摘要
风力机叶片采用分裂式叶尖小翼可以改善叶片的气动性能。以风能利用系数最大和风轮推力系数最小为目标,采用自由涡尾迹(FVW)方法与快速非支配排序遗传算法(NSGA-Ⅱ)耦合对小翼的形状进行优化设计。NS-GA-Ⅱ算法对每一代种群进行评价、筛选和变异,最终得到小翼形状的Pareto最优解集,其中气动性能评价目标通过FVW方法计算。结果表明,FVW模型能够较准确的模拟叶片的气动性能;两目标优化给出的不是传统优化方法追求的单个最优解,而是一个Pareto最优解集,且分布在一条曲线上;相比NREL原始叶片,风能利用系数最高能提高30%;小翼的几何形状在最优解集下分布具有一定的规律性,对后面的设计及改型有很好的指导性作用。
Forked winglet can improve the aerodynamic performance of wind turbine blades. Taking the maximum power coefficient and the minimum thrust coefficient as the optimization objectives, couple the free vortex wake （FVW） method and the fast and elitist non-dominated sorting genetic algorithm （NSGA-II ） to optimize the winglet shape. NSGA- II can obtain the Pareto-optimal solutions of winglet shape by evaluating, selecting and mutating the population members, of this aerodynamic performance is calculated by FVW method. The results indicate that FVW method could simulate the aerodynamic performance accurately, and two objectives optimization gives a Pareto-optimal solution set distributing on a curve rather than the particular optimum solution. Power coefficient can be increased by 30 percent than original NREL blade. bution of winglet geometry has some regularity which can guide the later works of design and mo The distri dification.
出处
《空气动力学学报》
CSCD
北大核心
2013年第1期132-136,共5页
Acta Aerodynamica Sinica
基金
国家科技支撑计划项目(2009BAA22B03)
江苏省优势学科建设工程资助项目
国家自然科学基金(11172135)
南京航空航天大学基本科研业务费专项科研项目资助(NJ2011003)
关键词
风力机
叶尖小翼
自由涡尾迹
快速非支配排序遗传算法
气动优化设计
wind turbine
winglet
free vortex wake
fast and elitist non-dominated sorting genetic algo-rithm
aerodynamic optimal design
