In this pillar, our interest is to develop tools to understand and predict nonlinear mechanics of aerospace structures with a particular focus on damage evolution and  buckling. We use a combination of nonlinear finite element modelling, Variational Asymptotic Method, and damage models to dimensionally reduce complex aerospace structures to beam or plate models made of composite materials.

In this pillar, our research focusses on enabling multifunctionalities in aerospace structures such as self-healing, electromagnetic energy absorption, energy recycling, storage, morphing and sensing. We extensively use multphyiscs and multiscale modelling in combination with experiments to realise multifunctional material/structural systems.  Our research is supported by funding from Royal Society and we collaborate with Airbus.

In this cross-cutting research pillar, we develop and employ deep learning models for prediction and design of materials, structures and systems. We believe the appropriate fusion of AI architectures with mechanics-driven simulation frameworks will yield efficient tools for aerospace structural design.  Some projects include hybrid physics+ML simulation architectures for damage simulations (EPSRC), generative deep learning for composite material and rotor blade design (Industry), and damage monitoring in aerospace structures using magneto-electro-elastic composites (IMechE Astridge Fund and George Daniels Scholarship).