摘要
以固定翼式微型飞行器为研究背景,针对小展弦比机翼,将遗传算法与Navier-Stokes方程数值解法相结合,提出了一种以实数编码为基础的数值优化模型。流场数值模拟采用人工压缩方法,遗传算法采用锦标赛选择、自适应交叉和变异操作算子,对微型飞行器机翼选取五个设计点分别进行了升阻比优化设计。优化结果表明,本文的优化模型具有较高的搜索效率和显著的优化效果,五个设计点机翼的升阻比均提高30%以上。优化后的翼面接近椭圆形状,翼型前缘钝厚,尾缘向上拱起,这种形状能大大增加升力,降低诱导阻力,从而显著提高机翼的气动性能。
Fixed-wing micro air vehicle （MAV） is numerically investigated and optimized with genetic algorithm （GA）. Aerodynamic performance evaluation is provided by three-dimensional incompressible Navier-Stokes equations that are solved numerically with artificial compressibility method. Present GA is real-coded and is developed with elite production, tournament selection, adaptive crossover and mutation. Five design points, angles of attack ranging from 2 deg. to 10 deg. with interval of 2.0, are considered. As a result, higher searching efficiency and more than 30% improvement in lift-drag ratio are obtained for every design point. Conclusively, optimal airfoils have three main characteristics. 1） elliptical planform, 2） AR of 1. 2, 3） large leading-edge radius and cusped trailing-edge. This shape yields higher lift coefficient and decreases induced drag greatly, so the performance of MAV wing is improved highly at low Reynolds number.
出处
《空气动力学学报》
EI
CSCD
北大核心
2009年第4期462-468,共7页
Acta Aerodynamica Sinica
关键词
微型飞行器
遗传算法
低雷诺数
小展弦比
人工压缩方法
micro air vehicle
genetic algorithm
low aspect ratio
low Reynolds number
artificial com- pressibility method
