arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 1805.03025cond-mat.soft
收藏

细颗粒随机堆积问题的快速粘性离散元法

A fast adhesive discrete element method for random packings of fine particles

Sheng Chen, Wenwei Liu, Shuiqing Li

摘要:

在离散单元法(DEM)中引入降低的颗粒刚度允许更大的时间步长,因此在模拟中的总迭代次数更少。虽然这种方法对于干燥的非粘性颗粒效果很好,但已经表明,对于具有粘性的细颗粒,系统行为对颗粒刚度非常敏感。此外,还缺乏一个简单、实用的参数设置原则。为了解决这两个问题,我们首先提出了一个快速DEM的基础上缩放法律,以减少颗粒的杨氏模量,表面能和修改滚动和滑动阻力同时在约翰逊-肯德尔-罗伯茨(JKR)为基础的接触理论的框架。提出了一种新的反演方法,以帮助用户快速确定阻尼系数,颗粒刚度和表面能,重现预定的实验结果。在验证了该反演方法的有效性后,我们将快速粘附离散元法应用于微粒堆积问题。颗粒的堆积率、平均配位数、局部堆积率和接触数的分布与用原始颗粒性质值模拟的结果吻合较好。新方法将有助于加快离散元法模拟与团聚体或团聚体相关的系统。

英文摘要:

Introducing a reduced particle stiffness in discrete element method (DEM) allows for bigger time steps and therefore fewer total iterations in a simulation. Although this approach works well for dry non-adhesive particles, it has been shown that for fine particles with adhesion, system behaviors are drastically sensitive to the particle stiffness. Besides, a simple and applicable principle to set the parameters in adhesive DEM is also lacking. To solve these two problems, we first propose a fast DEM based on scaling laws to reduce particle Young's modulus, surface energy and to modify rolling and sliding resistances simultaneously in the framework of Johnson-Kendall-Roberts (JKR)-based contact theory. A novel inversion method is then presented to help users to quickly determine the damping coefficient, particle stiffness and surface energy to reproduce a prescribed experimental result. After validating this inversion method, we apply the fast adhesive DEM to packing problems of microparticles. Measures of packing fraction, averaged coordination number and distributions of local packing fraction and contact number of each particle are in good agreement with results simulated using original value of particle properties. The new method should be helpful to accelerate DEM simulations for systems associated with aggregates or agglomerates.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

↑