Machine Learning for Air-breathing Hypersonic Propulsion

About the Project

This PhD project is to develop and apply Machine Learning (ML) to address the aerodynamic problems associated with air-breathing hypersonic propulsion systems. The focus of this work will be on the SCRAMJET engine design and operation. A key issue with these types of engines is the design of the air-intake and the implications for the local flow field. The project will apply ML to evaluate previous design work and studies on these engines to be able to identify solutions for in-take design as this would be a key feature of any flight vehicle. Therefore, understanding the local flow characteristics such as shock wave boundary layer interaction is key for developing a successful intake. The application of ML is to investigate through evaluation and de-risking a suitable design to take forward for modelling the proposed intake through Computational Fluid Dynamics (CFD) this approach will allow for the proposed design to be evaluated and compared with current state-of-the-art design. Upon completion of this project, you will be an expert in high-speed propulsion design and will have developed skills in numerical and computational modelling and machine leaning.

Funding Notes

there is no funding for this project