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

机译:充气翼型结构优化在飞行翼浮动升降无人航空车上

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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:作为一种生成电梯的整个升降表面,飞行翼浮动升降的无人机(无人机)目前在军事和民用使用领域提出了广泛的关注点。该飞机收集来自固定翼无人机和传统空气硬盘的优势,以更好的空气动力学特性,体积较少。然而,基于预期平滑翼型来确定充气翼配置是挑战。本文提出了一种新型充气翼型结构,基于改进的非统治分类遗传算法(NSGA-II),实现了飞行翼浮力升降型航空车辆的多目标优化设计,以实现对理想模型的近似配合各方面。差异最小化和质量最小化策略的组合用于在空气动力学性能与具有相对独立的决策变量的结构之间的平衡。由此产生的结构模型对于充气翼型来表示具有优良空气动力学的刚性光滑件的能力;因此,它对飞行翼浮力升降机的区域产生了极大的意义。

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