Surface orientation tensor to predict preferred contact orientation and characterise the form of individual particles

Orosz Á, Angelidakis V, Bagi K (2021)

Publication Status: Published

Publication Type: Journal article, Original article

Publication year: 2021

Journal

Powder Technology Elsevier

Publisher: ELSEVIER

Book Volume: 394

Pages Range: 312-325

DOI: 10.1016/j.powtec.2021.08.054

Open Access Link: https://www.sciencedirect.com/science/article/pii/S0032591021007415

Abstract

The characterisation and classification of particle form are typically based on the consideration of the main particle dimensions, for the derivation of which no method has been unanimously accepted or proven to be representative of its morphology or load-bearing capabilities. This study proposes a weighted fabric tensor, named "surface orientation tensor", that characterises the form of an individual particle. Using the eigenvalues of this tensor, efficient measures of compactness, flakiness and elongation are proposed. In comparison to the traditional oriented bounding box approaches, it has the advantage that it is based directly on the orientations of the normal vectors of the faces forming the surface of the particle, i.e. those directions along which the particle can best transmit contact forces to its neighbours. The advantages of the proposed approach are pointed out with discrete element simulations on assemblies of polyhedral particles. (c) 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http:// creativecommons.org/licenses/by/4.0/).

Authors with CRIS profile

Vasileios Angelidakis Lehrstuhl für Multiscale Simulation of Particulate Systems

Involved external institutions

Budapest University of Technology and Economics (BME) / Budapesti Műszaki és Gazdaságtudományi Egyetem HU Hungary (HU)

How to cite

APA:

Orosz, Á., Angelidakis, V., & Bagi, K. (2021). Surface orientation tensor to predict preferred contact orientation and characterise the form of individual particles. Powder Technology, 394, 312-325. https://dx.doi.org/10.1016/j.powtec.2021.08.054

MLA:

Orosz, Ákos, Vasileios Angelidakis, and Katalin Bagi. "Surface orientation tensor to predict preferred contact orientation and characterise the form of individual particles." Powder Technology 394 (2021): 312-325.

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