High-fidelity stochastic modeling of carbon black-based conductive polymer composites for strain and fatigue sensing

Abstract

The research presented in this paper aims to improve on the accuracy and computational efficiency of previously reported stochastic models for predicting the electromechanical properties of conductive polymer composite (CPC) materials with nanoparticle constituents. The proposed approach provides a means of quantifying the degree of particle dispersion in a mixture—a parameter that is most often reported as qualitative- and process-specific. CPC-based sensors have recently garnered attention in the field of structural health monitoring due to their atomic similarities with composite materials that are an increasingly popular commodity among numerous industries. In this study, CPCs composed of carbon black nanoparticles and phenolic-based resin epoxy are manufactured and characterized both experimentally and via computational methods. A high-fidelity stochastic modeling solution is proposed to estimate electromechanical properties of such CPCs. Qualitative analysis on the fidelity of the stochastic model is demonstrated. The potential uses for the model and validation techniques are discussed.