Project outline
Aircraft noise remains a major environmental and public health concern, with landing gear systems representing a significant contributor to the overall noise signature during approach and landing. Understanding the complex mechanisms behind landing gear noise generation requires detailed knowledge of the turbulent flow development around these intricate geometries.
The project will combine experimental and analytical approaches to investigate the aerodynamic and aeroacoustics behaviour of realistic landing gear configurations.
The extensive datasets obtained from these experiments will support the application of AI and Machine Learning techniques for the development of fast and reliable noise prediction tools. This project offers an excellent opportunity to combine hands-on experimental work with advanced data-driven and AI-based modelling approaches, providing a holistic training in modern aeroacoustics research.
The successful candidate will have access to the National Aeroacoustics Wind Tunnel and state-of-the-art measurement facilities, including high-speed PIV systems, hot-wire anemometry, and beamforming microphone arrays. The project also offers collaboration with industry experts, a placement at Safran, and opportunities to present research findings at leading international conferences.
Project Partners
In this exciting PhD project based at University of Bristol and conducted in close collaboration with Safran SLS, we aim to advance the fundamental understanding of noise generation in realistic landing gear systems.
Further project information
This PhD project will experimentally investigate the aerodynamic and aeroacoustics characteristics of landing gear systems using advanced measurement techniques and data-driven analysis. The focus will be on identifying the dominant flow structures responsible for noise generation and understanding their interactions with complex geometrical features. Key objectives include:
- Characterise the turbulent flow field and associated acoustic signatures across different landing gear configurations.
- Apply modal decomposition and statistical analysis (e.g., POD, SPOD) to identify dominant noise-generation mechanisms.
- Generate high-fidelity benchmark datasets for validation of computational and AI-based aeroacoustic prediction models.
- Support the design and assessment of next-generation low-noise landing gear concepts.
Working closely with Safran SLS and the University of Bristol’s aeroacoustics research group, the successful candidate will gain experience in experimental fluid dynamics, aeroacoustics, and data-driven and reduced order modelling.
Subject Areas
- Fluid Dynamics
- Aeroacoustics
- Turbulence
- Experimental Methods
Required qualifications/skills
Essential
- Minimum 2:1 (or equivalent) in Aerospace, Mechanical Engineering, Physics, Mathematics, or related discipline.
Desirable
- Interest in aerodynamics, aeroacoustics and flow-induced noise.
- Experience with wind tunnel testing, PIV, or microphone array measurements.
Student
Commencing in October 2026