Multi-objective design optimization of morphing UAV aerofoil/wing using hybridised MOGA

The paper investigates two advanced Computational Intelligence Systems (CIS) for a morphing Unmanned Aerial Vehicle (UAV) aerofoil/wing shape design optimisation. The first CIS uses Genetic Algorithm (GA) and the second CIS uses Hybridized GA (HGA) with the concept of Nash-Equilibrium to speed up the optimisation process. During the optimisation, Nash-Game will act as a pre-conditioner. Both CISs; GA and HGA, are based on Pareto optimality and they are coupled to Euler based Computational Fluid Dynamic (CFD) analyser and one type of Computer Aided Design (CAD) system during the optimisation. For the practical test case, one type of morphing techniques; Leading and Trailing Edge Deformation (LTED) is considered to control flow over the aerofoil/wing. LTED to a Natural Laminar Flow (NLF) aerofoil is applied to maximise the lift coefficients (Cl) at both the take-off and landing conditions. Two applications on LTED with low/middle and high design complexities are optimised using GA and HGA. The optimisation efficiency for GA and HGA are compared in terms of computational cost and design quality. Numerical results clearly show that Nash-Game helps a GA based CIS to accelerate the optimisation process and also to produce higher performance solutions in solving both the low/middle and high complex design optimisation problems. In addition numerical CFD study demonstrates that the implementation of morphing technique on the aerofoil/wing significantly improves the lift coefficients at both the take-off and landing conditions when compared to the baseline design.