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

Abstract

During 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°.

Keywords

Genetic algorithm computational fluid dynamics response surface based model Latin hypercube sampling optimisation surfboard fin

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