## 4 Conclusions  
This study was conducted to perform aerodynamic optimization of Morphing Trailing Edge (MTE) and Droop Nose Leading Edge (DNLE) airfoils for the UAS-S45 at different flight conditions. A new hybrid optimization technique was chosen by coupling the Particle Swarm Optimization (PSO) with the Pattern Search (PS) algorithms. The optimization function was designed to minimize drag with respect to given constraints such as airfoil lower and upper bounds, as well as to increase endurance in given flight conditions. The Bezier-PARSEC (BP) technique was used to parameterize the baseline airfoil shape, as well as to obtain its various optimized morphing configurations by using different constraints on the morphing of both the Morphing Trailing Edge (MTE) and the Droop Nose Leading Edge (DNLE) of the wing. Within the aerodynamics optimization framework, the low-fidelity solver XFoil and the high-fidelity CFD solver Ansys Fluent were used. The results obtained using both solvers were compared for their validation. Specifically, the flow transition was predicted using Menter's Transition \((\gamma-R\epsilon_{\theta})\) SST turbulence model. In addition, the optimization framework was done from an aerodynamics perspective, and therefore, no structural studies were taken into consideration. Both DNLE and MTE airfoil optimizations were aimed to increase the aerodynamic performance of the UAS-S45 for a wide range of angles of attack. For each of the three considered flight conditions, enhanced aerodynamic efficiency was obtained by optimizing the morphing airfoil design with respect to its UAS-S45 baseline airfoil. The optimization results have shown an increase of lift coefficients in DNLE airfoils until the stall angle of attack and thus a delay in the stall angle. An improvement in the lift coefficient was produced for the DNLE airfoils with respect to the baseline airfoil of up to 21%. In addition, an increase of 9.6% was obtained for the maximum lift coefficient, and the stall angle was also delayed by 3 degrees. Similarly, the aerodynamic performance showed a significant improvement for the MTE configurations. An increase in the maximum lift coefficient of up to 8.13% and of the efficiency of \(\text{CL}^{3/2}\text{/C}_{\text{D}}\) by 10.25% was obtained, thus indicating an  
Figure 26: Velocity magnitude and Turbulent Kinetic Energy (TKE) contour plots. (**a**) Velocity contour lines at low angles of attack such as 4\({}^{\circ}\); (**b**) Velocity contour lines at low angles of attack such as 10\({}^{\circ}\); (**c**) the turbulent intensity at a low angle of attack of 4\({}^{\circ}\); (d) the turbulent intensity at a low angle of attack of 10\({}^{\circ}\). increased endurance performance for the MTE airfoils of the UAS-S45. The delay of the leading edge separation by use of DNLE airfoils was another interesting result. The flight conditions were chosen from the UAS-S45 manufacturer Hydra Technology user manual. These conditions do not cover aircraft maneuvers or gust responses. UAS-S45 maneuvers and gust response studies could be done in the future. Other objectives of this morphing optimization study will include the three-dimensional analysis of a UAS-S45 wing with a combined droop nose leading edge (DNLE) and continuous morphing trailing edge (MTE). The improvements at the performance level, such as the typical fuel savings with the optimized morphing concepts for a given flight mission, will also be studied in the future. Based on the aero-structural studies, various configurations will be analyzed, and the internal actuation mechanism will be implemented. Wind tunnel and flight tests could be further performed to validate the optimized UAS-S45 model. Conceptualization; methodology, M.B. ; software, S.L.-M., and M.B. ; validation, M.B. ; investigation, M.B., and S.L.-M.; writing--original draft preparation, M.B. ; writing--review and editing, R.M.B. ; visualization, M.B., S.L.-M. and R.M.B. ; supervision, R.M.B. ; and T.W. ; funding acquisition, R.M.B.