Citation link: http://dx.doi.org/10.25819/ubsi/10188
DC FieldValueLanguage
crisitem.author.orcid0000-0002-7444-702X-
dc.contributor.authorKüppers, Jan-Philipp-
dc.contributor.authorReinicke, Prof. Dr.-Ing. Tamara-
dc.date.accessioned2022-09-22T11:16:05Z-
dc.date.available2022-09-22T11:16:05Z-
dc.date.issued2022de
dc.descriptionFinanziert aus dem Open-Access-Publikationsfonds der Universität Siegen für Zeitschriftenartikelde
dc.description.abstractAccurate modeling of the dynamic stall remains a challenge for the design and construction of turbine blades and helicopter rotors. At the same time, wind turbines, for instance, are becoming steadily larger, further increasing the demands on their structure and necessitating even more detailed modeling of the forces at hand. The primarily used (semi-)empirical models today have a long research history and are invariably based on phase-averaged data from oscillating blade pitch experiments. However, much potential for more accurate modeling of uncertainties and force peaks is wasted here, since averaging blurs many features of the response signals. Even computational fluid dynamics can help little in this regard, since the Reynolds-averaged Navier–Stokes equations used in practice cannot account for cycle variations, and scale-resolving models require extremely large amounts of computational resources. This paper presents an approach for a fully stochastic machine learning model that can nevertheless simulate these critical properties. Aerodynamic coefficients are compared with experimental data for different test cases. It is shown that synthetic force profiles which cannot be distinguished from the experimental data visually and are very close to them in the frequency spectrum can be generated. Additionally, attention is drawn to the difficulty of evaluating such a model, as traditional error metrics are of little use. A combination of dynamic time warping and the Earth mover's distance provides a robust solution for this problem.en
dc.identifier.doihttp://dx.doi.org/10.25819/ubsi/10188-
dc.identifier.urihttps://dspace.ub.uni-siegen.de/handle/ubsi/2277-
dc.identifier.urnurn:nbn:de:hbz:467-22775-
dc.language.isoende
dc.sourceWind Energy Science ; 7 (5), S. 1889–1903. - DOI: https://doi.org/10.5194/wes-7-1889-2022de
dc.subject.ddc620 Ingenieurwissenschaften und zugeordnete Tätigkeitende
dc.subject.otherStochastisches dynamisches Strömungsabrissmodellde
dc.subject.otherStochastic dynamic stall modelde
dc.subject.swbGrenzschichtablösungde
dc.subject.swbStrömungsabrissmodellde
dc.titleA WaveNet-based fully stochastic dynamic stall modelen
dc.typeArticlede
item.fulltextWith Fulltext-
ubsi.publication.affiliationDepartment Maschinenbaude
ubsi.source.doi10.5194/wes-7-1889-2022-
ubsi.source.issued2022de
ubsi.source.issuenumber5de
ubsi.source.pagefrom1889de
ubsi.source.pageto1903de
ubsi.source.placeGöttingende
ubsi.source.publisherCopernicus Publicationsde
ubsi.source.titleWind Energy Sciencede
ubsi.source.volume7de
ubsi.subject.ghbsWDKde
ubsi.subject.ghbsWDAde
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