I'd like to draw your attention to the long-running mini-symposium "Mechanics of Granular Media: Experiments, Theory, and Modeling" (Mini symposium 6.1) to be held at the 2026 SES Meeting at Purdue University (October 11-14, 2026). The mini-symposium description may be found below. Please feel free to pass this announcement on to your students, postdocs, or anyone who may be interested. The abstract submission deadline is May 1, 2026. Abstracts may be submitted using Conftool, and more information may be found on the meeting website.
We are excited to hold this latest installment of our mini-symposium and hope to see you there either in the audience or giving a talk!
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Granular materials, such as those arising in industry and geophysics, display a variety of unique phenomena, such as evolving volume compaction/dilatation, localized shear-banding, fabric evolution, grain breakage, particle shape effects, rate-and-pressure-sensitive rheological behavior, segregation, and pore-fluid interactions. The varied behavior of granular materials at the macroscopic, continuum scale stems from the rich physics at the microstructural scale; however, understanding the underlying microscale mechanics and physics and the connections between the particle and continuum scales remain unresolved issues of current research. Experiments, theory, and (particle and continuum) modeling aimed at a deeper understanding of granular materials are necessary for improving modeling for large-scale systems comprised of billions of grains. This symposium shall focus on the current state-of-the-art of research in the mechanics and physics of granular materials at length-scales ranging from the particle level to the continuum scale. We encourage submissions related to experimental, theoretical, and computational research. Modeling methods of interest include discrete-particle modeling, statistical mechanics of granular media, homogenization approaches toward continuum modeling, classical and higher-order continuum theories, finite-element modeling, multi-scale approaches, parallel computing architecture, and machine learning methods.
David Henann, Brown University
Ryan Hurley, Johns Hopkins University