摘要
基于Busemann双翼的设计方法,采用径向基函数神经网络(Radial-Basis Function Neural Network,RBFNN)和基于遗传算法(Genetic Algorithm,GA)的优化技术对Licher双翼进行了优化设计以提高设计马赫数情况下的升阻比。通过计算流体力学(Computational Fluid Dynamics,CFD)方法在无黏性和黏性模式下对优化设计结果进行了验证。结果表明,与典型的Busemann双翼相比,优化后的双翼构型在无黏模拟情况下的升力和升阻比分别提高了27.3%和27.4%,黏性模拟情况下则提升了近60%和40%,表明本文采用的方法对于将双翼构型应用于未来超声速运输机领域具有很大的潜力。
Based on the design method of the Busemann-type biplane,the Radial-Basis Function Neural Network(RBFNN)and Genetic Algorithm(GA)based optimization technique is used to optimize the aerodynamic shape of the Licher biplane to reduce the wave drag at the Mach number of 1.7.The optimization design results were validated by Computational Fluid Dynamics(CFD)method in both inviscid and viscous mode.The results show that the lift and liftto-drag ratio increased 27.3%and 27.4%for inviscid simulation and nearly 60%and 40%for viscous simulation compared with the typical Busemann biplane,indicating that the method adopted in present paper has great potential for the application of the biplane in the field of future supersonic transport.
作者
马博平
王刚
叶坤
叶正寅
Ma Boping;Wang Gang;Ye Kun;Ye Zhengyin(Northwestern Polytechnical University,Xi’an 710072,China)
出处
《航空科学技术》
2019年第9期73-80,共8页
Aeronautical Science & Technology
