Toggle Main Menu Toggle Search

Open Access padlockePrints

SHape Analyser for Particle Engineering (SHAPE): Seamless characterisation and simplification of particle morphology from imaging data

Lookup NU author(s): Vasileios AngelidakisORCiD, Dr Sadegh NadimiORCiD, Professor Stefano Utili



This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


The mechanical and rheological behaviour of particulate and granular assemblies is significantly influenced by the shape of their individual particles. We present a code that implements shape characterisation of three-dimensional particles in an automated and rigorous manner, allowing for the processing of samples composed of thousands of irregular particles within affordable time runs. The input particle geometries can be provided in one of the following forms: segmented labelled images, three-dimensional surface meshes, tetrahedral meshes or point-clouds. These can be complemented with surface texture profiles. Shape characterisation is implemented for three key aspects of shape, namely surface roughness, roundness and form. Also, simplified particle shapes are generated by the code which can be used in numerical simulations to characterise the mechanical behaviour of particulate assemblies, using numerical approaches such as the Discrete Element method and Molecular Dynamics. Combining these two features in one automated framework, the code allows not only to characterise the original granular material but also to monitor how its morphological characteristics change as the shape of the particles is simplified according to the chosen fidelity level for the application of interest.

Publication metadata

Author(s): Angelidakis V, Nadimi S, Utili S

Publication type: Article

Publication status: Published

Journal: Computer Physics Communications

Year: 2021

Volume: 265

Print publication date: 01/08/2021

Online publication date: 20/04/2021

Acceptance date: 22/03/2021

Date deposited: 28/04/2021

ISSN (electronic): 0010-4655

Publisher: Elsevier


DOI: 10.1016/j.cpc.2021.107983


Altmetrics provided by Altmetric