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唐新姿,李鹏程,陆鑫宇,彭锐涛.随机湍流工况低雷诺数风力机翼型优化研究[J].计算力学学报,2019,36(5):664~671
随机湍流工况低雷诺数风力机翼型优化研究
Optimization of low Reynolds number wind turbine airfoil under stochastic turbulence condition
投稿时间:2018-07-26  修订日期:2018-08-31
DOI:10.7511/jslx20180726002
中文关键词:  风力机翼型  低雷诺数  随机湍流  多目标优化  代理模型
英文关键词:wind turbine airfoil  low Reynolds number  stochastic turbulence  multi-objective optimization  surrogate model
基金项目:国家自然科学基金(51305377;51475404);教育部留学回国人员科研启动基金(教外司留号[2015]1098号)资助项目.
作者单位E-mail
唐新姿 湘潭大学 机械工程学院, 湘潭 411105 xinzitang@163.com 
李鹏程 湘潭大学 机械工程学院, 湘潭 411105  
陆鑫宇 湘潭大学 机械工程学院, 湘潭 411105  
彭锐涛 湘潭大学 机械工程学院, 湘潭 411105  
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中文摘要:
      为改善小型风力机随机湍流工况适应性,以NACA0012翼型为研究对象,采用非嵌入式概率配置点法,获得随机湍流工况下小型风力机叶片翼型运行攻角分布规律;在气动优化中耦合层流分离预测,基于Transition SST模型、拉丁超立方试验设计、Kriging模型和带精英策略非支配排序遗传算法NSGA-Ⅱ进行高湍流低雷诺数风力机翼型气动优化。结果表明,优化翼型叶片平均风能捕获效率分别提高3.01%和4.76%,标准差分别降低4.76%和14.93%,优化翼型湍流适应性增强。该方法将翼型设计与湍流风况相匹配,为湍流工况低雷诺数翼型及小型风力机设计提供参考。
英文摘要:
      In order to improve the adaptability of small-scall wind turbines to stochastic turbulence conditions,the distribution of the angle of attack of the turbine blade airfoil under turbulence was analyzed based on the non-intrusive probabilistic collocation method.Using airfoil NACA0012 as the example,based on the Transition SST model,the Latin hypercube sampling,the Kriging model and the non-dominated sorting genetic algorithm NSGA-Ⅱ,the multi-objective optimization coupled with boundary layer transition prediction was carried out for high turbulence and low Reynolds number wind turbine airfoils.The results show that:The average wind energy capture efficiencies of the optimal airfoil at the design condition and the off-design condition increase by 3.01% and 4.76% respectively and the standard deviations decrease by 4.76% and 14.93% respectively,the turbulence adaptability of the optimal airfoil is enhanced.The proposed method provides a solution to match the airfoil design with the turbulent wind conditions,which provides an important reference for the design and application of low Reynolds number airfoil and small wind turbines under stochastic turbulence conditions.
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