Orca hydrofoil shape optimization using Bezier curve and artificial neural network – multiple objective genetic algorithm for low flow velocity

Tidal energy is a clean and predictable power source commonly harnessed using horizontal-axis tidal turbines. The power generation process of these turbines is influenced by various elements, including the hydrofoil performance metrics such as the lift coefficient, drag coefficient, and lift-to-drag ratio. This study introduces the utilization of the Orcinus Orca hydrofoil for horizontal-axis tidal turbines. The Orcinus Orca geometry was obtained by modifying the NACA0021 airfoil. Subsequently, Bézier curve parameterization was employed to achieve a smooth hydrofoil profile. The study further deployed an artificial neural network coupled with a multi- objective genetic algorithm, targeting hydrofoil shape optimization at a low flow velocity of 0.5 m/s. The aim of this optimization was to augment the lift coefficient and decrease the drag coefficient, thereby amplifying the lift- to-drag ratio. The findings reveal a significant enhancement in performance, as the optimized Orcinus Orca hydrofoil exhibits a remarkable increase of 42.78% and 27.93% compared to the original Orcinus Orca and the Bézier-modified Orcinus Orca hydrofoils, respectively.