飞翼布局隐身翼型优化设计
收稿日期: 2013-06-07
修回日期: 2013-10-21
网络出版日期: 2013-11-01
基金资助
国家级项目
On Stealth Airfoil Optimization Design for Flying Wing Configuration
Received date: 2013-06-07
Revised date: 2013-10-21
Online published: 2013-11-01
Supported by
National Level Project
针对飞翼布局设计中气动与隐身设计矛盾更为突出的问题，采用高精度气动和隐身计算方法，建立了基于Parsec参数化方法、径向基函数（RBF）神经网络、Pareto遗传算法和松散式代理模型管理方法的翼型多目标优化设计平台。根据飞翼布局内外翼不同功能和特点，确定了内外翼翼型不同的优化设计目标和约束条件，开展了兼顾气动与隐身性能要求的翼型综合优化设计研究。结果表明：对兼顾气动与隐身性能要求的飞翼布局，内翼段翼型主要通过弯度、前缘半径、尾缘角及厚度等设计，减小低头力矩和重点方位角的雷达散射截面（RCS）均值。外翼段翼型上表面的几何形状对跨声速气动效率的影响很大，应通过上表面设计提高跨声速气动效率，重点方位角RCS均值的减小则通过下表面设计实现。某些翼型参数对气动和隐身性能均有较大影响，但作用相反，应作为综合优化设计的主要设计参数，并采用不同的优化设计策略。Pareto方法给出的前沿阵面可为飞翼布局的三维设计提供更丰富的信息。
张彬乾 , 罗烈 , 陈真利 , 沈冬 , 焦子涵 , 袁广田 . 飞翼布局隐身翼型优化设计[J]. 航空学报, 2014 , 35(4) : 957 -967 . DOI: 10.7527/S1000-6893.2013.0429
To deal with the diametrically different requirements between the aerodynamic and stealth design of a flying wing configuration, high fidelity methods are used to evaluate the aerodynamic performance and stealth characteristics of the foil, and a multi-objective optimization platform is established based on the Parsec method, radial basis function (RBF) neural network, Pareto genetic algorithm and the loose surrogate model management method. Diverse optimal objectives and constraints are raised on the capability and features of the inner and outer wing. An aerodynamic and stealth integrated airfoil optimization design investigation is carried out. The results show that for a flying wing with both good aerodynamic and stealth performance,the pitching moment and radar cross section (RCS) in the key azimuth of the inner wing can be reduced by camber, leading-edge radius, trailing-edge angle and thickness design. The upper surface of the outer wing affects transonic aerodynamic performance seriously, which should be designed carefully to improve aerodynamic efficiency, while more attention should be paid to the stealth performance in the lower surface. Some airfoil parameters show opposite effect on aerodynamic performance and stealth characteristics, and they should be chosen as the main design variables for integrated optimization. The Pareto front can provide multiple choices for 3D design.
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