Turbulent Boundary Layer Noise

About the Project

Supervisory Team: Prof Neil Sandham

Noise production by turbulent boundary layers is an important practical problem that remains a challenge for prediction methods. On this industry-supported project you'll apply new techniques of large-scale numerical simulation and machine learning to understand the flow physics and develop new prediction methods.

The project relates to a challenging flow regime in which simultaneously the Reynolds number is large, but the Mach number is small, and we need to predict acoustic properties that may have small energy compared to the hydrodynamics. We are interested in developing efficient simulation techniques based on scale-resolving methods and improving prediction methods, including new data-driven methods. The project is particularly attractive as it is supported by industry (Thales Group) with opportunities for company placements and will include a top-up to the basic stipend.

On this project you'll gain experience with state-of-the-art simulation methods, using exascale-level high performance computers, based on modern heterogenous GPU/CPU computer architectures, and gain experience with large data processing techniques as well as modern machine learning methods, for example based on neural networks. You'll join a team of researchers studying problems in high and low speed flows using scale-resolving simulation techniques, including all aspects of transition and turbulent flow and aeroacoustics.

You'll be based in the Department of Aeronautics and Astronautics at the Boldrewood Innovation Campus in Southampton, benefiting from a large community of PhD and post-doctoral researchers. You'll have opportunities to present your work internally as well as to attend and present at international conferences. Training will be provided to get you up to speed in running the code-generation framework to generate code for high performance computer applications. You'll also be able to take advanced modules to further develop your skills.

Entry requirements

You must have a UK First-class honours degree, or its international equivalent, in fluid mechanics or aerodynamics.

Fees and funding

This is a fully funded UKRI-EPSRC research project, delivered through an Industrial Cooperative Award in Science and Engineering (CASE) in collaboration with Thales Ltd.

How to apply

Apply now

You need to:

• choose programme type (Research), 2026/27, Faculty of Engineering and Physical Sciences
• select Full time or Part time
• search for programme PhD Engineering & the Environment (7175)
• add name of the supervisor in section 2 of the application

Applications should include:

• your CV (resumé)
• 2 academic references
• degree transcripts and certificates to date
• English language qualification (if applicable)