A community detection method for network structure analysis of force chains in granular medium in a rotating drum

R. Navakas, A. Džiugys
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Abstract

We analyze the motion of granular matter in a partially filled drum rotating around the horizontal axis. The motion of granular medium is simulated using the discrete element model (DEM). As the drum rotates, the free surface sloping angle changes periodically as it attains the limit repose angle leading to an avalanche, after which its value is reduced to below the repose angle. Systems of this type are of interest from both theoretical and application viewpoints: similar setups are used in industry, such as rotary kilns and mixers; besides, dynamics of granular matter leads to macroscopic effects, such as segregation and emergence of patterns. Observable macroscopic effects depend largely on the underlying structure of force chains arising from pairwise mechanical contacts between the particles. Discrete element simulations produce the data for each individual particle: position, translational and rotational velocity, force vector between the interacting particle pairs. These data about the microscopic state must be processed to obtain the observable macroscopic states. Particle configurations at each time moment available from DEM simulations can be represented as graphs: each particle is represented as a graph vertex, the vertex pairs are connected by edges if the respective particle pairs are in contact, and the edge weights are proportional to the interaction force. After the graph for a particle state is created, the algorithms of the graph analysis can be applied to analyze the corresponding state of granular matter. Among such algorithms, we use the community detection algorithms to analyse the emergence of force groups among the particles, i.e., the groups of particles that have stronger mechanical forces among the particles in the group than the forces with particles that do not belong to the given group. Such groups are structures of larger scale than the usual force chains. Distribution of group sizes (number of particles belonging to the group) and their positions depend on the rotation velocities of the drum; in turn, they influence the variation of the repose angle and the process of the avalanches. We report the relations between the characteristics of the detected force groups and the observable effects in the granular matter obtained by DEM simulations.
转鼓中颗粒介质力链网络结构分析的社团检测方法
我们分析了颗粒物质在部分填充的绕水平轴旋转的滚筒中的运动。采用离散元模型(DEM)对颗粒介质的运动进行了模拟。随着鼓体的旋转,自由面倾斜角度在达到极限休止角时发生周期性变化,导致雪崩发生,之后自由面倾斜角度减小到休止角以下。从理论和应用的角度来看,这种类型的系统都很有趣:类似的装置在工业中使用,如回转窑和搅拌器;此外,颗粒物质的动力学导致了宏观效应,如偏析和图案的出现。可观察到的宏观效应很大程度上取决于粒子之间的成对机械接触所产生的力链的潜在结构。离散元模拟产生每个粒子的数据:位置,平移和旋转速度,相互作用的粒子对之间的力向量。这些关于微观状态的数据必须经过处理才能得到可观测的宏观状态。从DEM模拟中获得的每个时刻的粒子配置可以用图形表示:每个粒子用图形顶点表示,如果粒子对接触,顶点对通过边连接,边的权重与相互作用力成正比。在创建了粒子状态的图之后,就可以应用图分析的算法来分析颗粒物质的相应状态。在这些算法中,我们使用社区检测算法来分析粒子之间的力组的出现,即,组中粒子之间的机械力比不属于给定组的粒子的力更强的粒子组。这样的群体结构比通常的力链规模更大。组大小的分布(属于组的颗粒数量)及其位置取决于滚筒的旋转速度;反过来,它们又影响着休止角的变化和雪崩的过程。我们报告了通过DEM模拟获得的颗粒物质中检测到的力组特征与可观察到的效应之间的关系。
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