Please wait a minute...
浙江大学学报(工学版)  2020, Vol. 54 Issue (4): 833-842    DOI: 10.3785/j.issn.1008-973X.2020.04.023
航空航天技术     
基于代理模型的旋翼翼型动态失速优化设计
喻伯平(),李高华,谢亮,王福新*()
上海交通大学 航空航天学院,上海 200240
Dynamic stall optimization design of rotor airfoil based on surrogate model
Bo-ping YU(),Gao-hua LI,Liang XIE,Fu-xin WANG*()
School of Aeronautics and Astronautics, Shanghai Jiao Tong University, Shanghai 200240, China
 全文: PDF(1833 KB)   HTML
摘要:

利用代理模型方法取代计算流体动力学(CFD)方法,开展旋翼翼型的动态失速特性优化设计. 建立基于动网格技术的旋翼翼型非定常气动特性求解方法,获得旋翼翼型在不同外形下的升阻力和力矩气动特性参数. 利用类型转换(CST)翼型参数化方法,对初始翼型进行拟合重构;选取12个设计参数,利用基于自然启发的全局优化差分进化算法,优化目标是降低旋翼翼型的力矩和阻力特性,主要限制条件是保证升力特性不降低和翼型厚度增幅不明显. 将本文的优化设计结果与基于伴随方法和CFD方法的优化结果进行对比. 结果表明,基于Kriging模型的动态失速特性优化方法与伴随方法相比,在二维翼型优化设计上具有更好的寻优性能,优化翼型气动特性的表现更好;该方法与CFD方法相比,在利用全局优化算法的优势下,减少了过早陷入局部最优点的可能性,对比优化结果表明,在力矩和阻力特性相差无几的情况下,升力特性的表现更优.

关键词: 旋翼翼型动态失速气动优化设计代理模型    
Abstract:

The surrogate model was used to replace the computational fluid dynamic (CFD) method to optimize and design rotor airfoil considering dynamic stall characteristics. The unsteady aerodynamics calculation of rotor airfoil based on moving grid technology was established to obtain the lift, drag and torque coefficients under different airfoil shapes. The class shape transformation (CST) airfoil parameterization method was used to conduct fitting and reconstruction of the initial airfoil, and 12 design parameters were selected. The global optimal differential evolution algorithm based on natural heuristic was used to reduce the airfoil’s torque and drag coefficients. The main limiting condition was to ensure that the lift characteristics were not reduced and the airfoil thickness increase was not obvious. The optimization results of the method were compared with those of the adjoint and CFD method. Results show that the optimization method based on Kriging model has better search performance and better aerodynamic performance than the adjoint method in the two-dimensional airfoil optimization. The possibility of prematurely falling into the local best was reduced under the advantage of using the global optimization algorithm compared with CFD method. The comparison optimization results show that the lift characteristics are better when the torque and resistance characteristics are almost the same.

Key words: rotor airfoil    dynamic stall    aerodynamic optimization design    surrogate model
收稿日期: 2019-01-23 出版日期: 2020-04-05
CLC:  V 212  
基金资助: 装备预先研究项目(41406040202)
通讯作者: 王福新     E-mail: boping_yu@sjtu.edu.cn;fuxinwang@sjtu.edu.cn
作者简介: 喻伯平(1994—),男,硕士生,从事旋翼外形气动优化的研究. orcid.org/0000-0001-9923-826X. E-mail: boping_yu@sjtu.edu.cn
服务  
把本文推荐给朋友
加入引用管理器
E-mail Alert
作者相关文章  
喻伯平
李高华
谢亮
王福新

引用本文:

喻伯平,李高华,谢亮,王福新. 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.

Bo-ping YU,Gao-hua LI,Liang XIE,Fu-xin WANG. Dynamic stall optimization design of rotor airfoil based on surrogate model. Journal of ZheJiang University (Engineering Science), 2020, 54(4): 833-842.

链接本文:

http://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2020.04.023        http://www.zjujournals.com/eng/CN/Y2020/V54/I4/833

图 1  影像测量机结构示意图
图 2  升力系数、阻力系数和力矩系数数值模拟与实验对比结果图
Ai Ai
上翼面 下翼面 上翼面 下翼面
0.171 25 0.147 05 0.115 98 0.041 32
0.157 34 0.068 40 0.170 30 0.141 48
0.122 90 0.138 70 0.076 55 0.037 16
表 1  初始翼型设计参数
图 3  类型转换参数化方法拟合示意图
图 4  类型转换参数化方法拟合误差图
图 5  传统的和改进的拉丁超立方抽样方法对比图
图 6  优化设计空间
图 7  测试点1和测试点2的Kriging模型初代和最终预测结果对比图
模型 $\sigma_{C_{\rm L}} $ $\sigma_{C_{\rm D}} $ $\sigma_{C_{\rm M}} $
初代模型 10.67% 8.72% 12.38%
最终模型 2.16% 1.35% 3.06%
表 2  初代和最终Kriging模型预测误差表
图 8  差分进化算法的优化流程图
翼型 最大厚度 最大厚度位置 弯度 前缘半径
原始翼型 9.5% c 26.9% c 0.81% c 0.77% c
优化翼型 11.80% c 24.3% c 3.27% c 2.80% c
表 3  算例1的原始翼型与优化翼型细节参数对比表
图 9  算例1的动态优化翼型对比
图 10  算例1的升力、阻力和力矩系数气动特性结果对比图
图 11  算例1的动态优化翼型和初始翼型流场对比图
翼型 最大厚度 最大厚度位置 弯度 前缘半径
原始翼型 9.5% c 26.9% c 0.81% c 0.77% c
优化翼型 9.97% c 14.7% c 3.95% c 2.43% c
表 4  算例2原始翼型与优化翼型细节参数对比表
图 12  算例2的动态失速优化翼型对比
图 13  算例2的升力、阻力和力矩系数气动特性结果对比图
图 14  算例2的动态优化翼型和初始翼型流场对比
1 NGOCANH V, JAEWOO L, JUNGLL S Aerodynamic design optimization of helicopter rotor blade design including airfoil shape for hover performance[J]. Chinese Journal of Aeronautics, 2013, 26 (1): 1- 8
doi: 10.1016/j.cja.2012.12.008
2 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 Qi. Optimal design and analysis on advanced aerodynamic shape of rotor based on a viscous adjoint method [D]. Nanjing: Nanjing University of Aeronautics and Astronautics, 2014.
3 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 Xuan-wu, ZHENG Yao, YANG Bo-wei, et al Aerodynamic optimization design of airfoil configurations based on cascade feedforward neural network[J]. Journal of Zhejiang University: Engineering Science, 2017, 51 (7): 1405- 1411
4 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 Xin-shuai, LIU Jun, LUO Shi-bin An improved multi-objective cuckoo search algorithm for airfoil aerodynamic shape optimization design[J]. Acta Aeronautica et Astronautica Sinica, 2019, 40 (5): 122550
5 JAMESON A Aerodynamic design via control theory[J]. Journal of Scientific Computing, 1988, 3 (3): 233- 260
doi: 10.1007/BF01061285
6 MANI K, LOCKWOOD B A, MAVRIPLIS D J. Adjoint-based unsteady airfoil design optimization with application to dynamic stall [C] // AHS Forum. Texas: American Helicopter Society International, Inc, 2012: 68.
7 WANG Q, ZHAO Q, WU Q Aerodynamic shape optimization for alleviating dynamic stall characteristics of helicopter rotor airfoil[J]. Chinese Journal of Aeronautics, 2015, 28 (2): 346- 356
doi: 10.1016/j.cja.2014.12.033
8 WANG Q, ZHAO Q Rotor airfoil profile optimization for alleviating dynamic stall characteristics[J]. Aerospace Science and Technology, 2018, 72: 502- 515
doi: 10.1016/j.ast.2017.11.033
9 王晓锋, 席光 基于Kriging模型的翼型气动性能优化设计[J]. 航空学报, 2005, 26 (5): 545- 549
WANG Xiao-feng, XI Guang Aerodynamic optimization design for airfoil based on Kriging model[J]. Acta Aeronautica et Astronautica Sinica, 2005, 26 (5): 545- 549
doi: 10.3321/j.issn:1000-6893.2005.05.004
10 许瑞飞, 宋文萍, 韩忠华 改进Kriging模型在翼型气动优化设计中的应用研究[J]. 西北工业大学学报, 2010, 28 (4): 34- 41
XU Rui-fei, SONG Wen-ping, HAN Zhong-hua Application of improved kriging-model-based optimization method in airfoil aerodynamic design[J]. Journal of Northwestern Polytechnical University, 2010, 28 (4): 34- 41
11 HAN Z, ZHANG K, SONG W, et al. Surrogate-based aerodynamic shape optimization with application to wind turbine airfoils [C] // AIAA Aerospace Sciences Meeting Including the New Horizons Forum and Aerospace Exposition. Texas: AIAA, 2013: 1108.
12 PARK J S Optimal Latin-hypercube designs for computer experiments[J]. Journal of Statistical Planning and Inference, 1994, 39 (1): 95- 111
13 JONES D R Efficient global optimization of expensive black-box functions[J]. Journal of Global Optimization, 1998, 13 (4): 455- 492
doi: 10.1023/A:1008306431147
14 STORN R, PRICE K Differential evolution: a Simple and efficient heuristic for global optimization over continuous spaces[J]. Journal of Global Optimization, 1997, 11 (4): 341- 359
doi: 10.1023/A:1008202821328
15 SILVA W A, BARTELS R E Development of reduced-order models for aeroelastic analysis and flutter prediction using the CFL3Dv6.0 code[J]. Journal of Fluids and Structures, 2004, 19 (6): 729- 745
16 PANDA J, ZAMAN K B M Q Experimental investigation of the flow field of an oscillating airfoil and estimation of lift from wake surveys[J]. Journal of Fluid Mechanics, 2006, 265 (265): 65- 95
17 CORKE T C, THOMAS F O Dynamic stall in pitching airfoils: aerodynamic damping and compressibility effects[J]. Annual Review of Fluid Mechanics, 2015, 47 (1): 479- 505
doi: 10.1146/annurev-fluid-010814-013632
18 KULFAN B M Universal parametric geometry representation method[J]. Journal of Aircraft, 2008, 45 (1): 142- 158
doi: 10.2514/1.29958
19 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 Zhong-hua 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
20 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. Australia: [s. n.], 2012: 23-28.
21 张越. 差分进化算法及其在气动优化设计中的应用[D]. 上海: 上海交通大学, 2009.
ZHANG Yue. Differential evolution algorithm study and application in aerodynamic optimization design [D]. Shanghai: Shanghai Jiao Tong University, 2009.
[1] 徐文浩,邱展,喻伯平,王福新. 双层反转垂直轴风力机的流场特性数值模拟[J]. 浙江大学学报(工学版), 2019, 53(11): 2223-2230.
[2] 阮胤, 邱展, 王福新. 周期激励下NACA 0012翼型单自由度失速颤振研究[J]. 浙江大学学报(工学版), 2017, 51(9): 1870-1880.
[3] 张玄武, 郑耀, 杨波威, 张继发. 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.
[4] 杨茂,徐珊珊. 耦合运动的襟翼-翼型气动特性数值仿真[J]. J4, 2014, 48(1): 149-153.
[5] 赵朋, 傅建中, 李阳, 崔树标. 基于代理模型的注射参数迭代优化方法[J]. J4, 2011, 45(2): 197-200.