Predictions of the electro-mechanical response of conductive CNT-polymer composites

We present finite element simulations to predict the conductivity, elastic response and strain-sensing capability of conductive composites comprising a polymeric matrix and carbon nanotubes. Realistic representative volume elements (RVE) of the microstructure are generated and both constituents are modelled as linear elastic solids, with resistivity independent of strain; the electrical contact between nanotubes is represented by a new element which accounts for quantum tunnelling effects and captures the sensitivity of conductivity to separation. Monte Carlo simulations are conducted and the sensitivity of the predictions to RVE size is explored. Predictions of modulus and conductivity are found in good agreement with published results. The strain-sensing capability of the material is explored for multiaxial strain states

A new algorithm to generate representative volume elements of composites with cylindrical or spherical fillers

A new algorithm to generate random spatial distributions of cylindrical fibres and spheres is developed based on a constrained optimization formulation. All filler particles are generated simultaneously within the specimen domain; subsequently their position is iteratively perturbed to remove particle overlapping. The algorithm is able to achieve volume fractions of up to 0.8 in the case of circular cylindrical fibres of equal diameter; the method can be applied to any statistical distribution of fibre diameters. The spatial distribution of fibres and spheres is analysed by plotting spatial statistical metrics; it is shown that the microstructures generated are spatially random and similar to those observed in real fibre composites. The algorithm is employed to effectively predict the transversely isotropic elastic, damping and plastic properties of a unidirectional fibre composite by analysis of an RVE of smaller size than previously reported