Multidisciplinary Design Optimization of an UAV Wing Using Kriging Based Multi-Objective Genetic Algorithm

Abstract

This paper investigates the preliminary wing design of Unmanned Aerial Vehicle (UAV) using a two step optimization approach. The first step is a single objective aerodynamic optimization whereas the second step is a coupled dual objective aerodynamic and structural optimization. In the single objective case, 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. Constraints are imposed on the leading edge curvature, trailing edge radius, zero lift drag coefficient and zero lift moment coefficient. After arriving at the optimized airfoil geometry, the wing planform parameters are optimized with minimization of wing weight and maximization of endurance parameter corresponding to the wing and four more design variables from the aerodynamics discipline namely taper ratio, aspect ratio, wing loading and wing twist are added in the second step. 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 with stall speed, maximum speed, rate of climb, strength and stiffness as constraints. The 2D airfoil and 3D wing aerodynamic analysis is performed by the XFLR5 code and the structural analysis is performed by the MSC-NASTRAN software. In the optimization process, a relatively newly developed multi-objective evolutionary algorithm named NSGA-II (non-dominated sorting genetic algorithm) is used to capture 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 the UAV wing.