Accurate small models speed up simulations of smart magnetic materials
Generating Accurate Microstructures of Anisotropic Magnetorheological Elastomers
Computational Engineering, Finance, and Science
Summary
Magnetorheological elastomers are smart materials that change their stiffness when exposed to magnetic fields, and their behavior depends a lot on their tiny internal structure. The authors developed a way to create small, simplified models of this structure from images, using math to arrange round particles accurately. These small models behave very much like the real material but use fewer particles, which means computers can simulate them faster and at less cost. This helps engineers and scientists study and design these materials more efficiently.
What this means in practice
- •For materials engineers: Create efficient small models of magnetorheological elastomers to speed up simulation and design of smart materials with anisotropic properties.
- •For computational simulation teams: Reduce computational time and resources in multiscale simulations by using optimized representative volume elements with fewer particles.
Authors
Heinrich T. Roth, Lukas Jabs, Matti Schneider, Konstantin Gerstenberger, Günter K. Auernhammer, Karl A. Kalina, Markus Kästner
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.