Flow-Acoustic Correlation of Turbulent Flow in Pipelines Using Deep Learning

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atmire.migration.oldid5400
dc.contributor.advisorLeung, Henry
dc.contributor.authorMa, King
dc.contributor.committeememberFar, Behrouz
dc.contributor.committeememberYanushkevich, Svetlana
dc.date.accessioned2017-05-01T15:33:07Z
dc.date.available2017-05-01T15:33:07Z
dc.date.issued2017
dc.date.submitted2017en
dc.description.abstractThis thesis considers the development of a proposed pipeline monitoring approach based on acoustic measurements of a pipe. Relationship between the acoustics generated by a turbulent pipeline and the flowrate is examined to understand the physical behaviour of the phenomenon and verify assumptions. A framework is developed to extract features from the flow acoustics in offline and real-time settings for continuous monitoring. To ensure these features are suitable for modelling a flow-acoustic correlation, deep learning and empirical models are compared from experimental measurements of turbulent pipe flows. For deeper insight to turbulent flows, the spatio-temporal dynamics of the flow and acoustics are presented. Empirical dynamic models are shown to predict the dynamics of turbulent flow. The results show experimental evidence of ordered structures in turbulence captured in the acoustics. By isolating these structures, the turbulent motion can be predicted.en_US
dc.identifier.citationMa, K. (2017). Flow-Acoustic Correlation of Turbulent Flow in Pipelines Using Deep Learning (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. doi:10.11575/PRISM/26190en_US
dc.identifier.doihttp://dx.doi.org/10.11575/PRISM/26190
dc.identifier.urihttp://hdl.handle.net/11023/3761
dc.language.isoeng
dc.publisher.facultyGraduate Studies
dc.publisher.institutionUniversity of Calgaryen
dc.publisher.placeCalgaryen
dc.rightsUniversity of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.
dc.subjectArtificial Intelligence
dc.subjectEngineering--Electronics and Electrical
dc.subject.otherdeep learning
dc.subject.otherturbulent flows
dc.subject.otherpipeline monitoring
dc.titleFlow-Acoustic Correlation of Turbulent Flow in Pipelines Using Deep Learning
dc.typemaster thesis
thesis.degree.disciplineElectrical and Computer Engineering
thesis.degree.grantorUniversity of Calgary
thesis.degree.nameMaster of Science (MSc)
ucalgary.item.requestcopytrue
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