Autonomous Smart Morphing Wing

Abstract

With the increasing desire of the aerospace industry to reduce emissions and fuel consumption, morphing wings have gained much interest due to the ability to adapt the wing shape in-flight for improved energy efficiency and aerodynamic performance. Active wing morphing is a technology that can improve aerodynamic performance continuously through different flight phases. However, a multidisciplinary approach is needed, which integrates the design, modelling, sensing and control methodologies in a multi-objective framework, and allows the smart autonomous morphing wing system to adapt its shape autonomously.

The SmartX project was initiated for this purpose at the Delft University of Technology, Faculty of Aerospace Engineering, Department of Aerospace Structures and Materials, aiming to investigate the energy-efficient wing concepts through smart wings.

This dissertation presents the Development, Realisation & Validation of a smart morphing wing, the SmartX-Alpha, capable of meeting various real-time objectives with distributed seamless morphing modules. This is done through a holistic approach considering all building blocks of a morphing system presented in four Parts of the dissertation.

Part I tackles the sensing approach required to reconstruct the shape of the wing in real-time with a vision-based sensing approach. Part II presents the design, development, realisation and experimental testing of a distributed modular morphing concept, SmartX-Alpha. Part III presents the multi-objective control framework developed to meet the gust and manoeuvre load alleviation objective and the real-time shape optimisation strategy to improve online aerodynamic performance. Furthermore, a vision-based control strategy is proposed to mitigate nonlinearities in the actuation system arising from mechanical imperfections. A series of wind tunnel experiments are conducted in the OJF to validate the methodologies on the SmartX-Alpha, ensuring the objectives are satisfied autonomously, in-real time. The final Part, Part IV presents the development of a second wing demonstrator, the SmartX-Neo, with distributed discretised control surfaces incorporating the previous learnings.