Optimisation of the surfboard fin shape using computational fluid dynamics and genetic algorithms

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dc.contributor.authorSakellariou, Konstantinos
dc.contributor.authorRana, Zeeshan
dc.contributor.authorJenkins, Karl W.
dc.date.accessioned2017-08-10T08:19:46Z
dc.date.available2017-08-10T08:19:46Z
dc.date.issued2017-05-11
dc.description.abstractDuring the sport of wave surfing, the fins on a surfboard play a major role in the overall performance of the surfer. This article presents the optimisation of a surfboard fin shape, using coupled genetic algorithms with the FLUENT® solver, aiming at the maximisation of the lift per drag ratio. The design-variable vector includes six components namely the chord length, the depth and the sweep angle of the fin as well as the maximum camber, the maximum camber position and the thickness of the hydrofoil (the four-digit NACA parametrization). The Latin hypercube sampling technique is utilised to explore the design space, resulting in 42 different fin designs. Fin and control volume models are created (using CATIA® V5) and meshed (unstructured using ANSYS® Workbench). Steady-state computations were performed using the FLUENT SST k−ω (shear stress transport k−ω) turbulence model at the velocity of 10 m/s and 10° angle of attack. Using the obtained lift and drag values, a response surface based model was constructed with the aim to maximise the lift-to-drag ratio. The optimisation problem was solved using the genetic algorithm provided by the MATLAB® optimisation toolbox and the response surface based model was iteratively improved. The resultant optimal fin design is compared with the experimental data for the fin demonstrating an increase in lift-to-drag ratio by approximately 62% for the given angle of attack of 10°.en_UK
dc.identifier.citationSakellariou K, Rana ZA, Jenkins KW. Optimisation of the surfboard fin shape using computational fluid dynamics and genetic algorithms. Proceedings of the Institution of Mechanical Engineers, Part P: Journal of Sports Engineering and Technology, 2017, Volume 231, Issue 4, pp344-354en_UK
dc.identifier.issn1754-3371
dc.identifier.urihttp://dx.doi.org/10.1177/1754337117704538
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/12299
dc.language.isoenen_UK
dc.publisherSAGEen_UK
dc.rightsAttribution-Non-Commercial 3.0 Unported (CC BY-NC 3.0) You are free to: Share — copy and redistribute the material in any medium or format, Adapt — remix, transform, and build upon the material. The licensor cannot revoke these freedoms as long as you follow the license terms. Under the following terms: Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. Information: Non-Commercial — You may not use the material for commercial purposes. No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
dc.subjectGenetic algorithmen_UK
dc.subjectcomputational fluid dynamicsen_UK
dc.subjectresponse surface based modelen_UK
dc.subjectLatin hypercube samplingen_UK
dc.subjectoptimisationen_UK
dc.subjectsurfboard finen_UK
dc.titleOptimisation of the surfboard fin shape using computational fluid dynamics and genetic algorithmsen_UK
dc.typeArticleen_UK

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