为改善风力机翼型动态失速性能,利用代理模型方法替代计算流体力学（CFD）方法开展翼型动态失速特性优化设计。通过CST参数化方法构建翼型几何外形,采用优化的拉丁超立方抽样进行试验设计,获得样本点处的气动力参数,建立高斯过程回归模型,依据改善期望最大准则增加样本点,不断提高模型精度。以降低风力机翼型的平均力矩与阻力系数为优化目标,以平均升力系数不降为限制条件,采用受自然启发的全局进化类遗传算法进行寻优。结果表明：与原始翼型相比,优化翼型综合气动性能更优,尤其是平均阻力与平均力矩系数,分别减小9.57%与16.6%;此外,优化翼型可抑制后缘涡向前缘发展,在一定程度上改善动态失速。
Abstract
In order to improve the dynamic stall performance of wind turbine airfoil, the surrogate model method was used to replace CFD calculation to optimize the dynamic stall characteristics of wind turbine airfoil. The airfoil geometry profile was constructed by CST parameterization method, and the aerodynamic parameters at the sample points were obtained by using the optimized Latin hypercube sampling for experimental design. The Gaussian process regression model was established, and the sample points were added according to the maximum improvement expectation criterion to continuously improve the model accuracy. With the reduction of the average torque and drag coefficient of the wind turbine airfoil as the optimization objective and the non-reduction of the average lift coefficient as the limiting condition, Global evolutionary genetic algorithm inspired by nature is used to search for optimization. The results show that compared with the original airfoil, optimization of airfoil aerodynamic performance is better, especially the drag and torque coefficient are reduced 9.57% and 16.6%, respectively; In addition, the development of trailing edge vortex is inhibited by the optimized airfoil, and the dynamic stall is improved to some extent.
关键词
风力机翼型 /
动态失速 /
CST参数化 /
代理模型 /
翼型优化
Key words
wind turbine airfoil /
dynamic stall /
CST parameterization /
surrogate model /
airfoil optimization
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参考文献
[1] 李春, 叶舟, 高伟, 等. 现代陆海风力机计算与仿真[M]. 上海: 上海科学技术出版社, 2012.
LI C, YE Z, GAO W, et al.Computation and simulation of modern land-sea wind turbine[M]. Shanghai: Shanghai Scientific & technical publishers, 2012.
[2] 陈进, 陈刚, 谢翌. 考虑弯扭变形的风力机叶片结构优化[J]. 太阳能学报, 2018, 39(4): 1119-1126.
CHEN J, CHEN G, XIE Y.Structure optimization of wind turbine blade considering bend and twist deformation[J]. Acta energiae solaris sinica, 2018, 39(4): 1119-1126.
[3] 吴琪. 基于粘性伴随方法的旋翼先进气动外形优化设计分析[D]. 南京: 南京航空航天大学, 2014.
WU Q.Optimal design and analysis on advanced aerodynamic shape of rotor based on a viscous adjoint method[D]. Nanjing: Nanjing University of Aerodynamics and Astronautics, 2014.
[4] 刘俊, 宋文萍, 韩忠华, 等. Kriging模型在翼型反设计中的应用研究[J]. 空气动力学学报, 2014, 32(4): 518-526.
LIU J, SONG W P, HAN Z H, et al.Kriging-based airfoil inverse design[J]. Acta aerodynamica sinica, 2014, 32(4): 518-526.
[5] 张鑫帅, 刘俊, 罗世彬. 基于改进多目标布谷鸟搜索算法的翼型多目标气动优化设计[J]. 航空学报, 2019, 40(5): 122550.
ZHANG X S, LIU J, LUO S B.An improved multi-objective cuckoo search algorithm for airfoil aerodynamic shape optimization design[J]. Acta aeronautica et astronautica sinica, 2019, 40(5): 122550.
[6] 张玄武, 郑耀, 杨波威, 等. 基于级联前向网络的翼型优化设计[J]. 浙江大学学报(工学版), 2017, 51(7): 1405-1411.
ZHANG X W, ZHENG Y, YANG B W, et al.Aerodynamic optimization design of airfoil configurations based on cascade feedforward neural network[J]. Journal of Zhejiang University(engineering science edition), 2017, 51(7): 1405-1411.
[7] ERIC R L, BENJAMIN V, GIRMA B, et al.Comparison of neural network types and architectures for generating a surrogate aerodynamic wind turbine blade model[J]. Journal of wind engineering & industrial aerodynamics, 2021, 216: 104696.
[8] HAN Z H, XU C Z, ZHANG L, et al.Efficient aerodynamic shape optimization using variable-fidelity surrogate models and multilevel computational grids[J]. China journal of aeronautics, 2020, 33(1): 31-47.
[9] DE T, FERRIRA C, VIRE A, et al.Controlling dynamic stall using vortex generators on a wind turbine airfoil[J]. Renewable energy, 2021, 172: 1194-1211.
[10] VISUAL R, LEIFUR L.Surrogate-based aerodynamic shape optimization for delaying airfoil dynamic stall using kriging regression and infill criteria[J]. Aerospace science and technology, 2021, 111: 106555.
[11] CHEN J, WANG Q, ZHANG S Q, et al.A new direct design method of wind turbine airfoils and wind tunnel experiment[J]. Applied mathematical modelling, 2016, 40(3): 2002-2014.
[12] PHOLDEE N, BUREERAT S, NUANTONG W.Kriging surrogate-based genetic algorithm optimization for blade design of a horizontal axis wind & sciences, 2021, 126(1): 261-273.
[13] OH S.Comparison of a response surface method and artificial neural network in predicting the aerodynamic performance of a wind turbine airfoil and its optimization[J]. Applied sciences-basel, 2020, 10(18): 6277.
[14] BELAMADI R, DJEMILI A, ILINCA A, et al.Aerodynamic performance analysis of slotted airfoils for application to wind turbine blades[J]. Journal of wind engineering & industrial aerodynamics, 2016, 151: 79-99.
[15] RAMSAY R, HOFFMAN M J, GREGOREK G M.Effects of grit roughness and pitch oscillations on the S809 airfoil[R]. Golden, Colorado: National Renewable Energy Laboratory, 1999: 1-165.
[16] CORKE T C, THMOAS F O.Dynamic stall in pitching airfoils: aerodynamic damping and compressibility effects[J]. Annual review of fluid mechanics, 2015, 47(1): 479-505.
[17] 喻伯平, 李高华, 谢亮, 等. 基于代理模型的旋翼翼型动态失速优化设计[J]. 浙江大学学报(工学版), 2020, 54(4): 833-842.
YU B P, LI G H, XIE L, et al.Dynamic stall optimization design of rotor aifoil based on surrogate model[J]. Journal of Zhejiang University(engineering science), 2020, 54(4): 833-842.
[18] KULFAN B M.Universal parametric geometry representation method[J]. Journal of aircraft, 2008, 45(1): 142-158.
[19] 尹国庆, 王军, 王威, 等. 基于CST参数化方法的轴流风机多目标优化设计[J]. 风机技术, 2020, 62(6): 45-51.
YIN G Q, WANG J, WANG W, et al.Multi-objective optimization of axial flow fan based on CST parameterization method[J]. Chinese journal of turbomachinery, 2020, 62(6): 45-51.
[20] 廖炎平, 刘莉, 龙腾. 几种翼型参数化方法研究[J]. 弹箭与制导学报, 2011, 31(3): 160-164.
LIAO Y P, LIU L, LONG T.The research on some parameterized methods for airfoil[J]. Journal of projectiles rockets missiles and guidance, 2011, 31(3): 160-164.
[21] 韩忠华. Kriging模型及代理优化算法研究进展[J]. 航空学报, 2016, 37(11): 3197-3225.
HAN Z H.Kriging surrogate model and its application to design optimization: a review of recent progress[J]. Acta aeronautica et astronautica sinica, 2016, 37(11): 3197-3225.
[22] 叶鹏程, 潘光, 高山. 一种快速优化拉丁超立方试验设计方法[J]. 西北工业大学学报, 2019, 37(4): 714-723.
YE P C, PAN G, GAO S.Sampling design method of fast optimal Latin hypercube[J]. Journal of Northwestern Polytechnical University, 2019, 37(4): 714-723.
[23] 陈文英. 概率论与数理统计[M]. 北京: 科学出版社, 2012.
CHEN W Y.Probability and mathematical statistics[M]. Beijing: Science Press, 2012.
[24] 吴宽展. 基于多输出高斯过程回归的超临界翼型优化[D]. 南京: 南京航空航天大学, 2015.
WU K Z.A supercritical airfoil design based on multi-output surrogate model[D]. Nanjing: Nanjing University of Aeronautics and Astronautics, 2015.
[25] LIU J, HAN Z H, SONG W P.Comparison of infill sampling criteria in kriging-based aerodynamic optimization[C]//28th Congress of the International Council of the Aeronautical Sciences, Brisbane, Australia, 2012: 23-28.
[26] JONES D R.Efficient global optimization of expensive black-box functions[J]. Journal of global optimization, 1998, 13(4): 455-492.
基金
国家自然科学基金（51976131; 52006148）; 上海市“科技创新行动计划”地方院校能力建设项目（19060502200）
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