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Inflatable airfoil structure optimization on flying wing Buoyancy-lifting unmanned aerial vehicles

机译:飞翼浮力提升无人机的充气翼型结构优化

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As a kind of entire lifting surfaces to generate lift, flying wing buoyancy-lifting unmanned aerial vehicles (UAVs) have currently raised wide concerns in the field of military affairs and the civil use. The aircraft collects the advantages from fixed-wing UAVs and traditional aerostats to perform better aerodynamic characteristics with less volume. However, it is a challenge to determine the inflatable wing configuration in the basis of the anticipative smooth airfoil. This paper presents a novel inflatable airfoil structure multi-objective optimization design of flying wing buoyancy-lifting unmanned aerial vehicles based on improved non-dominated sorting genetic algorithm (NSGA-II) to achieve an approximate fitting to the ideal model in spite of constraints from various aspects. A combination of difference minimization and mass minimization strategies is used to bring equilibrium between the aerodynamic performance and the mass of the structure with relatively independent decision variables. The resulting structural model has noteworthy capabilities for the inflatable airfoil to represent the rigid smooth one with excellent aerodynamics; therefore, it makes a great sense for the area of the flying wing buoyancy-lifting aircraft.
机译:作为产生升力的整个升力面,目前,在军事和民用领域中,飞翼浮力升力无人机已引起广泛关注。该飞机从固定翼无人机和传统浮空器中汲取了优势,以较小的体积实现了更好的空气动力学特性。然而,在预期的平滑翼型的基础上确定充气翼的构型是一个挑战。本文提出了一种基于改进非支配排序遗传算法(NSGA-II)的新型飞翼浮力提升无人飞行器充气结构设计的多目标优化设计,尽管存在诸多约束,但仍能近似理想模型各方面。差异最小化和质量最小化策略的组合用于通过相对独立的决策变量在空气动力学性能和结构质量之间实现平衡。由此产生的结构模型具有显着的功能,可充气翼型代表了具有出色空气动力学特性的刚性光滑翼型;因此,这对于飞行翼浮力提升飞机的区域意义重大。

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