A strongly coupled computational-experimental framework is currently being developed. It relies on innovative analytical formulations, approximations and model reduction using artificial neural networks, large multi-objective optimizations, high-fidelity simulation methodologies, and advanced test techniques. The computational models include a rapid tool for calculating different multi-stable configurations through analytical formulations and deep learning, as well as high-fidelity finite element models to ensure structural reliability in the post-buckling regime.
Given the sensitivity of the buckling phenomenon to various parameters, we are pursuing a robust and reliability-based design optimization strategy. This involves considering the key parameters influencing the buckling phenomena from the initial design stages, treating structures and materials no longer as two separate entities. Additionally, managing multi-stability and the required stiffness to support loads poses challenges, as repeated stable-mode switches can cause fatigue damage. Therefore, we are studying low fatigue algorithms to create more efficient and durable structures.
Our design efforts for buckling focus on increasingly complex aircraft structures. We explore various concepts at the panel level, analyze these concepts on wingbox structures, manufacture and test the most promising ideas, and finally design morphing buckling-driven solutions for adaptive wings.
We have investigated new solutions to modify the aircraft wing shape, primarily the twist span-wise, for various flight conditions by designing selected elements that can switch into different post-buckling configurations as needed. This approach allows us to adjust the structures to the desired aerodynamic shape with minimal energy requirements.
