Access to this article is restricted until 24 months after publication by request of the publisher. Restriction lift date: 01/07/2025
Reduced-order modeling of solid-liquid mixing in a stirred tank using data-driven singular value decomposition
Loading...
Files
Date
2023-07-01
Authors
Jiang, Yu
Byrne, Edmond P.
Glassey, Jarka
Chen, Xizhong
Journal Title
Journal ISSN
Volume Title
Publisher
Elsevier Ltd.
Published Version
Abstract
Stirred 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.
Description
Keywords
Singular value decomposition , Data-driven , Reduced-order model , Solid-liquid mixing , Stirred tank
Citation
Jiang, 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.2023.06.019