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pytrax: A simple and efficient random walk implementation for calculating the directional tortuosity of images

Tranter, TG; Kok, MDR; Lam, M; Gostick, JT; (2019) pytrax: A simple and efficient random walk implementation for calculating the directional tortuosity of images. SoftwareX , 10 , Article 100277. 10.1016/j.softx.2019.100277. Green open access

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Abstract

Given the huge advances in tomographic imaging capability in recent years, image analysis has become a powerful means of measuring transport and structural properties of porous materials. One of the most important material characteristics is the tortuosity, which is difficult to measure experimentally. We present pytrax: (tortuosity from random axial movements) a simple and efficient random walk method implemented in python to calculate the average tortuosity and orthogonal directional tortuosity components of an image. The code works for both two and three-dimensional images and completes a statistically significant number of walks in parallel for large images in a few minutes using a standard desktop computer. By comparison, a Lattice Boltzmann or finite element simulation on similar sized images can take several hours.

Type: Article
Title: pytrax: A simple and efficient random walk implementation for calculating the directional tortuosity of images
Open access status: An open access version is available from UCL Discovery
DOI: 10.1016/j.softx.2019.100277
Publisher version: https://doi.org/10.1016/j.softx.2019.100277
Language: English
Additional information: Copyright © 2019 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/).
Keywords: Random walk, Directional tortuosity, Python, Image analysis
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Chemical Engineering
URI: https://discovery.ucl.ac.uk/id/eprint/10087672
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