Project Outline
Advanced Air Mobility could improve connectivity, logistics and transport efficiency, but community noise remains a major barrier to its widespread acceptance. This project will develop methods to predict and reduce the noise generated by electric vertical take-off and landing (eVTOL) aircraft during the transition between vertical and forward flight. It will combine physical modelling, experimental data, auralisation and artificial intelligence or machine learning to investigate quieter aircraft operations.
Further project information
The Advanced Air Mobility sector is growing rapidly through the development of electric vertical take-off and landing (eVTOL) aircraft and other novel aerial vehicles. However, community noise remains a major barrier to their widespread acceptance and deployment.
This project will focus on the transition between vertical and forward flight, which is characterised by highly transient aerodynamic and aeroacoustic phenomena that can significantly influence both overall noise levels and sound quality. The student will develop and extend reduced-order noise-source models for eVTOL aircraft during transitional manoeuvres. Laboratory-scale experiments and outdoor flight-test data will be used to investigate the dominant noise-generation mechanisms and establish scalable prediction methods. These source models will then be incorporated into whole-aircraft noise prediction tools to assess community exposure and perceived sound quality.
The project will also investigate quieter operating strategies. Artificial intelligence or machine-learning methods may be used to support trajectory optimisation, identifying flight paths and operating conditions that reduce community noise while accounting for relevant aircraft and operational constraints.
In partnership with ARUP, the project will incorporate full-scale acoustic measurements from operational aircraft, providing validation data and benchmarks for the modelling approaches.
Subject Areas
- Aeroacoustics;
- Acoustics;
- Aerospace engineering;
- Advanced Air Mobility;
- eVTOL aircraft; aircraft noise;
- Trajectory optimisation;
- Artificial intelligence;
- Machine learning;
- Auralisation;
Required technical skills/academic qualifications/experience
Applicants should have, or expect to obtain, a UK 2:1 honours degree or its international equivalent in engineering, physics, applied mathematics, computer science or a related discipline.
Experience in aerospace engineering, particularly flight mechanics, aircraft operations or aeroacoustics, would be advantageous but not essential. Programming experience in MATLAB or Python, or an interest in optimisation, artificial intelligence or machine learning, would also be useful.
You do not need experience of every method described above. Training will be provided, and we value curiosity, collaboration, persistence and a willingness to learn. We welcome applicants from a wide range of backgrounds and particularly encourage applications from women and other groups under-represented in research. All appointments will be made on merit.
Mode of attendance
Successful applicants are expected to register as full-time students. However, eligible UK home fees applicants may request to be considered for part-time mode of attendance by the supervisory team.
Supervisors
Project Partners
This project will be hosted at the University of Southampton with support from ARUP
