Methodology for the design and optimization of a morphing wing droop-nose structure for greener aircraft

Droop-Nose Leading Edge (DNLE) morphing wings are one of the most promising devices in order to achieve aerodynamic drag and noise reduction during take-off and landing phases. An accurate design of these structures could lead to the decrease of aircraft fuel consumption in the perspective of reaching a greener aviation, following the objectives indicated by Flightpath 2050 issued by the E.U. However, due to the challenges related to the realization of this technology and TRL reached, DNLE are more likely implemented in Unmanned Aerial Systems (UAS) for testing and evaluation purposes. In the present study, an optimization methodology for the DNLE composite laminate skin and morphing mechanism structure is proposed and applied to a study case represented by the UAS-S45 aircraft. The work starts from the morphing leading edge structure developed by the LARCASE laboratory at ETS Montreal. The results showed that by means of the optimization strategy adopted, the force required on the actuator mechanism is 88% lower than the original design. A significant improvement on the profile smoothness along its section and in the spanwise direction in morphing conditions has been obtained too. However, further investigations are still needed in order to achieve a more appropriate morphing shape. Despite this, it appears from the results obtained that the proposed methodology can be useful to tackle the DNLE design problem in an effective and efficient way. What developed in this work has been conceived to support the investigation of DNLE in the small leading edge profiles typical of the UAS. In this way, an easier procedure for the set up of the design flow, and a decrease in the computational effort for the optimization process can be obtained. An experimental validation of the results obtained is currently being performed at ETS, and future development regards the assessment of the errors of the numeric procedure herein presented respect to real data.