期刊文献+

风力机翼型参数化表达及收敛特性 被引量:20

Parametric Representation and Convergence of Wind Turbine Airfoils
下载PDF
导出
摘要 为了扩展出新的风力机翼型型线和建立便于优化统一的数学模型,基于儒可夫斯基保角变换理论和西奥道生法,提出一种通用的翼型形状参数化集成表达函数(形状函数),分析并推导生成翼型的控制条件方程,研究翼型设计空间覆盖特性情况。以FX66-S196-V1翼型为例,研究应用参数化集成方法生成的不同阶次拟合翼型的几何收敛特性和空气动力收敛特性,随着拟合阶次的增加,拟合翼型的几何坐标和气动性能结果向原翼型逐渐逼近,其11阶拟合翼型的气动特性能够很好的吻合原翼型。在相同工况下11阶拟合翼型升力特性、阻力特性结果与该翼型试验数据的误差很小,可以利用11阶拟合翼型代替原翼型进行风力机的设计和分析,并总结不同翼型参数化集成表达的最低拟合阶数。 Based on the Joukowski conformal transformation theory and Theodorsen method,a novel parametric representation function(shape function) for wind turbine airfoils is developed.The airfoil design space and shape control equations are studied.Results of the analyses of FX66-S196-V1 airfoil are shown to illustrate the evaluation processes and to demonstrate the rate of convergence of the geometric and aerodynamic characteristics.The geometric coordinates and aerodynamic performance of approximate airfoils gradually approach the baseline airfoil along with the increase of polynomial orders.The surface pressure distribution of 11-order airfoil is in good agreement with the original airfoil.The differences of lift and drag performance between 11-order airfoil and experimental data of actual airfoil are very small.11-order airfoil can be used to substitute for the actual airfoil in wind turbine design.The lowest orders of different airfoils parametric representation are presented.
出处 《机械工程学报》 EI CAS CSCD 北大核心 2010年第10期132-138,共7页 Journal of Mechanical Engineering
基金 国家自然科学基金(50775227) 重庆市自然科学基金(CSTC 2008BC3029)资助项目
关键词 风力机 翼型型线 参数化表达 收敛特性 压力分布 Wind turbine Airfoil profile Parametric representation Convergence property Pressure distribution
  • 相关文献

参考文献10

  • 1AUBREY C, KJAER C, MILLAIS C, et al. Wind force 12 [R]. Brussels Belgium. European Wind Energy Association & Greenpeace International, 2005. 被引量:1
  • 2林勇刚,李伟,崔宝玲.基于SVR风力机变桨距双模型切换预测控制[J].机械工程学报,2006,42(8):101-106. 被引量:13
  • 3SOBIECZKY H. Parametric airfoils and wings[J]. Notes on Numerical Fluid Mechanics. 1998, 68: 71-88. 被引量:1
  • 4FUGLSONG P, BAK C. Development of the Ris wind turbine airfoils [J]. Wind Energy, 2004, 7: 145-162. 被引量:1
  • 5LADSON C L, BROOKS C W. HILL A S, et al. Computer program to obtain ordinates for naca airfoils[R]. USA.. Langley Research Center. 1996. 被引量:1
  • 6HAJEK J. Parameterization of airfoils and its application in aerodynamic optimization[C]//Proceedings of the 16th Annual Conference of Doctoral Students - WDS 2007. WDS'07 Proceedings of COntributed Papers Partl, June 5-8, 2007, Charles University, Prague. Czech Republic: Matfyz Press, 2007: 233-240. 被引量:1
  • 7ABBOTT I H, ALBERT E, DOENFIOFF V. Theory of wing sections [M]. New York: Dover Publications, INC, 1959. 被引量:1
  • 8钱翼稷.空气动力学[M].北京:北京航空航天大学出版社,2005. 被引量:21
  • 9BERTAGNOLIO F, SORENSEN N, JOHANSEN J, ct al. Wind turbine airfoil catalogue[R]. Roskilde, Denmark: Risa National Laboratory, 2001. 被引量:1
  • 10MONGOMERIE B O G, BRAND A J, BOSSCHERS J, et al. Three-dimensional effects in stall[R]. ECN, Netherlands: ECN-Renewable Energy,1996. 被引量:1

二级参考文献11

  • 1林勇刚,李伟,叶杭冶,邱敏秀,金波,刘湘琪.变速恒频风力机组变桨距控制系统[J].农业机械学报,2004,35(4):110-114. 被引量:43
  • 2VapnikVN.统计学习理论的本质[M].北京:清华大学出版社,2000.. 被引量:171
  • 3诸静.智能预测控制及其应用[M].杭州:浙江大学出版社,2003. 被引量:7
  • 4CHANG C G,LIN C J.LIBSVM:a library for support vector machines[OL].http://citeseer.ni.nec.com/changol libsvm.html. 被引量:1
  • 5CHEDID R,MRAD F,Basma M.Intelligent control of a class of wind energy conversion systems[J].IEEE Trans.on EC,1999,12:1 597-1 604. 被引量:1
  • 6SOUSSO K,KODJO A.Nonlinear model identification of wind turbine with a neural network[J].IEEE Trans.on EC,2004(99):1-6. 被引量:1
  • 7ARTHUR G,ARNAUD D,RALF H,et al.Support vector regression for black-box system identification[C]//Statistical Signal Processing.Proceedings of the 11th IEEE Signal Processing Workshop on,2001:341-344. 被引量:1
  • 8QI M,SHI F W.Nonlinear model predictive control based on support vector regression[C]//Proceedings of the First International Conference on Machine Learning and Cybernetics,2002,11:1 657-1 667. 被引量:1
  • 9SHEVADE S K,KEERTHI S S,BHATTACHARYYA C,et al.Improvements to the SMO algorithm for SVM regression[J].IEEE Trans.on NN,2000,9(11):1 188-1 193. 被引量:1
  • 10PLATT J C.Fast training of support vector machine using sequential minimal optimization[OL].http://www.research.miscrosoft.com/~jplatt. 被引量:1

共引文献32

同被引文献238

引证文献20

二级引证文献177

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部