摘要
采用"试验设计+响应面模型+遗传算法"的优化设计体系,结合压气机设计常用的叶片造型程序和流场模拟软件,搭建了轴流压气机叶片三维优化设计平台,并对某涡喷发动机加零级压气机的零级转子进行了优化.优化目标为极大化转子的设计点绝热效率.约束条件为流量、增压比基本不变.分别以相对气流角和气流脱轨角作为优化自变量,进行了两个算例的优化.即为与现代设计系统相接轨,不同于叶片几何参数优化,取设计中具有物理含义的气动参数作为优化自变量.优化后的绝热效率分别提高了0.82和0.73个百分点.
Employing the strategy of ＂experimental design＋ response surface model ＋ genetic algorithm＂,a response surface model code based on the artificial neural network and a genetic algorithm code were developed;and this paper established a numerical aero-optimization platform for three-dimensional（3-D） axial-flow compressor blade.This integral platform contains also the commonly used arbitrary camber line airfoil and blade formatting code and 3-D computational fluid dynamics solver.A fulfilled inverse-design of a zero-stage compressor rotor of a turbojet engine was optimized by this platform.The optimization object is to achieve the maximum adiabatic efficiency at the design point,while the mass flow rate and total pressure ratio are kept unchanged as the constraints.The relative flow-angle and the inner-blade deviation-angle are chosen separately as the independent-variables in two optimization cases.In order to directly couple the optimization method with the modern design system,the aerodynamic variables with physical meanings were chosen instead of the commonly used geometric variables as the optimization independent-variables.As compared with the original rotor,the adiabatic efficiencies of two optimal rotors increase by 0.82 and 0.73 percentage points,respectively.It shows that 3-D optimization method based on the aerodynamic variables has a good performance.
出处
《航空动力学报》
EI
CAS
CSCD
北大核心
2010年第4期884-890,共7页
Journal of Aerospace Power
基金
中航工业燃气涡轮研究院外委课题
关键词
轴流压气机
叶片造型
气动数值优化
人工神经网络
遗传算法
axial flow compressor
airfoil and blade formatting
numerical aerodynamic optimization
artificial neural network
genetic algorithm
