Structure Estimation of Vertical Axis Wind Turbine Using Artificial Neural Network

dc.contributor.author Teksin, S.
dc.contributor.author Azginoglu, N.
dc.contributor.author Akansu, S. O.
dc.date.accessioned 2025-05-10T17:13:02Z
dc.date.available 2025-05-10T17:13:02Z
dc.date.issued 2022
dc.description Azginoglu, Nuh/0000-0002-4074-7366 en_US
dc.description.abstract Vertical axis wind turbines (VAWTS) can be a suitable choice for usage in urban areas. Wind conditions and structural parameters are critical to power production. In this study, some variations of a darrieus vawt was attempted via wind tunnel testing. A modified naca4412 blade profile was selected for this investigation. The influence of aspect ratios, angle of attack, chord length, etc. were investigated experimentally. After taken more than 800 data artificial neural network (ANN) was applied to estimate the other range of scales using different algorithms that are utilized the aforementioned experimental parameters. This study focuses on the design criteria and application of VAWTS inbuilt real environments without testing. The results have been promising and will provide ease of design for similar designs. Moreover, this work will contribute to environmental cleanup and a more livable world by increasing renewable energy resources. (C) 2021 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Alexandria University. en_US
dc.description.sponsorship Scientific And Technological Research Council Of Turkey, Tubtak [315M478]; doctorate foundation of Erciyes University, Scientific Research Center [9685] en_US
dc.description.sponsorship This work was supported in part by the Scientific And Technological Research Council Of Turkey, Tubtak under grant no. 315M478 and the doctorate foundation of Erciyes University, Scientific Research Center, Project No: 9685. en_US
dc.identifier.doi 10.1016/j.aej.2021.05.002
dc.identifier.issn 1110-0168
dc.identifier.issn 2090-2670
dc.identifier.scopus 2-s2.0-85110088074
dc.identifier.uri https://doi.org/10.1016/j.aej.2021.05.002
dc.identifier.uri https://hdl.handle.net/20.500.14720/8072
dc.language.iso en en_US
dc.publisher Elsevier en_US
dc.rights info:eu-repo/semantics/openAccess en_US
dc.subject Ann en_US
dc.subject Estimation en_US
dc.subject Sustainable Energy en_US
dc.subject Vawt en_US
dc.subject Wind en_US
dc.subject Environment en_US
dc.title Structure Estimation of Vertical Axis Wind Turbine Using Artificial Neural Network en_US
dc.type Article en_US
dspace.entity.type Publication
gdc.author.id Azginoglu, Nuh/0000-0002-4074-7366
gdc.author.scopusid 56728619900
gdc.author.scopusid 55364407100
gdc.author.scopusid 6603264417
gdc.author.wosid Teksin, Suleyman/Afk-3449-2022
gdc.author.wosid Azgınoğlu, Nuh/G-7335-2019
gdc.author.wosid Akansu, Selahaddin/Aal-4052-2020
gdc.coar.access open access
gdc.coar.type text::journal::journal article
gdc.description.department T.C. Van Yüzüncü Yıl Üniversitesi en_US
gdc.description.departmenttemp [Teksin, S.; Akansu, S. O.] Erciyes Univ, Dept Mech Engn, Fac Engn, TR-38039 Kayseri, Turkey; [Azginoglu, N.] Kayseri Univ, Engn Architecture & Design Fac, Dept Comp Engn, TR-38280 Kayseri, Turkey; [Teksin, S.] Yuzuncu Yil Univ, Dept Mech Engn, Fac Engn, TR-65080 Van, Turkey en_US
gdc.description.endpage 314 en_US
gdc.description.issue 1 en_US
gdc.description.publicationcategory Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı en_US
gdc.description.scopusquality Q1
gdc.description.startpage 305 en_US
gdc.description.volume 61 en_US
gdc.description.woscitationindex Science Citation Index Expanded
gdc.description.wosquality Q1
gdc.identifier.wos WOS:000709484000003
gdc.index.type WoS
gdc.index.type Scopus

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