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Multidisciplinary Design Optimization of Long Endurance Unmanned Aerial Vehicle Wing

机译:长航时无人机机翼的多学科设计优化

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摘要

The preliminary wing design of a low speed, long endurance UAV is formulated as a two step optimization problem. The first step performs a single objective aerodynamic optimization and the second step involves a coupled dual objective aerodynamic and structural optimization. During the first step, airfoil geometry is optimized to get maximum endurance parameter at a 2D level with maximum thickness to chord ratio and maximum camber as design variables. Leading edge curvature, trailing edge radius, zero lift drag coefficient and zero lift moment coefficient are taken as constraints. Once the airfoil geometry is finalized, the wing planform parameters are optimized with minimization of wing weight and maximization of endurance. Four design variables from aerodynamics discipline namely taper ratio, aspect ratio, wing loading and wing twist are considered. Also, four more design variables from the structures discipline namely the upper and lower skin thicknesses at root and tip of the wing are added. Constraints are stall speed, maximum speed, rate of climb, strength and stiffness. The 2D airfoil and 3D wing aerodynamic analysis is performed by the XFLR5 panel method code and the structural analysis is performed by the MSC-NASTRAN finite element code. In the optimization process, a multi-objective evolutionary algorithm named NSGA-II (non-dominated sorting genetic algorithm) is used to discover the full Pareto front for the dual objective problem. In the second step, in order to reduce the time of computation, the analysis tools are replaced by a Kriging meta-model. For this dual objective design optimization problem, numerical results show that several useful Pareto optimal designs exist for the preliminary design of UAV wing.
机译:低速,长寿命无人机的初步机翼设计被表述为两步优化问题。第一步执行单目标空气动力学优化,第二步涉及耦合的双目标空气动力学和结构优化。在第一步中,优化机翼几何形状,以在2D级别获得最大耐力参数,并以最大厚度与弦长比和最大外倾角为设计变量。前缘曲率,后缘半径,零升阻力系数和零升力矩系数均作为约束条件。一旦机翼的几何形状最终确定,机翼平面参数就可以通过最小化机翼重量和最大耐力来优化。考虑了空气动力学学科的四个设计变量,即锥度比,纵横比,机翼载荷和机翼扭曲。此外,还增加了来自结构学科的四个设计变量,即机翼根部和尖端的上,下蒙皮厚度。限制因素包括失速速度,最大速度,爬升率,强度和刚度。通过XFLR5面板方法代码执行2D翼型和3D机翼空气动力学分析,并通过MSC-NASTRAN有限元代码执行结构分析。在优化过程中,使用名为NSGA-II(非支配排序遗传算法)的多目标进化算法来发现双目标问题的完整Pareto前沿。在第二步中,为了减少计算时间,将分析工具替换为Kriging元模型。对于这个双重目标设计优化问题,数值结果表明,存在几种有用的帕累托最优设计,用于无人机机翼的初步设计。

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