基于任意空间属性FFD技术的融合式翼稍小翼稳健型气动优化设计

黄江涛; 高正红; 白俊强; 赵轲; 李静; 许放

doi:10.7527/S1000-6893.2013.0005

航空学报 >

2013 , Vol. 34 >Issue 1: 37 - 45

DOI: https://doi.org/10.7527/S1000-6893.2013.0005

流体力学、飞行力学与发动机

基于任意空间属性FFD技术的融合式翼稍小翼稳健型气动优化设计

黄江涛 ,
高正红 ,
白俊强 ,
赵轲 ,
李静 ,
许放

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1. 西北工业大学翼型叶栅空气动力学国家重点实验室, 陕西西安 710072;
2. 中国空气动力研究与发展中心, 四川绵阳 621000

黄江涛,男,博士后。主要研究方向:飞行器总体气动设计、计算空气动力学与飞行器气动弹性力学。,E-mail:hjtcyflove@163.com;高正红,女,教授,博士生导师。主要研究方向:飞行力学与飞行控制、飞行器气动外形设计及计算空气动力学。Tel:029-88495971,E-mail:zgao@nwpu.edu.cn

收稿日期: 2012-01-11

修回日期: 2012-02-28

网络出版日期: 2013-01-19

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Study of Robust Winglet Design Based on Arbitrary Space Shape FFD Technique

HUANG Jiangtao ,
GAO Zhenghong ,
BAI Junqiang ,
ZHAO Ke ,
LI Jing ,
XU Fang

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1. National Key Laboratory of Science and Technology on Aerodynamic Design and Research, Northwestern Polytechnical University, Xi’an 710072, China;
2. China Aerodynamic Research and Development Center, Mianyang 621000, China

Received date: 2012-01-11

Revised date: 2012-02-28

Online published: 2013-01-19

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摘要

以非均匀有理B样条基函数为空间控制体属性,建立了任意空间形状自由变形(FFD)技术参数化方法。所建立的气动外形参数化系统通过FFD控制体的分布以及控制顶点的合理选取,能够对任意复杂外形进行参数化设计。首先采用FFD控制体对某型客机翼稍小翼进行空间属性构建;然后结合基于Delaunay图映射技术建立了结构对接网格变形模式,采用分群粒子群算法以及误差反向传播训练算法(BP)神经网络进行稳健型气动优化系统的构建;最后对某型客机融合式翼稍小翼的后掠角、倾斜角和高度等参数进行稳健型气动优化设计,分析对比了优化前后翼梢小翼表面压力云图、截面压力分布及载荷分布。优化设计结果表明:设计后的翼稍小翼的升阻比与阻力发散特性明显提高。

关键词： 翼稍小翼; FFD技术; 稳健型设计; BP神经网络; Delaunay图映射; 粒子群算法

本文引用格式

黄江涛 , 高正红 , 白俊强 , 赵轲 , 李静 , 许放 . 基于任意空间属性FFD技术的融合式翼稍小翼稳健型气动优化设计[J]. 航空学报, 2013 , 34(1) : 37 -45 . DOI: 10.7527/S1000-6893.2013.0005

Abstract

An arbitrary space shape free-form deformation (FFD) technique is first established in this paper based on the non-uniform rational B-splines basis function, and any complex configuration can be parameterized through choosing an FFD shape and lattice reasonably. First an airliner wingtip is parameterized using the FFD technique. Then the multi-block structure grid deformation technique is established by the Delaunay graph mapping method. An aerodynamic optimization design system is established by combining the FFD technique, the grouping particle swarm optimization arithmetic with the back propagation (BP) neural network approximation model. Finally, it processes the robust aerodynamic optimization design of the winglet by taking the swept angle, deflection angle and height of the airliner as design variables. The surface pressure contour, pressure distribution of the wing section and load distribution of the initial and optimized winglet are analyzed. The results show that the optimized winglet has significantly better aerodynamic characteristics.

Key words： winglet; FFD technique; robust design; BP neural network; Delaunay graph mapping; particle swarm optimization arithmetic

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