Sensor-based Temporal Superresolution: Application to turbulent separated flow over a three-dimensional Gaussian hill

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dc.contributor.advisorMartinuzzi, Robert
dc.contributor.advisorMorton, Christopher
dc.contributor.authorManohar, Kevin Harsh
dc.contributor.committeememberLimacher, Eric John
dc.contributor.committeememberLiao, Wenyuan
dc.date2023-11
dc.date.accessioned2023-09-20T17:51:09Z
dc.date.available2023-09-20T17:51:09Z
dc.date.issued2023-09-14
dc.description.abstractThe high Reynolds-number turbulent separated flow over a Gaussian speed-bump benchmark geometry presents challenges for predicting smooth-body flow separation. The lack of time-resolved experimental data further hampers the understanding of the three-dimensional unsteady dynamics. This thesis addresses these issues in two parts. First, a data-driven technique using high-rate surface-pressure sensors and long short-term memory (LSTM) neural networks is proposed to estimate aliased velocity dynamics from undersampled particle image velocimetry (PIV) data, revealing low and medium-frequency modes. Second, the three-dimensional unsteady wake dynamics is characterized using additional surface-pressure measurements and two-component PIV. Four dominant frequencies are identified, with a very low-frequency spanwise oscillation of the recirculating zone, two low frequencies associated with the primary separation front motion, and a higher frequency from shear layer vortex shedding. Proper orthogonal decomposition analysis highlights interactions between these modes. The instantaneous vortex topology is conceptualized to infer physical mechanisms that give rise to these frequencies.
dc.identifier.citationManohar, K. H. (2023). Sensor-based temporal superresolution: application to turbulent separated flow over a three-dimensional Gaussian hill (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.
dc.identifier.urihttps://hdl.handle.net/1880/117089
dc.identifier.urihttps://doi.org/10.11575/PRISM/41931
dc.language.isoen
dc.publisher.facultyGraduate Studies
dc.publisher.institutionUniversity of Calgary
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.subjectTurbulent separated flows
dc.subjectMachine learning
dc.subjectSensor fusion
dc.subjectSuperresolution
dc.subject.classificationEngineering--Aerospace
dc.subject.classificationEngineering--Mechanical
dc.subject.classificationFluid and Plasma
dc.titleSensor-based Temporal Superresolution: Application to turbulent separated flow over a three-dimensional Gaussian hill
dc.typemaster thesis
thesis.degree.disciplineEngineering – Mechanical & Manufacturing
thesis.degree.grantorUniversity of Calgary
thesis.degree.nameMaster of Science (MSc)
ucalgary.thesis.accesssetbystudentI do not require a thesis withhold – my thesis will have open access and can be viewed and downloaded publicly as soon as possible.
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