Project Outline
Aircraft noise is a major barrier to sustainable aviation. As aeroengines adopt larger, slower fans, broadband noise generated by turbulence interacting with outlet guide vanes (OGVs) is becoming increasingly important yet remains difficult to predict. Engineering models rely on simplified turbulence inputs that do not fully capture the three-dimensional fan wake turbulence characteristics in real fan stages.
This project will use scale-resolving Computational Fluid Dynamics (CFD) to identify the flow features and key turbulence statistics that drive broadband noise and use these insights to develop fast and accurate prediction models. The resulting models will enable reliable noise prediction during aeroengine design, supporting the development of quieter and more efficient fan systems.
Further project information
The research question of this PhD project is:
How do spanwise-varying turbulent characteristics within a fan stage drive broadband noise generation at the outlet guide vanes (OGVs), and which turbulence statistics are essential to accurately predict this noise in engineering models?
The objectives include:
- Characterise the fan wake turbulence statistics and validate against available experimental data.
- Identify key turbulence characteristics driving broadband noise generation.
- Develop and validate improved engineering prediction models.
Approaches
The project will combine scale-resolving CFD, turbulence analysis and aeroacoustic modelling, with validation against available experimental data.
- Scale-resolving CFD: LES or DES of representative fan/OGVs stages at low-speed operating conditions.
- Turbulence characterisation: Spanwise distributions of turbulence intensity, length scales, spectra, spatial coherence, etc., upstream of the OGVs, with comparison against theoretical models and available experimental data.
- Aeroacoustic analysis: Analyse OGVs unsteady loading and broadband response, quantify the relationship between the acoustic response and upstream turbulence characteristics to identify which flow regions and turbulence statistics matter most.
- Model development and validation: Use turbulence statistics extracted from CFD to develop spanwise-varying inputs for semi-analytical models, and assess the resulting predictions against existing models, CFD and available experimental data.
Industry Involvement
Rolls-Royce will provide guidance and support on:
- Use of their turbomachinery CFD solver.
- Representative low-speed fan and OGVs geometries, operating conditions and validation data.
- Technical feedback through regular project meetings.
The successful candidate will have the opportunity to engage directly with Rolls-Royce engineers, helping ensure the models developed are relevant to real design needs.
Subject Areas
- Aeroacoustics;
- Acoustics,
- Fluid Mechanics,
- Computational Fluid Dynamics,
- Turbomachinery,
- Propulsion,
- Aeroengines,
- Aerospace Engineering,
- Acoustic Engineering,
- Mechanical Engineering
Required technical skills/academic qualifications/experience
A UK 2:1 honours degree (or international equivalent) in Engineering, Mathematics, Physics or a closely related discipline.
Desirable skills/experience:
- Experience of CFD, ideally for turbomachinery or other internal flows.
- Knowledge of scale-resolving turbulence modelling approaches (e.g. LES, DES).
- Experience of data analysis, particularly statistical analysis of large datasets.
You do not need experience in every area listed above. Training will be tailored to the successful candidate’s background and needs, with opportunities to develop expertise throughout the project. 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.
Prior to appointment prospective students will have to undergo Basic Personnel Security Standard (BPSS) checks.
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 Rolls-Royce.
