Adaptation of Bio-inspired Design for Wind/Tidal Turbines

Abstract

Wind turbines technology has been developing rapidly with the rapid advancement of computational capabilities and modeling techniques, and these developments are primarily aiming to maximize the power generated from the wind turbines. This work will focus on analyzing bioinspired wind turbine blade designs performance in order to understand the advantages of using bioinspired designs. The blade designs are inspired from the humpback whale where the blades leading edges are modified to be sinusoidal, resulting in tubercles/perturbations similar to the whale’s fin. The amplitude and wavelength of these perturbations were varied to get different blade designs. the amplitudes and wavelengths varied from 5% to 20% of the root chord and 60% to 90% of the root chord respectively. In addition, five configurations were analyzed, with four configurations having different tubercles distribution along the span, and that resulted in a total of 80 blades. Two types of analyses were performed, initially, Blade Element Momentum (BEM) analysis using QBlade was done to the 80 blades to find the configuration, amplitude to root chord ratio, and wavelength to root chord ratio that would provide the highest Power Coefficient (CP). It was found that higher amplitudes and lower wavelengths would give higher CP. Moreover, two of the blades with the highest CP were from the configuration where tubercles were applied along the full span, and followed by a blade from the configuration where tubercles were applied to the outer half of the blade. The Top three blades were selected alongside the baseline design to be further analyzed through CFD simulations using ANSYS Fluent. It was found from the simulations that the application of perturbations to the blade leading edge gives better stall performance with some of the blades not stalling in the considered range of angles of attack. Also, the baseline design resulted in higher lift and lower drag over a large portion of the studied Angle of Attack (𝛼) range compared to the bioinspired blades.

Date of Award	29 Nov 2024
Original language	American English
Supervisor	Imran Afgan (Supervisor)