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Towards a more accurate characterization of granular media: extracting quantitative descriptors from tomographic images

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Abstract

Imaging, epitomized by computed tomography, continues to provide unprecedented 3D access to granular microstructures at ever-greater resolutions. The non-destructive technique has enabled deep insight into the morphology and behavior of granular materials, in situ and as a function of macroscopic states, e.g., loads. However, a significant bottleneck in this paradigm is that it ultimately yields qualitative ‘pictures’ of microstructure. Hence, a major challenge is to extract quantitative descriptors of grain-scale processes, e.g., morphological description of particles, kinematics, and spatial interactions. Existing methods, including watershed and burn algorithms, are plagued with limitations related to image resolution and with the inability to sharply identify grain-to-grain contact regions, which is crucial for studying force transmission and strength in granular materials. In this work, we propose a method to overcome these drawbacks. Specifically, a novel way to extract grain topology in particulate materials via level sets is introduced. It is shown that the proposed method can sharply resolve the topology of grain surfaces near to and far from grain-to-grain contact regions with sub-voxel resolution, and is capable of grain extraction directly in three dimensions. The proposed method still relies on traditional techniques for input, but ultimately leads to much improved grain characterization. We validate the approach using three dimensional CT images of highly rounded (Caicos ooid) and highly angular (Hostun sand) natural materials, with excellent results.

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Notes

  1. To provide a sense of scale, the images utilized in this work contain roughly one particle for every 8,000 greyscale voxels of data.

  2. Recall that our proposed technique ultimately yields one level set function for each grain, i.e., a scalar value for each node on a regular Eulerian grid. A zero contour of this function describes the surface of a chosen grain.

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Acknowledgments

This work is supported in part by W.M. Keck Institute for Space Studies. This support is gratefully acknowledged.

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Authors and Affiliations

  1. InSight Mission, Jet Propulsion Laboratory, Pasadena, CA, 91109, USA

    Ivan Vlahinić & José E. Andrade

  2. Grenoble-INP / UJF-Grenoble 1 / CNRS UMR 5521, Laboratoire 3SR, 38041 , Grenoble, France

    Edward Andò & Gioacchino Viggiani

Authors
  1. Ivan Vlahinić
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  2. Edward Andò
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  3. Gioacchino Viggiani
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  4. José E. Andrade
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Correspondence to José E. Andrade.

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Vlahinić, I., Andò, E., Viggiani, G. et al. Towards a more accurate characterization of granular media: extracting quantitative descriptors from tomographic images. Granular Matter 16, 9–21 (2014). https://doi.org/10.1007/s10035-013-0460-6

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