摘要
本文将粘性流场分析与数值优化方法结合起来,由粘性流场分析得到升力、阻力等气动参数作为样本训练神经网络,并用训练好的神经网络来预测优化目标函数,分别采用了多目标遗传算法与多目标粒子群算法,对一种跨音速翼型的气动性能进行了多目标优化设计,并采用模糊偏好信息的多属性决策方法对多个优化解进行评价选优。算例研究表明,两种多目标优化算法都能得到有限多个多目标优化解,通过多属性决策方法评价选优的优化翼型气动性能有明显提高。
A multi-objective optimization design method of transonic airfoils was developed which couples viscous flow analysis and numerical optimization to search for airfoil geometry with improved aerodynamic performance.Aerodynamic quantities such as lift,drag were computed by the flow solver and were used as samples to train artificial neural net(ANN)to define the objective functions of the Multi-objective genetic algorithm(MOGA)and multi-objective particle swarm algorithm(MOPSO). A multi-attribute decision makin...
出处
《工程热物理学报》
EI
CAS
CSCD
北大核心
2008年第7期1129-1132,共4页
Journal of Engineering Thermophysics
基金
国家自然科学基金(No.50725621)
关键词
跨音速翼型
优化设计
多目标优化
多属性决策
transonic airfoil
optimization method
multi-objective optimization
multi-attribute decision making
