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Advances in Mechanical Engineering
Volume 2013 (2013), Article ID 284693, 7 pages
Numerical Simulation on Dense Packing of Granular Materials by Container Oscillation
1Key Laboratory of Ministry of Education for Geomechanics and Embankment Engineering, Hohai University, 1 Xikang Road, Nanjing, Jiangsu 210098, China
2Institute of Engineering Safety and Disaster Prevention, Hohai University, 1 Xikang Road, Nanjing, Jiangsu 210098, China
Received 26 August 2013; Accepted 11 October 2013
Academic Editor: Xiaoting Rui
Copyright © 2013 Jun Liu and Dongxu You. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
The packing of granular materials is a basic and important problem in geomechanics. An approach, which generates dense packing of spheres confined in cylindrical and cuboidal containers in three steps, is introduced in this work. A loose packing structure is first generated by means of a reference lattice method. Then a dense packing structure is obtained in a container by simulating dropping of particles under gravitational forces. Furthermore, a scheme that makes the bottom boundary fluctuate up and down was applied to obtain more denser packing. The discrete element method (DEM) was employed to simulate the interactions between particle-particle and particle-boundary during the particles’ motions. Finally, two cases were presented to indicate the validity of the method proposed in this work.
The compaction of granular materials is a current subject of keen interest, which is widely used in sciences and engineering; for example, a compaction structure of granular materials can be used to model the structure of granular media, liquids, living cells, glasses, and random media. The spheres packing problem has its roots in geometry and number theory (it is part of Hilbert’s 18th problem) . Finding sphere packing with maximal density ais important to most unsolved problems with many applications.
The bulk density of a packed assembly is characterized by the packing fraction, which is the ratio of the particles’ volume to the total occupied volume of the system . A collection of uniformly sized balls in Euclidean 3-space is called a packing if no two balls have a common interior point. The packing fraction of equal spheres’ packing in three dimensions cannot exceed that of the face-centered cubic packing, (it can exceed this value in the case of sphere packing with varying sizes) and this is the famous Kepler conjecture. Recently, Hales has proved it by a program .
At present, many methods have been developed to obtain higher density of particle packing. These methods can be divided into four types: geometric method (GM), discrete element method (DEM), combined geometry and particle motion (CGPM), and Monte Carlo method (MC). In the GM, the particles’ motions are not considered and a dense packing was formed by a purely geometrical computation; for example, Place and Mora, Delarue and Jeulin [4, 5] randomly placed a given number of particles within 3D space and a dense packing was obtained by filling the remaining voids with particles. In the DEM, the interaction forces between particles in contact and between particles and boundaries were calculated and the Newtonian second law was employed to describe particles’ motions; for example, Siiriä and Yliruusi used DEM to simulate the particles’ packing formation [6–8]. In the CGPM, the geometrical method was not only used, but also the particles’ motions were considered, for example, Lubachevsky and Stillinger’s compression algorithm [9–11], and the spheres grow in size during the process of the simulation at a certain expansion rate until a final state with diverging collision rate is reached. In the Monte Carlo method, a loose particle packing structure is first generated. Then a slight random displacement is exerted on each particle. Furthermore, the new configuration can be accepted or not by identifying overlap and variety of potential energy. Finally, a dense packing can be reached by repeating the above course many times . These above mentioned methods mainly focus on reaching the highest packing density instead of describing real mechanical characteristics during the course of forming dense packing.
In this work, the objective is to generate a dense packing structure of particles to model practical dense packing, for instance, structure of concrete, packing of grain, soil, and sand, and so forth, instead of only obtaining the highest packing density. Therefore, the mechanical characteristics of forming dense packing are considered in our study. The Discrete Element Method (DEM) is employed to simulate particles’ packing confined in container (cylindrical container and cuboidal container). A loose structure was first generated. Then the dropping of particles onto the bottom of the container due to gravity is simulated. Furthermore, the bottom boundary of the container was fluctuated up and down to reach a more dense packing.
2. Initial Loose Packing
A loose packing structure must be generated in order to simulate the process of particle packing under gravitational forces. Loose packing means that there is no overlap existing between particle and its neighbors or between particle and boundaries. The objective is to avoid interactions among particles and between particles and boundaries at initial state. A simple scheme has been applied to generate loose packing. First, a reference cuboid whose projection in plane is a square has been constructed within container. The symmetrical axis of reference cuboid is the same as that of container. For a cylindrical container, the reference cuboid has the maximal size when it is inscribed to the container (see Figure 1(a)), but for cuboidal container, the maximal size of reference cuboid is the same as that of the container within the range of the container height (see Figure 1(b)). The size of the reference cuboid does not have a determinate value for a certain container because the length of its section must be integral times of the size of lattice. The height of the reference cuboid is determined by the number of particles. Secondly, the reference cuboid is partitioned using cubic lattice. The cubic lattices are regarded as reference lattice for generating coordinates of spheres’ centers. The size of lattice is determined by the sizes of particles and container. Finally, a random point is selected within each cubic lattice and the point is regarded as a particle’s center. It should be noted that the random point within a cubic lattice must meet the request of no existing intersection between particle’s surface and lattice’s faces.
Let represent the number of particles; and denote the maximal and minimal radius of the particles; and are the radius and height of cylinder; the size of the cuboidal container is ; and the size of the reference cuboid is . For the cylindrical container, the size of reference lattice is limited by the following equation: The size of reference cuboid for cylindrical container is where means remaining integral part of the result.
For the cuboidal container, the size of reference lattice is The size of reference cuboid for cuboidal container is
3. Mechanical Model
In the current study, the Discrete Element Method (DEM)  has been employed to simulate the interactions among particles and between particles and boundaries. DEM is a finite difference scheme. It is used to study assemblies of individual particles. By monitoring the interaction between particles, the behavior of the material is known.
3.1. Contact Forces
The sketch map of contact model is shown in Figure 2.
The normal contact force and shear contact force can be expressed by where , represent the normal and shear spring constant; , are the normal and shear damping coefficient due to the dissipative force; and are the unit vector in normal direction and tangential direction, respectively, which can be specified by where and are the positions of particle and , respectively; is the relative velocity of particle and at contact points when forming contact, which can be expressed by where , , , are the velocities and angular velocities of the particles and , respectively; and represent the normal and tangential contact depth. The forces are only applied to the particles in the case of , which can be expressed as follows: where is the time step.
3.2. Particles Motions
The particles’ motions are simulated using Newton’s second law; that is, where , represent the mass and moment of inertia of particle , , , represent acceleration and angular acceleration of particle , , are the resultant force and resultant moment of particle , and . The equations of motion (11) are solved by means of Verlet integrator. See  for details.
3.3. Oscillations of Bottom Boundary
To improve the volume fraction, the oscillations of bottom boundary are applied after a dense packing formation under gravity forces. A sinusoidal function has been used to describe increment of containers’ bottom boundaries displacements; see the following equation: where is the increment of bottom boundary displacement, is the amplitude of increment, is the period of oscillation, is the number of time step within a period, and is the current time. The increment of displacement must be small enough in each time step to keep stability of calculation. The central coordinates of the bottom of containers vary with calculation time; that is, where is the central position vector of the bottom boundary of container, is a normal vector of the bottom boundary, and is the initial value of (when ).
4. Two Cases
A computer program has been developed using the above-mentioned algorithm. To verify validity of the method proposed in this work, two cases are introduced for cylindrical and cuboidal containers, respectively.
4.1. Initial Loose Packing
4.2. Dense Packing under Gravity Forces
The dense packing under gravity forces process is simulated using the methods described in Section 3. Parameters used in the simulation are listed in Table 2. The simulation results are shown in Figures 4 and 5.
4.3. Dense Packing Induced by Oscillations of Containers
The packing fraction has been calculated in the oscillating process. The sum of particles volume is a constant when oscillating containers, but the volume occupied by particles within the container varies with the container oscillating. So, the packing fraction is calculated approximately for the cuboidal container as follows: where is the sum of particles volume, is the side length of the bottom boundary, is the maximal coordinate of particles during particles motions, and is the corresponding radius of the particle. For cylindrical container, replace by .
Figures 6(a), 6(b), 6(c), and 6(d) are the simulation results before and after container oscillations for cylindrical and cuboidal container, respectively. In addition, the dense packings of particles with four kinds of grain size distribution were also simulated for cylindrical and cuboidal container, respectively, as shown in Figure 7. Figure 7 shows that the particles with smaller sizes can reach a larger volume fraction both for cylindrical and cuboidal containers. Furthermore, the effects of amplitude of oscillation on volume in the case of keeping other factors the same were analyzed. An optimal oscillation amplitude exists as shown in Figure 8. The frequency of oscillation was also analyzed in this work. In some values of oscillation frequency, the dense packing structure can reach a higher volume fraction. However, there is no definite relationship between oscillation frequency and the resulted volume fraction, as shown in Figure 9.
5. Conclusions and Discussions
The simulating results implicate that oscillation of container can significantly affect the packing density. However, different oscillation amplitudes and frequencies have different effects on packing density. For a given particles system, there exists an optimal amplitude of oscillation corresponding to the maximum volume fraction. But there is no definite relationship between the frequency of oscillation and the volume fraction.
The packing density from our simulation does not exceed that from the method in the view of pure geometry. The main reason lies that the mechanical behaviors of interactions between particles and between particles and boundaries was simulated such as motions, collisions, and frictions in condition of container oscillation.
It should be noted that there exists slight overlap in the final packing structure since the gravitational forces of particles have been considered in the simulation. However, the penetration depth is small enough ( in the simulation). Therefore, the effects of small penetration depth in particles on the packing structure can be ignored in the simulation.
This work was supported by the Grant from the National Natural Science Foundation of China (no. 51174076). This grant is gratefully acknowledged.
- D. J. Daley, “Packings and approximate packings of spheres,” Tech. Rep. 104, National Institute of Statistical Sciences, Research Triangle Park, NC, USA, http://www.niss.org.
- J. A. Elliott, A. Kelly, and A. H. Windle, “Recursive packing of dense particle mixtures,” Journal of Materials Science Letters, vol. 21, no. 16, pp. 1249–1251, 2002.
- T. C. Hales, “A computer verification of the Kepler conjecture,” Proceedings of the International Congress of Mathematicians, vol. 3, no. 1–3, pp. 1–10, 2002.
- D. Place and P. Mora, “A random lattice solid model for simulation of fault zone dynamics and fracture processes,” in Bifurcation and Localisation Theory for Soils and Rocks, H.-B. Muhlhaus, A. V. Dyskin, and E. Pasternak, Eds., A.A. Balkema, Rotterdam, The Netherlands, 1999.
- A. Delarue and D. Jeulin, “Multi-scale simulation of spherical aggregates,” Image Analysis & Stereology, vol. 20, pp. 181–186, 2001.
- S. Siiriä and J. Yliruusi, “Particle packing simulations based on Newtonian mechanics,” Powder Technology, vol. 174, no. 3, pp. 82–92, 2007.
- Y. Shi and Y. Zhang, “Simulation of random packing of spherical particles with different size distributions,” Applied Physics A, vol. 92, no. 3, pp. 621–626, 2008.
- X. L. Deng and R. N. Davé, “Dynamic simulation of particle packing influenced by size, aspect ratio and surface energy,” Granular Matter, vol. 15, pp. 401–415, 2013.
- B. D. Lubachevsky and F. H. Stillinger, “Geometric properties of random disk packings,” Journal of Statistical Physics, vol. 60, no. 5-6, pp. 561–583, 1990.
- A. R. Kansal, S. Torquato, and F. H. Stillinger, “Computer generation of dense polydisperse sphere packings,” Journal of Chemical Physics, vol. 117, no. 18, pp. 8212–8218, 2002.
- A. Donev, S. Torquato, F. H. Stillinger, and R. Connelly, “Jamming in hard sphere and disk packings,” Journal of Applied Physics, vol. 95, no. 3, pp. 989–999, 2004.
- V. A. Luchnikov, M. L. Gavrilova, N. N. Medvedev, and V. P. Voloshin, “The Voronoi-Delaunay approach for the free volume analysis of a packing of balls in a cylindrical container,” Future Generation Computer Systems, vol. 18, no. 5, pp. 673–679, 2002.
- P. A. Cundall and O. D. L. Strack, “A discrete numerical model for granular assemblies,” Geotechnique, vol. 29, no. 1, pp. 47–65, 1979.