Cartographing dynamic stall with machine learning

dc.contributor.authorLennie, Matthew
dc.contributor.authorSteenbuck, Johannes
dc.contributor.authorNoack, Bernd R.
dc.contributor.authorPaschereit, Christian Oliver
dc.date.accessioned2020-11-30T12:43:52Z
dc.date.available2020-11-30T12:43:52Z
dc.date.issued2020-06-29
dc.description.abstractOnce stall has set in, lift collapses, drag increases and then both of these forces will fluctuate strongly. The result is higher fatigue loads and lower energy yield. In dynamic stall, separation first develops from the trailing edge up the leading edge. Eventually the shear layer rolls up, and then a coherent vortex forms and then sheds downstream with its low-pressure core causing a lift overshoot and moment drop. When 50+ experimental cycles of lift or pressure values are averaged, this process appears clear and coherent in flow visualizations. Unfortunately, stall is not one clean process but a broad collection of processes. This means that the analysis of separated flows should be able to detect outliers and analyze cycle-to-cycle variations. Modern data science and machine learning can be used to treat separated flows. In this study, a clustering method based on dynamic time warping is used to find different shedding behaviors. This method captures the fact that secondary and tertiary vorticity vary strongly, and in static stall with surging flow the flow can occasionally reattach. A convolutional neural network was used to extract dynamic stall vorticity convection speeds and phases from pressure data. Finally, bootstrapping was used to provide best practices regarding the number of experimental repetitions required to ensure experimental convergence.en
dc.description.sponsorshipTU Berlin, Open-Access-Mittel – 2020en
dc.identifier.eissn2366-7451
dc.identifier.issn2366-7443
dc.identifier.urihttps://depositonce.tu-berlin.de/handle/11303/12083
dc.identifier.urihttp://dx.doi.org/10.14279/depositonce-10958
dc.language.isoenen
dc.relation.ispartof10.14279/depositonce-10356en
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en
dc.subject.ddc600 Technik, Technologiede
dc.subject.otherdynamic stallen
dc.subject.othermachine learningen
dc.subject.otherdata scienceen
dc.titleCartographing dynamic stall with machine learningen
dc.typeArticleen
dc.type.versionpublishedVersionen
dcterms.bibliographicCitation.doi10.5194/wes-5-819-2020en
dcterms.bibliographicCitation.issue2en
dcterms.bibliographicCitation.journaltitleWind Energy Scienceen
dcterms.bibliographicCitation.originalpublishernameCopernicusen
dcterms.bibliographicCitation.originalpublisherplaceGöttingenen
dcterms.bibliographicCitation.pageend838en
dcterms.bibliographicCitation.pagestart819en
dcterms.bibliographicCitation.volume5en
tub.accessrights.dnbfreeen
tub.affiliationFak. 5 Verkehrs- und Maschinensysteme>Inst. Strömungsmechanik und Technische Akustik (ISTA)de
tub.affiliation.facultyFak. 5 Verkehrs- und Maschinensystemede
tub.affiliation.instituteInst. Strömungsmechanik und Technische Akustik (ISTA)de
tub.publisher.universityorinstitutionTechnische Universität Berlinen
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