A computational fluid dynamics and machine learning study on web flutter during an oven drying process in roll-to-roll manufacturing

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dc.contributor.advisorLi, Wei (Of University of Texas at Austin)
dc.contributor.advisorSuryanarayanan, Saikishan
dc.creatorAhmed, Muhammad Bilal
dc.creator.orcid0000-0002-7526-8453
dc.date.accessioned2022-09-18T17:56:34Z
dc.date.available2022-09-18T17:56:34Z
dc.date.created2020-05
dc.date.issued2020-05-08
dc.date.submittedMay 2020
dc.date.updated2022-09-18T17:56:35Z
dc.description.abstractFluttering of a web is a major problem during the oven drying process of roll-to-roll manufacturing. In this study, two-dimensional (2D) computational fluid dynamics (CFD) models were developed to understand the flutter phenomenon. The CFD results revealed meandering of air jets as a source of flutter through air-web interactions. The root mean squared pressure (P [subscript RMS]) and mean wall shear stress (τmean) were identified as reasonable measures of web flutter cause and web drying efficiency, respectively. Machine learning models were then trained using the results of CFD simulations. It was shown that machine learning models captured the underlying physics of CFD simulations and were able to make accurate predictions. Using the machine learning models, optimization of parameters was performed where several key design and process parameters of the oven were adjusted to reduce the web flutter while keeping the rate of drying unchanged. Optimization produced promising results that showed about 30% reduction in P [subscript RMS] or web flutter could be achieved. Results of optimization were confirmed to be accurate by performing further CFD simulations
dc.description.departmentMechanical Engineering
dc.format.mimetypeapplication/pdf
dc.identifier.urihttps://hdl.handle.net/2152/115791
dc.identifier.urihttp://dx.doi.org/10.26153/tsw/42689
dc.language.isoen
dc.subjectComputational fluids dynamics
dc.subjectMachine learning
dc.subjectOptimization
dc.subjectWeb-jet interactions
dc.subjectRoll-2-roll manufacturing
dc.subjectRoll-to-roll manufacturing
dc.subjectAir flotation oven drying process
dc.subjectWeb flutter
dc.titleA computational fluid dynamics and machine learning study on web flutter during an oven drying process in roll-to-roll manufacturing
dc.typeThesis
dc.type.materialtext
thesis.degree.departmentMechanical Engineering
thesis.degree.disciplineMechanical Engineering
thesis.degree.grantorThe University of Texas at Austin
thesis.degree.levelMasters
thesis.degree.nameMaster of Science in Engineering

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