Desmond Kenneth W, Weeks Eric R
Department of Physics, Emory University, Atlanta, Georgia 30322, USA.
Phys Rev E Stat Nonlin Soft Matter Phys. 2014 Aug;90(2):022204. doi: 10.1103/PhysRevE.90.022204. Epub 2014 Aug 22.
The densest amorphous packing of rigid particles is known as random close packing. It has long been appreciated that higher densities are achieved by using collections of particles with a variety of sizes. For spheres, the variety of sizes is often quantified by the polydispersity of the particle size distribution: the standard deviation of the radius divided by the mean radius. Several prior studies quantified the increase of the packing density as a function of polydispersity. A particle size distribution is also characterized by its skewness, kurtosis, and higher moments, but the influence of these parameters has not been carefully quantified before. In this work, we numerically generate many sphere packings with different particle radii distributions, varying polydispersity and skewness independently of one another. We find that the packing density can increase significantly with increasing skewness and in some cases skewness can have a larger effect than polydispersity. However, the packing fraction is relatively insensitive to the higher moment value of the kurtosis. We present a simple empirical formula for the value of the random close packing density as a function of polydispersity and skewness.
刚性颗粒的最致密无定形堆积被称为随机密堆积。长期以来,人们认识到通过使用具有多种尺寸的颗粒集合可以实现更高的密度。对于球体,尺寸的多样性通常通过粒度分布的多分散性来量化:半径的标准偏差除以平均半径。此前的几项研究量化了堆积密度随多分散性的增加情况。粒度分布还以其偏度、峰度和更高阶矩为特征,但这些参数的影响此前尚未得到仔细量化。在这项工作中,我们通过数值方法生成了许多具有不同颗粒半径分布的球体堆积,其中多分散性和偏度彼此独立变化。我们发现,堆积密度会随着偏度的增加而显著增加,并且在某些情况下,偏度的影响可能比多分散性更大。然而,堆积分数对峰度的高阶矩值相对不敏感。我们给出了一个简单的经验公式,用于表示随机密堆积密度作为多分散性和偏度函数的值。