The use of nanomaterials enables the development of new materials with tailored properties and coupled responses in different physical domains. In particular, the superlative combination of properties displayed by carbon nanotubes makes them an extremely interesting filler in polymer composites. The progress of industrial applications is, however, limited by the quality of predictions of their behaviour. This work develops multiscale simulation methods capable of predicting the electromechanical response of carbon nanotube polymer composites.

Finite element simulations able to capture the main mechanisms responsible for the conductivity of carbon nanotube polymer composites and their sensitivity to deformation are presented. The electrical contact between nanotubes is represented by a new element which accounts for quantum tunnelling effects and its sensitivity to deformation. Monte Carlo analyses allow for retrieving the mechanical, electrical and coupled response of these materials.

These simulations are used to successfully train an artificial neural network that predicts the bulk conductivity of these composites at negligible computational cost. Similarly, predictive multiscale models of the multiaxial electro-mechanical response are developed, enabling the simulation of the strain-sensing response of components of arbitrary shape subject to a non-uniform, multiaxial strain field. Savings in computational time of more than six orders of magnitude are obtained.

The effects of non-uniform concentration of carbon nanotubes on the bulk conductivity are explored using realistic three-dimensional representative volume elements. These are generated from measured two-dimensional concentration maps and a feature size parameter.

The models are used to predict the influence of inter-nanotube distance on the bulk conductivity, compared with the effects of applying an electric field during the curing process.

This work develops a series of modelling techniques that contribute to a better understanding of the electromechanical response of carbon nanotube composites. These methods may support the design and optimisation of applications in conductive and self-sensing structures.