About the project
The engineering design process for next generation aerospace engineering products is being revolutionised by the rapid development of artificial intelligence and machine learning technologies. Data-driven reduced order models have a significant influence and are increasingly integrated into the design and manufacture of aerospace engineering systems. This improves their automation, resilience, and efficiency.
However, their influence is limited when simulating the behaviours of increasingly complex and large dynamical systems. This is due to nonlinearities and uncertainties introduced by advanced lightweight materials, and multi-physics couplings between the structure and fluids, electrics, or tribological interfaces.
The project aims to shift the nonlinear modelling paradigm from using linear or linearised data driven approaches to truly nonlinear and universal approaches. The project will focus on overcoming these limitations by combining recent advances in the nonlinear dynamics theory.
You will explore and develop next-generation versatile and unsupervised data-driven modelling techniques. You will review the state-of-the-art data-driven reduced order techniques for:
- nonlinear dynamical systems such as dynamic mode decomposition
- sparse modelling techniques
- Koopman theory
You will then expand current capabilities through the recent invariant manifold based reduced order modelling techniques. You will test this data-driven modelling framework and validate it using the following complex aerospace systems across scales and physics:
- nonlinear aero-engine mechanical structure
- complex aeroelastic systems
- high-performance electric-mechanical systems
You will join a world-leading research team within the Computational Engineering and Design research group in the Department of Aeronautics and Astronautics. The research group is deliciated to developing advanced computational methods for design and optimisation problems in fluids, structures, control systems and biomechanics. It performs research with a number of large aerospace companies including Airbus and Rolls Royce plc through the Airbus Noise Technology Centre and Computational Rolls Royce University Technology Centre hosted in the Department.
