Vigor, J.E., Bernal, S.A., Xiao, X. et al. (1 more author) (2020) Automated correction for the movement of suspended particulate in microtomographic data. Chemical Engineering Science, 223. 115736. ISSN 0009-2509
Abstract
This study reports the development and application of a digital image cross correlation based approach to resolve contiguous microstructural volumes of interest in X-ray microtomography data, collected in fluid suspensions that undergo significant microstructural changes over time, using fresh cementitious pastes as an example. This computational method provides a high precision both for cementitious pastes that sediment only slightly (i.e. are cohesive), and for those that undergo significant sedimentation and/or settlement within the first few minutes of reaction. The normalised cross correlation algorithm presented here enables the observation of an identical volume of interest, i.e., one which contains a contiguous particle group, from the first seconds of observation onwards with excellent accuracy. This method enables segmentation of the same cluster of particles to be almost entirely automated and resolved in large sets of sequentially collected data, therefore enabling particle reaction to be observed directly while removing effects due to sedimentation.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2020 Elsevier Ltd. This is an author produced version of a paper subsequently published in Chemical Engineering Science. Uploaded in accordance with the publisher's self-archiving policy. Article available under the terms of the CC-BY-NC-ND licence (https://creativecommons.org/licenses/by-nc-nd/4.0/). |
Keywords: | Microtomography; Image processing; Suspensions; Particle-fluid reactions; Cement hydration |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Materials Science and Engineering (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 11 May 2020 10:18 |
Last Modified: | 28 Apr 2021 00:38 |
Status: | Published |
Publisher: | Elsevier BV |
Refereed: | Yes |
Identification Number: | 10.1016/j.ces.2020.115736 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:160504 |
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