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In this paper, we apply mesoscale numerical modeling to predict the effective elastic properties of conductive carbon-black/ultra-high-molecular-weight-polyethylene nanocomposites. The models are based on X-ray microcomputed tomography images. The images show that for the considered range of carbon additive weight fractions, the conductive carbon black (CB) particles are distributed around the ultra-high-molecular-weight-polyethylene (UHMWPE) granules forming a carbon-containing layer of a thickness on the order of 1-2 𝜇𝜇𝜇𝜇. Finite element models of representative volume elements (RVE), incorporating the CB-containing layer, are developed. The RVEs are generated based on the size and shape statistics extracted from processed microcomputed tomography images with further incorporation of the CB-containing layer by a custom image processing code. The layer is modeled analytically as a 2-phase composite consisting of spherical CB inclusions distributed in the UHMWPE matrix. Elastic moduli predicted in the models are compared to experimental data. Results show that the numerical simulations predict effective elastic moduli within the confidence intervals of the experimental measurements up to 7.5 wt % of CB inclusions.