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首页> 外文期刊>International journal of aerospace engineering >Aerodynamic Shape Optimization of a Missile Using a Multiobjective Genetic Algorithm
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Aerodynamic Shape Optimization of a Missile Using a Multiobjective Genetic Algorithm

机译：使用多目标遗传算法的导弹空气动力学优化

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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 SSTk-omega, realizablek-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.
机译：本文的目的是展示形状优化对超音速时的导弹性能的影响。 N1G导弹模型形状变化，在数值上研究了在确定的约束下在超声波流动上增加了其空气动力学阻力并增加了其空气动力学升力。从空气动力学方面的文献研究中选择了导弹几何体。进行导弹空气动力学系数预测，以使用SSTK-OMEGA，REALIZABLYK-EPSILON和SPALART-ALLMARAS湍流模型在超声波马赫数的现有实验结果中验证和比较。在优化过程中，需要估计导弹体和鳍设计参数来设计最佳导弹几何形状。升力和拖动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）用于优化导弹几何形状。改进了身体，主体和尾部的前部，以在超音速时找到最佳导弹模型。优化结果表明，在超声波马赫数下，确定导弹性能的升力系数比例提高了约11-17％。

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