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dc.contributor.authorMaral, Hıdır
dc.contributor.authorŞenel, Cem Berk
dc.contributor.authorDeveci, Kaan
dc.contributor.authorAlpman, Emre
dc.contributor.authorKavurmacıoğlu, Levent Ali
dc.contributor.authorCamci, Cengiz
dc.date.accessioned2024-05-10T13:00:45Z
dc.date.available2024-05-10T13:00:45Z
dc.date.issued2020en_US
dc.identifier.citationMaral, H., Şenel, Cem B., Deveci, K., Alpman, E., Kavurmacıoğlu, Levent A., Camci, C. (2020). A genetic algorithm based multi-objective optimization of squealer tip geometry in axial flow turbines: A constant tip gap approach. Journal of Fluids Engineering , 142 (2).en_US
dc.identifier.urihttps://asmedigitalcollection.asme.org/fluidsengineering/article-abstract/142/2/021402/975404/A-Genetic-Algorithm-Based-Multi-Objective?redirectedFrom=fulltext
dc.identifier.urihttps://hdl.handle.net/20.500.12846/1246
dc.description.abstractTip clearance is a crucial aspect of turbomachines in terms of aerodynamic and thermal performance. A gap between the blade tip surface and the stationary casing must be maintained to allow the relative motion of the blade. The leakage flow through the tip gap measurably reduces turbine performance and causes high thermal loads near the blade tip region. Several studies focused on the tip leakage flow to clarify the flow-physics in the past. The “squealer” design is one of the most common designs to reduce the adverse effects of tip leakage flow. In this paper, a genetic-algorithm-based optimization approach was applied to the conventional squealer tip design to enhance aerothermal performance. A multi-objective optimization method integrated with a meta-model was utilized to determine the optimum squealer geometry. Squealer height and width represent the design parameters which are aimed to be optimized. The objective functions for the genetic-algorithm-based optimization are the total pressure loss coefficient and Nusselt number calculated over the blade tip surface. The initial database is then enlarged iteratively using a coarse-to-fine approach to improve the prediction capability of the meta-models used. The procedure ends once the prediction errors are smaller than a prescribed level. This study indicates that squealer height and width have complex effects on the aerothermal performance, and optimization study allows to determine the optimum squealer dimensions.en_US
dc.language.isoengen_US
dc.relation.isversionof10.1115/1.4044721en_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectMulti-objective optimizationen_US
dc.subjectArtificial neural networksen_US
dc.subjectGenetic algorithmen_US
dc.subjectTip leakage flowen_US
dc.subjectSquealer tipen_US
dc.subjectAxial turbineen_US
dc.titleA genetic algorithm based multi-objective optimization of squealer tip geometry in axial flow turbines: A constant tip gap approachen_US
dc.typearticleen_US
dc.relation.journalJournal of Fluids Engineeringen_US
dc.identifier.volume142en_US
dc.identifier.issue2en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.contributor.departmentTAÜ, Fen Fakültesi, Enerji Bilimi ve Teknolojileri Bölümüen_US


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