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Eigenparticles: characterizing particles using eigenfaces

Abstract

The shape characteristics of particles have a pinnacle role in microsopic and macroscopic features of a system. Several studies have highlighted the need for considering deviations from a spherical representation of particles for accurate modeling of granular and multiphase flow systems. Using a shape factor, sphericity or roundness parameter alone is proven to be inadequate to capture the physical phenomena. In the present study we propose a novel metric based on the pattern recognition method Eigenfaces, coining the technique ‘Eigenparticles’. Using this technique we create a single statistical distribution of basis shapes to describe the morphological composition. The proposed technique is successfully validated with test shapes and applied to real particles. When compared with a state-of-the-art Fourier based method, ‘Eigenparticles’ performs favorably, clearly distinguishing the different particles.

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Acknowledgements

The work has been supported by the Oakridge Institute for Science and Education (ORISE). The authors are grateful for the support and guidance provided by the US Department of Energy’s National Energy Technology Laboratory, particularly support from Dr. Bill Rogers. The work was also supported in part through the Engineering and Physical Sciences Research Council, UK (Grant Number R/147129).

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Correspondence to J. E. Higham.

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Higham, J.E., Vaidheeswaran, A., Benavides, K. et al. Eigenparticles: characterizing particles using eigenfaces. Granular Matter 21, 45 (2019) doi:10.1007/s10035-019-0900-z

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Keywords

  • Particle characterization
  • Particle shape
  • Principle components analysis
  • Eigenparticles
  • Eigenfeatures
  • Eigenfaces