Data and code from: 3D pore shape is predictable in randomly packed particle systems

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    Geometric classifications of 3D pores are useful for studying relationships between pore geometry and function in granular materials. Pores are typically characterized by size, but size alone cannot explain 3D phenomena like transport. Here, we implement a KNN-based pore classification approach emphasizing shape-related properties. We find pore types produced in randomly packed systems resemble those of ideal, hexagonally packed systems. In both random and perfect systems, pores tend to configure as octahedrons (O’s) and icosahedrons (I’s). We demonstrate the physical implications of this by running flow simulations through a granular system and observe differences in fluid dynamic behaviors between pore types. We finally show the O/I pore distribution can be tuned by modifying particle properties (shape, stiffness, size). Overall, this work enables analysis of granular system behaviors by 3D pore shape and informs system design for desired distributions of pore geometries. ... [Read More]

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  • Saxena, Y., Riley, L., Wu, R., Kabir, M. S., Randles, A., & Segura, T. (2025). Data and code from: 3D pore shape is predictable in randomly packed particle systems. Duke Research Data Repository. https://doi.org/10.7924/r4280jp6x
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  • 10.7924/r4280jp6x
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  • ark:/87924/r4280jp6x
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  • Data and code from: 3D pore shape is predictable in randomly packed particle systems
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