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dc.contributor.authorDaniels, SJ
dc.contributor.authorRahat, AAM
dc.contributor.authorTabor, GR
dc.contributor.authorFieldsend, JE
dc.contributor.authorEverson, RM
dc.date.accessioned2021-03-10T08:20:18Z
dc.date.issued2021-03-06
dc.description.abstractThe draft tube of a hydraulic turbine plays an important role for the efficiency and power characteristics of the overall system. The shape of the draft tube affects its performance, resulting in an increasing need for data-driven optimisation for its design. In this paper, shape optimisation of an elbow-type draft tube is undertaken, combining Computational Fluid Dynamics and a multi-objective Bayesian methodology. The chosen design objectives were to maximise pressure recovery, and minimise wall-frictional losses along the geometry. The design variables were chosen to explore potential new designs, using a series of subdivision-curves and splines on the inflow cone, outer-heel, and diffuser. The optimisation run was performed under part-load for the Kaplan turbine. The design with the lowest energy-loss identified on the Pareto-front was found to have a straight tapered diffuser, chamfered heel, and a convex inflow cone. Analysis of the performance quantities showed the typically used energy-loss factor and pressure recovery were highly correlated in cases of constant outflow cross-sections, and therefore unsuitable for use of multi-objective optimisation. Finally, a number of designs were tested over a range of discharges. From this it was found that reducing the heel size increased the efficiency over a wider operating range.en_GB
dc.description.sponsorshipEngineering and Physical Sciences Research Council (EPSRC)en_GB
dc.identifier.citationPublished online 6 March 2021en_GB
dc.identifier.doi10.1007/s11081-021-09602-6
dc.identifier.grantnumberEP/M017915/1en_GB
dc.identifier.urihttp://hdl.handle.net/10871/125079
dc.language.isoenen_GB
dc.publisherSpringeren_GB
dc.rights© The Author(s) 2021. Open Access. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/en_GB
dc.subjectHölleforsen–Kaplan draft tubeen_GB
dc.subjectBayesian optimisationen_GB
dc.subjectMulti-objective optimisationen_GB
dc.subjectShape optimisationen_GB
dc.subjectSub-division curvesen_GB
dc.titleApplication of multi-objective Bayesian shape optimisation to a sharp-heeled Kaplan draft tubeen_GB
dc.typeArticleen_GB
dc.date.available2021-03-10T08:20:18Z
dc.identifier.issn1389-4420
dc.descriptionThis is the final version. Available on open access from Springer via the DOI in this recorden_GB
dc.descriptionData availability: The research data supporting this publication are provided within this paperen_GB
dc.identifier.journalOptimization and Engineeringen_GB
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_GB
dcterms.dateAccepted2021-02-08
exeter.funder::Engineering and Physical Sciences Research Council (EPSRC)en_GB
rioxxterms.versionVoRen_GB
rioxxterms.licenseref.startdate2021-02-08
rioxxterms.typeJournal Article/Reviewen_GB
refterms.dateFCD2021-03-09T20:52:45Z
refterms.versionFCDAM
refterms.dateFOA2021-03-10T08:20:33Z
refterms.panelBen_GB


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© The Author(s) 2021. Open Access. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/
Except where otherwise noted, this item's licence is described as © The Author(s) 2021. Open Access. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/