Project outline
The noise generated by complex aircraft components, such as landing gear systems, presents a significant scientific and engineering challenge. Reducing this noise is crucial as aircraft manufacturers strive to meet stringent environmental regulations and minimise the acoustic footprint of modern aircraft. Accurately predicting noise from such complex geometries depends on a detailed understanding of the underlying unsteady flow development around these bodies.
In this exciting PhD project, conducted in close collaboration with Safran SLS, we aim to use state-of-the-art computational tools, including the GPU supported Lattice Boltzmann Method (LBM), to characterise the flow field around representative landing gear configurations and uncover the mechanisms responsible for noise generation. The research will combine high-fidelity CFD and Computational Aeroacoustics (CAA) with advanced data-driven analysis techniques such as Proper Orthogonal Decomposition (POD), to identify dominant turbulent structures and their acoustic significance.
The large, high-resolution numerical datasets generated will also be leveraged for Machine Learning (ML) applications, enabling the development of fast and accurate noise prediction and flow reconstruction tools for complex geometries. The outcomes of this work will directly support the design of next-generation low-noise landing gear systems, contributing to quieter and more sustainable aircraft.
The successful candidate will have access to the ProLB (LBM solver) and High-Performance Computing (HPC) facilities at the University of Bristol (UoB), as well as opportunities for experimental validation using the National Wind Tunnel facilities at UoB. The project offers close collaboration with Safran’s world-leading R&D team, including an industrial placement, and opportunities to present research findings at major international conferences.
Project Partners
Working in close collaboration with Safran SLS and researchers at the University of Bristol, you will have access to advanced computational resources and industrial expertise.
Further project information
This PhD project will explore the fundamental fluid–acoustic mechanisms responsible for noise generation by landing gear configurations using advanced numerical simulations and data-driven analysis. The research will employ high-fidelity CFD tools to resolve the fine-scale turbulent structures and their interactions with the gear geometry. CAA modelling will be used to propagate and analyse the radiated sound field.
Beyond traditional CFD, the project will leverage modal decomposition techniques (e.g., POD) to extract coherent flow features and identify dominant noise-producing mechanisms. The resulting numerical datasets will also be exploited for Machine Learning applications, enabling the development of surrogate models for rapid flow and noise prediction. These tools will help accelerate acoustic design optimisation and provide deeper physical insight into the coupling between flow dynamics and noise generation.
The research will advance knowledge in several important areas, including:
- Determination of the types of noise being generated (broadband, tonal, or mixed).
- Identification of the locations and strengths of dominant noise sources.
- Establishment of best practices for numerical, spatial, and temporal resolution to capture noise-producing flow features.
- Development of guidelines for effective noise-reduction technologies in future civil transport aircraft.
The project also offers opportunities to engage with experimental and modelling partners, ensuring that outcomes are both scientifically rigorous and directly relevant to real-world aircraft design.
Subject Areas
- Fluid Dynamics
- Turbulence
- Experimental Aerodynamics
- Aeroacoustics
Required qualifications/skills
Essential
- Minimum 2:1 (or equivalent) in Aerospace, Mechanical Engineering, Physics, Mathematics, or related discipline.
- Programming experience in Python or MATLAB.
Desirable
- Interest in aerodynamics and/or aeroacoustics
Student
Commencing in October 2026