基于Isight的自适应翼型前缘气动优化设计

上海交通大学学报（自然版） ›› 2014, Vol. 48 ›› Issue (08): 1122-1126.

基于Isight的自适应翼型前缘气动优化设计

周晨，王志瑾，支骄杨

（南京航空航天大学飞行器先进设计技术国防重点学科实验室，南京 210016）

收稿日期:2013-12-16 出版日期:2014-08-28 发布日期:2014-08-28
基金资助:
江苏省普通高校研究生科研创新计划资助项目（CXLX13_163），中央高校基本科研业务费专项资金资助，江苏高校优势学科建设工程资助项目

Aerodynamic Optimization Design of Adaptive Airfoil Leading Edge Based on Isight

ZHOU Chen,WANG Zhijin,ZHI Jiaoyang

(Minister Key Discipline Laboratory of Advanced Design Technology of Aircraft, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China)

Received:2013-12-16 Online:2014-08-28 Published:2014-08-28

摘要/Abstract

摘要：

为了兼顾翼型在各个飞行状态下的气动效率，基于Isight优化设计平台对自适应翼型前缘进行了气动优化设计研究.首先对Hicks-Henne型函数进行了改进，实现了翼型前缘的参数化描述；然后采用拉丁超立方实验设计方法生成样本点，并运用CFD软件进行翼型流场的气动计算，进而利用该样本数据完成对RBF神经网络的训练；最后对神经网络近似模型应用多岛遗传算法进行优化.以NACA 0006翼型为例，采用上述组合优化策略以升阻比为目标函数进行优化设计.仿真结果表明：改进后的HicksHenne型函数较好地描述了翼型前缘；组合优化方法显著提高了翼型气动优化效率.

关键词: 翼型气动优化, 实验设计, 神经网络, 遗传算法, Hicks-Henne型函数

Abstract:

Aimed to gain a high aerodynamic efficiency in various flight conditions, the aerodynamic optimization of the adaptive airfoil leading edge was conducted based on Isight platform. First, a modified method of original Hicks-Henne shape function was adopted to parametrically describe the leading edge. Then, Latin hypercube sampling was applied to generate the samples, whose aerodynamic performances were obtained from a series of CFD simulations. Subsequently, those simulated data sets were used to train the RBF neural network. Finally, the optimization of the objective function using the multiisland genetic algorithm(MIGA) based on the neural network approximation model was performed to achieve the optimal results. The lift to drag ratio optimization design of NACA 0006 was performed as an example using this combined strategy. The results show that the modified Hicks-Henne shape function describes the airfoil leading edge with satisfaction. The combined optimization method evidently improves the efficiency of the airfoil aerodynamic optimization procedure.

Key words: airfoil aerodynamic optimization, design of experiment, neural network, genetic algorithm, Hicks-Henne shape function

中图分类号:

V 211.3

周晨，王志瑾，支骄杨. 基于Isight的自适应翼型前缘气动优化设计[J]. 上海交通大学学报（自然版）, 2014, 48(08): 1122-1126.

ZHOU Chen,WANG Zhijin,ZHI Jiaoyang. Aerodynamic Optimization Design of Adaptive Airfoil Leading Edge Based on Isight[J]. Journal of Shanghai Jiaotong University, 2014, 48(08): 1122-1126.

参考文献

［1］Jason Bowman, Brian Sanders, Terrence Weisshaar. Evaluating the impact of morphing technologies on aircraft performance［C］// 43rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference. Denver, Colorado: AIAA, 2002, AIAA 20021631.

［2］Stanewsky Egon. Aerodynamic benefits of adaptive wing technology［J］. Aerospace Science and Technology, 2000, 4(7): 439452.

［3］杨智春, 解江. 自适应机翼技术的分类和实现途径［J］. 飞行力学, 2008, 26(5): 14.

YANG Zhichun, XIE Jiang. Classification and realization methodology of adaptive wings［J］. Flight Dynamics, 2008, 26(5): 14.

［4］薛亮, 韩万金, 黄家骅, 等. 基于实验设计和响应面模型的风扇转子气动优化设计［J］. 推进技术, 2009, 30(3): 308313.

XUE Liang, HAN Wanjin, HUANG Jiahua, et al. Dynamic optimization design of a fan rotor based on design of experiment and response surface model［J］. Journal of Propulsion Technology, 2009, 30(3): 308313.

［5］夏露, 王丹. 基于Kriging自适应代理模型的气动优化方法［J］. 航空计算技术, 2013, 43(1): 1317.

XIA Lu, WANG Dan. Aerodynamic optimization method based on Kringing adaptive surrogate model［J］. Aeronautical Computing Technique, 2013, 43(1): 1317.

［6］王红涛, 竺晓程, 杜朝辉. 改进EGO算法在跨声速翼型气动优化设计中的应用［J］. 上海交通大学学报, 2009, 43(11): 18321836.

WANG Hongtao, ZHU Xiaocheng, DU Zhaohui. Application of the improved EGO algorithm in transonic airfoil aerodynamic optimization design［J］. Journal of Shanghai Jiaotong University, 2009, 43(11): 18321836.

［7］李沛峰, 张彬乾, 陈迎春. 基于响应面和遗传算法的翼型优化设计方法研究［J］. 西北工业大学学报, 2012, 30(3): 395401.

LI Peifeng, ZHANG Binqian, CHEN Yinchun. An effective transonic airfoil optimization method using response surface model(RSM)［J］. Journal of Northwestern Polytechnical University, 2012, 30(3): 395401.

［8］朱莉, 高正红. 基于神经网络的翼型优化设计方法研究［J］. 航空计算技术, 2007, 37(3): 3336.

ZHU Li, GAO Zhenghong. Aerodynamic optimization design of airfoil based on neural networks［J］. Aeronautical Computing Technique, 2007, 37(3): 3336.

［9］张彬乾, 罗烈, 陈真利, 等. 飞翼布局隐身翼型优化设计研究［J］. 航空学报, 2014, 35（4）：957967.

ZHANG Binqian, LUO Lie, CHEN Zhenli, et al. On stealth airfoil optimization design for flying wing configuration［J］. Acta Aeronautica et Astronautica Sinica, 2014, 35（4）：957967.

［10］Hicks R M, Henne P A. Wing design by numerical optimization［J］. J Aircraft, 1978, 15(7): 407412.

［11］Cook P H, McDonald M A, Firmin M C P. Aerofoil RAE 2822: Pressure distributions, and boundary layer and wake measurements［R］. AGARDAR138, Farnborough UK: Royal Aircraft Establishment, 1979.

［12］McKay M D, Beckman R J, Conover W J. A comparison of three methods for selecting values of input variables in the analysis of output from a computer code［J］. Technometrics, 1979, 21(2): 239245.

［13］Hagan Martin T, Demuth Howard B, Beale Mark H. 神经网络设计［M］. 北京: 机械工业出版社, 2002.

［14］王晓鹏, 高正红. 基于遗传算法的翼型气动优化设计［J］. 空气动力学学报, 2000, 18(3): 324329.

WANG Xiaopeng, GAO Zhenghong. Aerodynamic optimization design of airfoil based on genetic algorithm［J］. Acta Aerodynamica Sinica, 2000, 18(3): 324329.

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