Orca hydrofoil shape optimization using Bezier curve and Artificial Neural Network – Multiple objective Genetic Algorithm for low flow velocity

Abstract

Tidal energy is a clean and predictable power source that is commonly harnessed by using horizontal axis tidal turbines. The power generating process of tidal turbines is influenced by various elements, one of which is the hydrofoil performance. This includes 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 is obtained by modified the NACA0021 airfoil. Subsequently, Bezier curve parameterization is employed to make a smooth hydrofoil profile. The study further deploy an Artificial Neural Network coupled with a Multi-Objective Genetic Algorithm, specifically targeting optimization of the hydrofoil shape optimization at a low flow velocity, specifically at an inlet velocity of 0.5 m/s. The aim of this optimization is to augment the lift coefficient and diminish the drag coefficient in order to amplify 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 Bezier-modified Orcinus Orca hydrofoils, respectively.