改进的NSGA-Ⅱ算法研究风力机叶片多目标优化

将一种采用精英控制策略和动态拥挤方法用于快速非支配排序遗传算法（NSGA-Ⅱ）,并应用到风力机叶片的优化研究中,获得了一种新颖的风力机叶片多目标优化设计方法．作为应用算例,以设计风速下的功率系数最大和叶片质量最小为优化目标,用该方法设计了5 MW大型风力机叶片．优化结果表明,此算法在处理风力机多目标优化问题取得了良好的效果,给出的是一个Pareto最优解集,而不是传统优化方法追求的单个最优解,为风力机多目标优化设计提供新的思路和通用的算法．     

竹层积材在大型风力机叶片设计中的应用研究北大核心CSCD

从大型风力机竹叶片设计的材料及结构角度出发,首先简要介绍了作为竹叶片主增加材料——竹层积材的力学性能实验及其基本的力学特性.随后基于这些性能数据,给出了一种常规形式的1.5 MW竹叶片的气动和结构设计方法,并重点阐述了竹叶片的结构铺层方法和关键的技术细节,最后简要概述了目前竹叶片设计还需要解决的技术问题.     

Improved non-dominated sorting genetic algorithm （NSGA）-II in multi-objective optimization studies of wind turbine blades

The non-dominated sorting genetic algorithm (NSGA) is improved with the controlled elitism and dynamic crowding distance. A novel multi-objective optimization algorithm is obtained for wind turbine blades. As an example, a 5 MW wind turbine blade design is presented by taking the maximum power coefficient and the minimum blade mass as the optimization objectives. The optimal results show that this algorithm has good performance in handling the multi-objective optimization of wind turbines, and it gives a Pareto-optimal solution set rather than the optimum solutions to the conventional multiobjective optimization problems. The wind turbine blade optimization method presented in this paper provides a new and general algorithm for the multi-objective optimization of wind turbines.