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A graded granular material generation algorithm based on particle number probability distribution by DEM
Physica A: Statistical Mechanics and its Applications ( IF 2.8 ) Pub Date : 2021-03-13 , DOI: 10.1016/j.physa.2021.125919
Weichen Sun , Kai Wu , Haibo Huang

This paper proposes an algorithm based on particle number probability distribution (PNPD), which is validated by two classic grading curves of Rosin–Rammler curve and Fuller curve. The PNPD algorithm is effective for the generation of graded granular materials, including spherical particles, irregular forms of clumps particles, and a mixture of both. The grading curves of generated samples are validated via experimental curves. We verified not only the gradation of granular samples in the entire sample, but also the local locations of the generated samples. In addition, the PNPD algorithm can generate graded granular materials on the conveyor belt in two and three dimensions in a dynamic process, which is consistent with the actual situation.



中文翻译:

基于DEM的颗粒数概率分布的分级颗粒物生成算法。

本文提出了一种基于粒子数概率分布(PNPD)的算法,该算法通过Rosin–Rammler曲线和Fuller曲线的两个经典渐变曲线进行了验证。PNPD算法对于生成渐变颗粒状材料非常有效,包括球形颗粒,不规则形式的块状颗粒以及两者的混合物。生成的样品的等级曲线通过实验曲线进行验证。我们不仅验证了整个样本中颗粒状样本的等级,还验证了所生成样本的本地位置。另外,PNPD算法可以动态地在二维和三维上在传送带上生成分级的颗粒物料,这与实际情况相符。

更新日期:2021-04-01
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