采用粒子群算法的水平轴潮流能水轮机翼型多目标优化

为了提高水平轴潮流能水轮机叶片翼型空化性能，提出一种基于粒子群算法（PSO）的翼型性能多目标优化方法，主要针对较大攻角下翼型表面压力系数最小值；同时为保证翼型水动力性能，以翼型压力系数最小值及升力系数等建立多目标优化函数.通过程序调用XFoil对优化翼型水动力性能进行过程分析，替代计算流体动力学（CFD）分析，节省优化时间.采用此方法对NACA63-815翼型进行优化并采用CFD方法重点研究2个攻角工况下优化翼型与原翼型在3个空化数（1.0、1.5和2.0）下的空泡分布对比.结果表明，优化翼型在6.8°和10.8°攻角下压力系数最小值分别提升了17.0%和45.8%，最大升阻比提高了6.0%和61.1%.翼型的空化初生及全空化性能均得到明显提升，水动力性能也得到了提升，验证了此优化方法的可行性.
Abstract: A multi-objective optimization method for airfoil performance based on particle swarm optimization (PSO) was proposed in order to improve the cavitation performance of blade airfoil of horizontal axis tidal current turbine. Optimization mainly aimed at the minimum surface pressure coefficient of airfoil at large attack angle. In order to ensure the hydrodynamic performance of airfoil at the same time, the multi-objective optimization function was established by parameters such as the lift coefficient of airfoil and the minimum of the pressure coefficient. To improve the optimization efficiency, the process analysis of the optimized airfoil pressure coefficient was taken by the program XFoil instead of the CFD analysis. The NACA63-815 airfoils were optimized by this method. The CFD method was used to study the comparison of the cavitation distributions between the optimized airfoils and the original airfoils at three cavitation numbers (1.0, 1.5 and 2.0) under two attack angles. Results show that the minimum pressure coefficient of the optimized airfoil increases by 17.0% and 45.8% and the maximum lift-to-drag ratio increased by 6.0% and 61.1%, at the angle of attack being 6.8° and 10.8°, respectively. The cavitation initial and full cavitation performance of the optimized airfoil is significantly improved and the hydrodynamic performance is also improved, which verifies the correctness of the optimization method.