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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:
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:国家自然科学基金项目(面上项目,重点项目,重大项目);国家教育部回国人员科研启动基金
作者单位E-mail
唐新姿 湘潭大学 xinzitang@163.com 
李鹏程 湘潭大学  
陆鑫宇 湘潭大学  
彭锐涛 湘潭大学  
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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模型和多目标遗传算法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 adapt to stochastic turbulence conditions, taking airfoil NACA0012 as the research object, the distribution of the attack angle of the small wind turbine airfoil under turbulence was analyzed based on the non-intrusive probabilistic collocation method. Based on the Transition SST model, the Latin hypercube sampling, the Kriging model and the genetic algorithm, the multi-objective optimization coupled with boundary layer transition prediction was carried out for high turbulence and low Reynolds number wind turbine airfoil. The results show that: The average wind energy capture efficiencies of the optimal airfoil blade 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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