Investigation of S1046 profile bladed vertical axis wind turbine and artificial intelligence-based performance evaluation

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dc.authoridAkansu, Selahaddin Orhan/0000-0002-0085-7915
dc.contributor.authorOsmanli, Suleyman
dc.contributor.authorAkansu, Selahaddin Orhan
dc.contributor.authorAzginoglu, Nuh
dc.contributor.authorAkansu, Yahya Erkan
dc.contributor.authorDeveli, Ibrahim
dc.date.accessioned2024-11-07T13:34:31Z
dc.date.available2024-11-07T13:34:31Z
dc.date.issued2023
dc.departmentNiğde Ömer Halisdemir Üniversitesi
dc.description.abstractIt is very important to determine the parameters affecting the performance of the Darrieus-type wind turbine and its effects. In particular, it should be specified at which TSR value the peak power coefficient is obtained. In this study, standard and modified S1046 airfoils and aspect ratios (H/D), angle of attack (AoA), turbulent/non-turbulent flow (WT), number of blades (N), and chord length (C) were tested. Then, four different machines learning-based multi-output regression models (Decision Tree, Linear Regression, K-Nearest Neighbors, and Random Forest) were trained to make performance predictions with the data obtained from the evaluated test setup. Thirdly, feature selection based on the Random Forest algorithm, which is the best performing multi-output regression model, was performed using data due to changing parameter values on the established system. The importance of the parameters was determined. The operating range of the system was at relatively low TSR values. When analyzing the blade profile, the modified blade version performed better in certain combinations compared to the standard profile. Maximum power coefficient (Cp) was obtained from the modified turbine structure with 5 degrees of attack angle, H/D = 1.85, and C = 60 mm. The present study aims to increase the turbine's power coefficient and aims to predict results as power coefficient without doing many different experiments.
dc.description.sponsorshipScientific and Technological Research Council of Turkey, TUBITAK [FDA-2018-8258, 315M478]; Bilimsel Arastirma Projeleri, Erciyes UEniversitesi [FDA-2018-8258]; Turkiye Bilimsel ve Teknolojik Arastirma Kurumu [315M478]
dc.description.sponsorshipThe work was supported by the & nbsp;Bilimsel Arastirma Projeleri, Erciyes UEniversitesi [FDA-2018-8258]; Turkiye Bilimsel ve Teknolojik Arastirma Kurumu [315M478].
dc.identifier.doi10.1080/15567036.2023.2230930
dc.identifier.endpage8790
dc.identifier.issn1556-7036
dc.identifier.issn1556-7230
dc.identifier.issue3
dc.identifier.scopus2-s2.0-85164518379
dc.identifier.scopusqualityQ2
dc.identifier.startpage8771
dc.identifier.urihttps://doi.org/10.1080/15567036.2023.2230930
dc.identifier.urihttps://hdl.handle.net/11480/16035
dc.identifier.volume45
dc.identifier.wosWOS:001023860400001
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherTaylor & Francis Inc
dc.relation.ispartofEnergy Sources Part A-Recovery Utilization and Environmental Effects
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241106
dc.subjectVAWT
dc.subjectturbine
dc.subjectwind
dc.subjectfeature selection
dc.subjectmulti-output regression
dc.subjectmachine learning
dc.subjectenergy
dc.titleInvestigation of S1046 profile bladed vertical axis wind turbine and artificial intelligence-based performance evaluation
dc.typeArticle

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