Generating Accurate Microstructures of Anisotropic Magnetorheological Elastomers

Abstract

Anisotropic magnetorheological elastomers are magnetoactive smart materials whose complex magneto-mechanical behavior is strongly influenced by their underlying microstructure. Efficient and accurate multiscale simulations of these materials rely on small unit cells that reproduce the behavior of the real material. In this work, we present a framework for the generation and validation of such two-dimensional representative volume elements (RVEs) from a given microstructure image, assuming circular particles. The cells are generated through an optimization procedure where the two-point correlation function serves as a differentiable descriptor, while a minimum inter-particle distance is enforced as a constraint. To assess the equivalence of the generated cells, we compare their effective magneto-mechanical response with those of the reference microstructure. We demonstrate that the generated cells reproduce the reference response with high accuracy, also with substantially fewer particles. Consequently, the computational cost of subsequent multiscale simulations is reduced.