东北大学学报（自然科学版）

东北大学学报:自然科学版 ›› 2016, Vol. 37 ›› Issue (2): 232-236.DOI: 10.12068/j.issn.1005-3026.2016.02.018

• 机械工程 • 上一篇    下一篇

基于改进多目标粒子群算法的风力机大厚度翼型优化设计

陈进， 郭小锋， 孙振业， 李松林   

  1. (重庆大学 机械传动国家重点实验室， 重庆400044)
  • 收稿日期:2014-06-30 修回日期:2014-06-30 出版日期:2016-02-15 发布日期:2016-02-18
  • 通讯作者: 陈进
  • 作者简介:陈进(1956-)，男，重庆人，重庆大学教授，博士生导师.
  • 基金资助:
    国家高技术研究发展计划项目(2012AA051301)； 国家自然科学基金资助项目(51175526).

Optimization of Wind Turbine Thick Airfoils Using Improved Multi-objective Particle Swarm Algorithm

CHEN Jin， GUO Xiao-feng， SUN Zhen-ye， LI Song-lin   

  1. State Key Laboratory of Mechanic Transmission，Chongqing University，Chongqing 400044， China.
  • Received:2014-06-30 Revised:2014-06-30 Online:2016-02-15 Published:2016-02-18
  • Contact: GUO Xiao-feng
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摘要： 仅以气动性能最佳为目标进行优化设计的翼型，结构性能较差.为了克服这一缺点，基于改进的多目标粒子群算法(MOPSO)，提出了综合考虑气动性能和结构性能的大厚度翼型多目标优化设计方法.针对相对厚度为40%的翼型，应用翼型集成理论对翼型进行参数化表达，以翼型主要攻角处的升阻比最大和翼型面对弦线轴的惯性矩最大为设计目标，综合考虑翼型的粗糙度敏感性、失速特性及非设计工况特性，进行翼型的多目标优化设计，得到了Pareto最优解集.分析最优解集中的翼型，由此挑选出的新翼型在气动性能和结构性能上均比常用翼型DU00-W2-401有较大提高.

关键词: 风力机翼型, 结构性能, 气动性能, 多目标粒子群算法, 优化设计

Abstract: Considering the low structural performance of wind turbine airfoils designed just with aerodynamic performance as an optimization objective， a new multi-objective optimization method is proposed based on improved multi-objective particle swarm optimization， which balances aerodynamic and structural performance of airfoils. The airfoils with thickness of 40% are parametrically described using the wind turbine airfoil integrated theory. The optimization objectives are the maximum lift-drag ratio when designing attack angles and the maximum inertia moment about the chord axis， with such requirements as stalling characteristics， sensitivity to leading edge roughness and design lift at off-design condition considered together. The Pareto-optimal set of airfoils is obtained. The representative airfoils in the set outperform the airfoil (DU00-W2-401) in terms of aerodynamic and structural performance.

Key words: wind turbine airfoil, structural performance, aerodynamic performance, multi-objective particle swarm optimization, optimization design

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