Aerodynamic design and optimization of a small-scale wind turbine blade using a novel artificial bee colony algorithm based on blade element momentum (ABC-BEM) theory

Abstract

In this study, a novel optimization technique called the Artificial Bee Colony Algorithm based on Blade Element Momentum Theory (ABC-BEM) was developed and applied for the first time to design a small-scale wind turbine blade. Using the ABC-BEM, the aerodynamic geometry of a 1 kW small-scale wind turbine blade was optimized in terms of optimal chord length and twist angle distributions. The objective function used in the optimization study was the power coefficient, and the design parameters were the tip speed ratio, nominal wind speed, and the diameter of the rotor. Both experimental and numerical methods were used to investigate the aerodynamic performance of the optimized blades. Aerodynamic lift and drag coefficients were obtained experimentally for different low Reynolds numbers and angles of attack in a wind tunnel, while numerical simulations were conducted using Computational Fluid Dynamics (CFD) with the Transition k-kL-ω transition model. A flow visualization technique known as a smoke-wire test was carried out to analyze the flow patterns on the blades. The study resulted that the new turbine designs were more successful than the reference turbine in terms of power output. The reference turbine produced 1 kW at a wind speed of 11 m/s, while the new designs produced the same power at 9 m/s. Additionally, the reference turbine reached a maximum power of 1.2 kW at 13 m/s, while the new designs reached a maximum power of 1.46 kW at 10 m/s. These results indicated the success of the Novel ABC-BEM algorithm in optimizing the aerodynamic performance of small-scale turbine blades. This study provided an innovative approach to wind turbine design optimization and a new insight into wind turbine design.