||CMC: Computers, Materials, & Continua, Vol. 16, No. 1, pp. 51-74, 2010
||Full length paper in PDF format. Size = 4,227,036 bytes
||particle, composite, sphere, inclusion, Monte Carlo, distribution.
||It has been shown that in particulate filled composites, a cross-property relationship exists between various transport properties (e.g., electrical conductivity, mechanical reinforcement, gas permeation) of a macroscale composite. Thus, knowledge of the effective mechanical properties of a composite immediately places bounds on its electrical conductivity or gas permeation behavior. Using these bounds allows us to predict the phase dispersion state that optimizes one or multiple properties of the composite and, thus, the knowledge of how spatial arrangement of filler particles at their given content affects physical properties of the composite can be valuable. In this paper, a new numerical model is presented capable of generating 3D random spatial distribution of rigid monodisperse spherical particles. The optimal number of particles inside a reference sphere and the macroscopically homogenous distribution of particles were the two main aspects investigated. The proposed model can be used to calculate inter particle distance, to predict particle agglomeration and, finally, to predict macroscopic properties of particulate composites. This can be of great interest, especially, when considering effects clustering or self-assembly of nanoparticles have on the properties of polymer nanocomposites.