Сomposite materials are widely used in various industrial sectors, for example, in the aviation, marine and automotive industries, civil engineering and others. Methods based on measuring the electrical conductivity of a composite material have been actively developed to detect internal damage in polymer composite materials, such as matrix cracking, delamination, and other types of defects, which make it possible to monitor a composite’s state during its entire service life. Polymers are often used as matrices in composite materials. However, almost always pure polymers are dielectrics. The addition of nanofillers, such as graphene and its derivatives, has been successfully used to create conductive composites based on insulating polymers. The final properties of nanomodified composites can be influenced by many factors, including the type and intrinsic properties of nanoscale objects, their dispersion in the polymer matrix, and interphase interactions. The work deals with modeling of effective electric conductive properties of the representative volume elements of nanoscale composites based on a polymer matrix with graphene oxide particles distributed in it. In particular, methods for evaluating effective, electrically conductive properties have been studied, finite element modelling of representative volumes of polymer matrices with graphene oxide particles have been performed, and the influence of the tunneling effect and the orientation of inclusions on the conductive properties of materials have been investigated. The possibility of using models of resistive strain gauges operating on the principle of the tunneling effect is studied. Based on the finite-element modeling and graph theory tools, we created approaches for estimating changes in the conductive properties of the representative volume elements of a nanomodified matrix subjected to mechanical loading.
Modeling the Effective Conductive Properties of Polymer Nanocomposites with a Random Arrangement of Graphene Oxide Particles
