Reduced-order modeling of solid-liquid mixing in a stirred tank using data-driven singular value decomposition

Simple item page
dc.check.date01/07/2025en
dc.check.infoAccess to this article is restricted until 24 months after publication by request of the publisheren
dc.contributor.authorJiang, Yuen
dc.contributor.authorByrne, Edmond P.en
dc.contributor.authorGlassey, Jarkaen
dc.contributor.authorChen, Xizhongen
dc.contributor.funderNational Natural Science Foundation of Chinaen
dc.contributor.funderUniversity College Corken
dc.date.accessioned2023-08-09T08:37:12Z
dc.date.available2023-08-09T08:37:12Z
dc.date.issued2023-07-01en
dc.description.abstractStirred tanks are widely used across the (bio)chemical and process industries for solid-liquid mixing. Predicting solid suspension behavior under varying agitation speeds is critical for process control and optimization. However, inherent turbulence and multiphase interactions challenges the simulation in terms of accuracy and speed. In response, increasing attention has been paid to machine learning algorithms to enhance fluid dynamics simulations. In this work, a reduced-order model (ROM) to simulate solid-liquid flows in a stirred tank was developed, which uses singular value decomposition (SVD) to learn the flow patterns from computational fluid dynamics (CFD) simulations. The impact of mode numbers and design points were further investigated. The results show that the use of the ROM can result in a reduction of computation time of up to three orders of magnitude with reasonable accuracy. This study contributes by offering an exploration into extending ROM to multiphase flows, with a particular focus on solid-liquid mixing processes.en
dc.description.sponsorshipUniversity College Cork (Eli Lilly Research Scholarships); from National Natural Science Foundation of China (Excellent Young Scientists Fund Program (Overseas))en
dc.description.statusPeer revieweden
dc.description.versionAccepted Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationJiang, Y., Byrne, E., Glassey, J. and Chen, X. (2023) 'Reduced-order modeling of solid-liquid mixing in a stirred tank using data-driven singular value decomposition', Chemical Engineering Research and Design, 196, pp. 40-51. doi: 10.1016/j.cherd.2023.06.019en
dc.identifier.doi10.1016/j.cherd.2023.06.019en
dc.identifier.endpage51en
dc.identifier.issn0263-8762en
dc.identifier.journaltitleChemical Engineering Research and Designen
dc.identifier.startpage40en
dc.identifier.urihttps://hdl.handle.net/10468/14800
dc.identifier.volume196en
dc.language.isoenen
dc.publisherElsevier Ltd.en
dc.rights© 2023, Institution of Chemical Engineers. Published by Elsevier Ltd. All rights reserved. This manuscript version is made available under the CC BY-NC-ND 4.0 license.en
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/en
dc.subjectSingular value decompositionen
dc.subjectData-drivenen
dc.subjectReduced-order modelen
dc.subjectSolid-liquid mixingen
dc.subjectStirred tanken
dc.titleReduced-order modeling of solid-liquid mixing in a stirred tank using data-driven singular value decompositionen
dc.typeArticle (peer-reviewed)en
oaire.citation.volume196en
Files
Original bundle
Now showing 1 - 2 of 2
Thumbnail Image
Name:
ChERD_Manuscript_Yu_31May2023_marked_clean.pdf
Size:
2.1 MB
Format:
Adobe Portable Document Format
Description:
Accepted Version
Thumbnail Image
Name:
1-s2.0-S0263876223003805-mmc1.docx
Size:
1.82 MB
Format:
Microsoft Word XML
Description:
Supplementary Material
License bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
license.txt
Size:
2.71 KB
Format:
Item-specific license agreed upon to submission
Description:
Download
Collections
Process and Chemical Engineering - Journal Articles