Minisymposium on "Mechanics of Granular Media: Experiments, Theory, and Modeling" at SES 2023

We encourage you to contribute an abstract to the minisymposium "Mechanics of Granular Media: Experiments, Theory, and Modeling" (Minisymposium 2-1) at the 2023 SES Annual Technical Meeting to be held in Minneapolis, MN, October 8-11, 2023. The minisymposium description may be found below. For further details and to submit an abstract, please see the meeting website.

The abstract submission deadline is May 5, 2023.

It would be great to see you there!

David Henann (Brown), Ken Kamrin (MIT), and Ryan Hurley (Johns Hopkins)

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Minisymposium description: 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.