Computer simulation of random packing of unequal particles

Abstract

A Monte Carlo simulation model for the random packing of unequal spherical particles is presented in this paper. With this model, the particle radii obeying a given distribution are generated and randomly placed within a cubic packing domain with high packing density and many overlaps. Then a relaxation iteration is applied to reduce or eliminate the overlaps, while the packing space is gradually expanded. The simulation is completed once the mean overlap value falls below a preset value. To simulate the random close packing, a "vibration" process is applied after the relaxation iteration. For log-normal distributed particles, the effect of particle size standard deviation, and for bidisperse particles, the effects of particle size ratio and the volume fraction of large particles on packing density and on coordination number are investigated. Simulation results show good agreement with that obtained by experiments and by other simulations. The randomness, homogeneity, and isotropy, which have not been evaluated before for packing of distributed particles, are also examined using statistical measures.

Simulation of random packing of binary sphere mixtures by mechanical contraction

“…For g ¼ 2:6 we found f max ¼ 0:691. The simulation data reported by He et al [15] found f max for x 2 equal to 0.340 and 0.400 for size ratios 2.0 and 1.5, respectively, which significantly differs from x 2 ¼ 0:275 found in our results.…”

“…These tests are similar to the tests in Ref. [15]. The system we will present below consists of 10,976 particles where 80% of the particles are small spheres (x 2 ¼ 0:18).…”

“…Here, we note that the mechanical contraction method is similar to an existing Monte Carlo model given by He et al [15]. Particles were randomly placed within a cubic box with high density.…”

Dynamic Simulation of Random Packing of Polydispersive Fine Particles

“…It was already shown that improvements in the compaction of the particles can be accomplished when one shakes the formed aggregate, yielding particle packings with the highest densities. However, a sufficient time is required to achieve the saturation density [23], which depends on the shake amplitude. From previous numerical and experimental works [39], this saturation density increases as the shake amplitude decreases.…”

“…Significant contributions in the study of these processes were given by Yen and Chaki [13], Cheng et al [15] and Yang et al [14] using DEM. Collective rearrangement models [20][21][22][23][24] have also been used to simulate particle random packing. More recently, Jia et al [16] used DEM to study fine particle packing with Gaussian size distributions.…”