshare2025-01-09 更新2026-04-08 收录
下载链接:
https://figshare.com/articles/dataset/Local_particle_refinement_in_terramechanical_simulations/28159556/2
下载链接
链接失效反馈
官方服务:
资源简介:
<b>Supplementary material:</b>This upload is meant as supplementary material to the article "Local particle refinement in terramechanical simulations" (Local particle refinement in terramechanical simulations). It contains scripts to run both bed generation and track tests, as well as data from 200 already run simulations.<br><b>Abstract</b>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 3.4% to 11% 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.<br>
提供机构:
Servin, Martin; Pogulis, Markus
创建时间:
2025-01-09
5,000+
优质数据集
54 个
任务类型
进入经典数据集
二维码
社区交流群

面向社区/商业的数据集话题

二维码
科研交流群

面向高校/科研机构的开源数据集话题

数据驱动未来

携手共赢发展

商业合作