基于神经网络响应面的机翼气动稳健性优化设计

针对不确定性因素引起飞机性能波动的现象,探讨了机翼气动优化设计过程的稳健性问题;建立了面向速度和扭转角两个不确定性因素的气动性能稳健性约束模型;在利用MATLAB构造基于均匀设计法的BP(Back Propagation)神经网络响应面基础上,应用遗传算法对机翼分别进行考虑稳健性约束和不考虑稳健性约束的气动优化设计,得到两种不同的优化方案。计算结果表明:两种优化方案的最大升阻比都比初始方案的大;在巡航马赫数下,与不考虑稳健性约束的优化方案相比,考虑稳健性约束的优化方案的最大升阻比小0.027 9,但在马赫数、扭转角对应范围内其最大升阻比的变化量分别小0.034 0和0.001 6,其他气动性能参数也更加稳定,波动更小,气动性能具有更好的稳健性,从而证明本文方法进行机翼气动稳健性优化设计是可行、有效的。

Wing Aerodynamic Robustness Optimization Based on Neural Network Response Surface

The robustness problem in the aerodynamic optimization of an aircraft wing is discussed in reference to the undulation of aircraft performance derived from uncertainty factors. Aerodynamic performance robustness constrained models are built which are subject to the uncertainty factors of velocity and twist angle. By dint of the BP (Back Propagation) neural network response surface based on the uniform design which is constructed through MATLAB, two schemes, whose difference lies in whether or not robustness is taken into account, are respectively obtained based on genetic algorithm. The results suggest that the maximum lift-drag ratios at cruising speeds for both schemes are higher than those of the initial scheme. Though the scheme with the consideration of robustness is 0.027 9 lower than that of the scheme without it, the variation of maximum lift-drag ratio of the former scheme is respectively 0.034 0 and 0.001 6 less than the latter within the range of thecruise Mach number and the twist angle. Other aerodynamic performances of the design which takes robustness into consideration are also much more stable than those which does not. Therefore the aerodynamic robustness optimization method in this article is shown to be useful and efficient.