【摘要】 为解决因垂直轴风力机叶片的传统配比式研究灵活性不足而导致产生局部最优解的问题，使垂直轴风力机在应对复杂多变的实际问题时有更佳的转化效率，针对在役翼型的升力系数、阻力系数等多项气动性能指标进行优化，以提高空气动力学性能。通过采用带精英策略的快速非支配排序遗传算法(Non-dominated Sorting Genetic Algorithms-Ⅱ,NSGA-Ⅱ)进行寻优并结合翼型参数化得到优化翼型，然后对优化翼型各气动性能指标进行仿真验证。结果表明：优化翼型空气动力学性能有了显著提升，升阻比提高了20.85%、升力系数提高了17.35%且阻力系数降低了2.91%。验证结果表明：优化翼型较原始翼型风能转化效率有了一定提升，在低风速下，优化翼型所对应的垂直轴风力机有更良好的自启动能力且适应的风速更大、风能转化效率更高。此优化设计将带精英策略的快速非支配排序遗传算法与计算流体动力学(Computational Fluid Dynamics, CFD)仿真相结合，可为垂直轴风力机风能转化效率的提升研究提供新的思路。更多还原

【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.更多还原