Citation link: http://dx.doi.org/10.25819/ubsi/9922
DC FieldValueLanguage
crisitem.author.orcid0000-0002-7444-702X-
dc.contributor.authorKüppers, Jan-Philipp-
dc.contributor.authorMetzger, Jens-
dc.contributor.authorJensen, Jürgen-
dc.contributor.authorReinicke, Prof. Dr.-Ing. Tamara-
dc.date.accessioned2021-06-16T11:06:55Z-
dc.date.available2021-06-16T11:06:55Z-
dc.date.issued2019de
dc.descriptionFinanziert aus dem DFG-geförderten Open-Access-Publikationsfonds der Universität Siegen für Zeitschriftenartikelde
dc.description.abstractThe supply of energy is sustainable only if it is predominantly based on renewable or regenerative energies. For this reason, the use of micro-hydropower plants on rivers and streams is considered recently. This is a particular challenge for the preservation of ecologically permeable streams, so that no dams or similar structures can be considered. While the axial turbine design has prevailed in wind power, there is still no consensus for the generation of energy in free water flow conditions. In this work, an existing prototype of an unusual vertical axis Kirsten–Boeing turbine was investigated. A multivariate optimization process was created, in which all important machine parameters were checked and improved. By using neural networks as a metamodel coupled with flow simulations in ANSYS CFX, a broadly applicable optimization strategy is presented that yielded a blade design that is 36% more efficient than its predecessor in experiments. During the process, it was shown how to set up a complex sliding mesh problem with ANSYS expressions while evaluating a free surface problem.en
dc.identifier.doihttp://dx.doi.org/10.25819/ubsi/9922-
dc.identifier.urihttps://dspace.ub.uni-siegen.de/handle/ubsi/1911-
dc.identifier.urnurn:nbn:de:hbz:467-19114-
dc.language.isoende
dc.sourceEnergies ; 12 (9), 1777. - https://doi.org/10.3390/en12091777de
dc.subject.ddc620 Ingenieurwissenschaften und zugeordnete Tätigkeitende
dc.subject.otherKirsten-Boeingde
dc.subject.otherTurbine mit vertikaler Achsede
dc.subject.otherTensorflowde
dc.subject.otherANSYS CFXde
dc.subject.otherMetamodellierungde
dc.subject.otherKirsten-Boeingen
dc.subject.otherVertical axis turbineen
dc.subject.otherTensorflowen
dc.subject.otherANSYS CFXen
dc.subject.otherMetamodelingen
dc.subject.otherNeural netsen
dc.subject.otherOptimizationde
dc.subject.swbWasserkraftwerkde
dc.subject.swbTurbinede
dc.subject.swbFließgewässerde
dc.subject.swbBoeing [Markenname]de
dc.titlePerformance optimization of a Kirsten–Boeing turbine by a metamodel based on neural networks coupled with CFDen
dc.typeArticlede
item.fulltextWith Fulltext-
ubsi.publication.affiliationDepartment Maschinenbaude
ubsi.source.authorMDPIde
ubsi.source.doi10.3390/en12091777-
ubsi.source.issn1996-1073-
ubsi.source.issued2019de
ubsi.source.issuenumber9de
ubsi.source.linkhttps://www.mdpi.com/de
ubsi.source.pages26de
ubsi.source.placeBaselde
ubsi.source.publisherMDPIde
ubsi.source.titleEnergiesde
ubsi.source.volume12de
ubsi.subject.ghbsZPOMde
Appears in Collections:Publikationen aus der Universität Siegen
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