This is a potential research project for new research students. It is intended to be a starting point for discussion/further investigation rather than a complete research plan. If you are interested in working with me, see opportunities for new researchers.
Keywords: aeroelastics, scientific machine learning, control-based continuation, nonlinear dynamics
There is a drive towards high-efficiency and high-performance aircraft. This requires lighter and more fuel-efficient structural designs, which are often more flexible than traditional rigid designs. This flexibility can result in disastrous nonlinear behaviour; for example, the destruction of the NASA Helios prototype. This project seeks to develop new approaches to nonlinear behaviour in aircraft and so enable a new generation of low-carbon aircraft.
Within our well-equipped test facilities at the University of Bristol, Prof Mark Lowenberg and colleagues have developed a manoeuvre rig based on a scale model of a Hawk trainer aircraft. This 5 degree-of-freedom model provides an ideal test bed for investigating nonlinear aerodynamic behaviour. To explore the complex nonlinear dynamics, Prof David Barton has developed a range of experimental techniques known as Control-Based Continuation (CBC) that can track the onset of instabilities as system parameters change (e.g., the onset of flutter at a critical airspeed, Lee et al, 2023). As such, CBC can be used to investigate behaviour in the physical system that would have previously been out of reach. The combination of the manoeuvre rig and CBC opens up many possibilities for exploitation.
The data generated from these experiments is ideal for building new models and can be used in combination with scientific machine learning to create hybrid models: high-fidelity models that combine physics-based modelling with data-driven approaches. These models will then enable further design work to either mitigate or take advantage of the nonlinear behaviour in the experiment.
The overall aims are threefold:
- To generate industrially-relevant insights from the manoeuvre rig.
- To extend CBC to multi-degree-of-freedom systems, with application to other engineered structures.
- To develop a hybrid modelling approach for aerospace systems that combines machine learning with physics-based models.
If you are interested in working on this or a related project, please email david.barton@bristol.ac.uk with a copy of your CV and a short description of your research interests; we can co-develop a more complete research statement tailored to you. Applications from all backgrounds are encouraged, especially under-represented groups.