The aim of this paper is to demonstrate the effects of the shape optimization on the missile performance at supersonic speeds. The N1G missile model shape variation, which decreased its aerodynamic drag and increased its aerodynamic lift at supersonic flow under determined constraints, was numerically investigated. Missile geometry was selected from a literature study for optimization in terms of aerodynamics. Missile aerodynamic coefficient prediction was performed to verify and compare with existing experimental results at supersonic Mach numbers using SST -omega, realizable -epsilon, and Spalart-Allmaras turbulence models. In the optimization process, the missile body and fin design parameters need to be estimated to design optimum missile geometry. Lift and drag coefficients were considered objective function. Input and output parameters were collected to obtain design points. Multiobjective Genetic Algorithm (MOGA) was used to optimize missile geometry. The front part of the body, the main body, and tailfins were improved to find an optimum missile model at supersonic speeds. The optimization results showed that a lift-to-drag coefficient ratio, which determines the performance of a missile, was improved about 11-17 percent at supersonic Mach numbers.

中文翻译：

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基于多目标遗传算法的导弹气动形状优化

本文的目的是演示形状优化对超声速下导弹性能的影响。在确定的约束条件下，对N1G导弹模型的形状变化进行了数值研究，该变化在超音速流下减小了空气阻力，增加了空气动力升力。从文献研究中选择了导弹的几何形状，以优化空气动力学。进行导弹空气动力系数预测验证并使用SST与超音速马赫数现有的实验结果进行比较-欧米加，可实现的-epsilon和Spalart-Allmaras湍流模型。在优化过程中，需要估算导弹的主体和鳍片设计参数，以设计最佳的导弹几何形状。升力和阻力系数被认为是目标函数。收集输入和输出参数以获得设计点。多目标遗传算法（MOGA）用于优化导弹的几何形状。改进了机体的前部，主体和尾鳍，以便在超音速下找到最佳的导弹模型。优化结果表明，决定导弹性能的升阻系数比在超音速马赫数下提高了约11-17％。