Efficient prediction of the electrical conductivity and percolation threshold of nanocomposite containing spherical particles with three-dimensional random representative volume elements by random filler removal
In this work, the effective electrical conductivity (EEC) and percolation threshold (PT) of a nanocomposite containing spherical particle fillers are predicted by computational homogenization schemes (CHS) with three-dimensional random representative volume elements (RVEs) by random filler removal (RFR). For this prediction, we prepare the RVE having the maximum filler volume fraction (Vf) of 52% with random particle fillers, also termed the master model, by the discrete element method (DEM), and the corresponding finite-element (FE) model is created. Then, 100 RVE samples for each Vf are derived by randomly replacing the material properties of several fillers by those of the matrix from the master model with diverse Vf from 5% to 50%. In addition, the interphase layer is employed by replacing some matrix elements with interphase elements according to the neighboring distance of the fillers.
To demonstrate the performance of the proposed scheme, its randomness of RVEs is verified by spatial and physical metrics in terms of autocorrelation analysis, near-neighbor analysis, and directional conductivity ratio. The EEC prediction results with diverse Vf values are compared with those of an analytical model and test results. As a result, the PT at which EEC of the nanocomposites suddenly increases is successfully evaluated, and the effect of the void, interphase thickness, and conductivity, as well as the size of fillers on the EEC and PT is investigated through a sensitivity analysis.
Keywords: Polymer-matrix composites (PMC), Percolation threshold (PT), Random filler removal (RFR), Interphase modeling,