A Pareto Multi-Objective Optimization of a Camber Morphing Airfoil using Non-Dominated Sorting Genetic Algorithm

This study uses a multi-objective Non-Dominated Sorting Genetic Algorithm to optimise the aerodynamics of a well-known UAV, the UAS-S45. The optimization algorithm is combined with updated Class Shape Transformation (CST) parameterization to improve aerodynamic performance by increasing lift-to-drag ratio and aerodynamic endurance at various angles of attack. The reference airfoil is parameterized using the CST to give local shape changes and skin flexibility for optimum morphing airfoil combinations. The optimization scheme was carried out with an in-house MATLAB code and this procedure is based on a multi-objective Non-Dominated Sorting Genetic Algorithm coupled to XFoil solver, and validation is done using the SST-K Omega model. The results of the optimizations carried out using different operating conditions are presented; starting from the optimal Pareto fronts, several solutions are selected and compared in terms of airfoil shapes and performance. The results show that the morphing improves the UAS-S45 airfoil's aerodynamic efficiency. The improved airfoils have shown a high improvement in overall aerodynamic performance by up to 65.3% in lift to drag ratio at 8 degrees angle of attack compared to the reference airfoil, and an increase in C_L^(3/2)/C_D of up to 98.8% at 8 degrees for the UAS-S45 optimized airfoil configurations. The optimization increases the overall aerodynamic performance of the configuration and the stall angle from 12º to at least 16º. The method used in this work can be employed as a valuable tool for replacing the traditional slats and flaps at the leading edge and the trailing edge of the airfoil. The pareto optimization analysis will be presented with the optimization results and numerical analysis using high fidelity CFD.