Particle near-neighbour separation index for quantification of segregation of granular material

Authors

Deside Chibwe, University of Wollongong
Geoffrey Michael Evans, University of Wollongong
Elham Doroodchi
Brian J. Monaghan, University of WollongongFollow
David J. Pinson, University of Wollongong
Sheng Chew, University of WollongongFollow

RIS ID

140324

Publication Details

Chibwe, D., Doroodchi, E., Evans, G. Michael., Monaghan, B., Pinson, D. & Chew, S. (2019). Particle near-neighbour separation index for quantification of segregation of granular material. Powder Technology,

Abstract

2019 Elsevier B.V. In this study, an alternative approach named near-neighbour separation index (NNSI) is proposed which defines mixing and segregation in terms of the nearest-neighbour particle scale methodology to characterise the evolution of particle-particle relationships in granular material flow systems so as to inform the extent of mixing and segregation (homogeneity) in space and time. Use is made of the geometrical particle information (position) as a function of time from the DEM simulations data to provide an instantaneous description and evaluation of the mixing and segregation process without due regard of particle history and initial configuration. The NNSI was found to give useful inferences on segregation and dispersion as it provides an unprecedented quantification of the particle-particle scale distributions of particles and fully provides a quantitative description of the particle mixture homogeneity. Furthermore, the NNSI is quite promising with added advantage and capability of handling discrete multi-size particle systems.