AI-based modelling of perforated surfaces in complex flows for noise control

About the Project

Supervisory Team: Dr Long Wu

This project uses physics-informed machine learning to model how perforated surfaces contribute to noise reduction in complex flows. You’ll develop a fundamental understanding and predictive tools to advance noise-control design for transport and HAVC systems, translating advanced AI modelling into practical engineering solutions.

Perforated surfaces are widely used for noise reduction under flow conditions. They typically act as a protective layer, preventing the flow from penetrating the acoustic treatment, while simultaneously influencing the dissipation of acoustic energy. Accurately predicting their performance under realistic flow conditions remains a major challenge in aeroacoustics due to the complex coupling between hole geometries, flow, and sound. These effects are difficult to capture theoretically or numerically, limiting our ability to design advanced noise-control systems. This project addresses this challenge by pioneering the use of physics-informed machine learning to model the acoustic response of perforated surfaces in complex flows.

You'll develop high-fidelity models and survey published data to characterise the coupled behaviour between flow, surface geometry, and acoustic excitation across a broad physical regime. Building on this understanding, you will integrate detailed simulation results and experimental datasets into physics-informed neural networks (PINNs), creating robust predictive models that bridge theory, data, and computation. This approach moves beyond traditional semi-empirical methods and computationally expensive high-fidelity simulations, providing a digital framework for predicting and optimising noise-control performance across a wide range of operating conditions.

Working at the intersection of AI, aeroacoustics, and fluid dynamics, you'll gain expertise in physical modelling, numerical simulation, scientific computing, and data-driven modelling. You'll also have the opportunity to collaborate with researchers at LAUM and leading industrial partners. You'll join the Acoustic Group and benefit from a dynamic, interdisciplinary research environment at the Institute of Sound and Vibration Research (ISVR), with access to state-of-the-art facilities, including one of the largest HPC facilities in the UK, and a vibrant postgraduate community. Opportunities include international conferences, workshops, training events, and engagement with a global network of researchers and industry experts.

Entry requirements

You must have a UK 2:1 honours degree, or its international equivalent, in one of the following:

• engineering
• maths
• physics
• computer science

Fees and funding

We offer a range of funding opportunities for both UK and international students. Horizon Europe fee waivers automatically cover the difference between overseas and UK fees for qualifying students.

Competition-based Presidential Bursaries from the University cover the difference between overseas and UK fees for top-ranked applicants.

Competition-based studentships offered by our schools typically cover UK-level tuition fees and a stipend for living costs for top-ranked applicants.

For international students, several external scholarships are available depending on eligibility and country of origin.

Funding will be awarded on a rolling basis, so apply early for the best opportunity to be considered.

For more information, please visit our postgraduate research funding pages.

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)