Abstract: To address the issue of local optima arising from the insufficient flexibility of traditional ratio-based studies on vertical axis wind turbine blades, it is essential to enhance the conversion efficiency of wind turbines when faced with complex and variable real-world challenges. This study focuses on optimizing multiple aerodynamic performance indicators, such as lift coefficient and drag coefficient, for existing airfoils to improve their aerodynamic characteristics. It employed a Non-dominated Sorting Genetic Algorithm-Ⅱ (NSGA-Ⅱ) with an elite strategy for optimization, combined with airfoil parameterization to derive optimized airfoils, followed by simulation validation of their aerodynamic performance metrics. The results indicate a significant enhancement in the aerodynamic performance of the optimized airfoil, with a 20.85 % increase in lift-to-drag ratio, a 17.35 % increase in lift coefficient, and a 2.91 % reduction in drag coefficient. Validation results demonstrate that the optimized airfoil exhibits improved wind energy conversion efficiency compared to the original design, showing better self-starting capability at low wind speeds, a broader range of adaptable wind speeds, and higher wind energy conversion efficiency. This optimization design integrates genetic algorithms with fluid dynamics simulations, providing new insights for enhancing the wind energy conversion efficiency of vertical axis wind turbines.

Key words: vertical axis wind turbine, wing shape parameterization, Non-dominated Sorting Genetic Algorithm-Ⅱ(NSGA-Ⅱ), elite strategy, aerodynamic performance