Experimental Measurement and Modelling of Turbulent Dispersed Multiphase Flows using Computer Vision and Machine Learning

About the Project

Supervisory Team: Dr. John Lawson

This PhD tackles the challenge of understanding turbulent emulsions, which is crucial for developing cleaner, more efficient industrial processes. Using cutting-edge experiments, computer vision and machine learning, you’ll generate unique data to build predictive models of droplet dynamics, advancing both fundamental fluid mechanics and sustainable process engineering.

Predicting the behaviour of turbulent dispersed multiphase flows, where droplets or bubbles are suspended in another fluid, is crucial for processes such as rain formation, wastewater treatment, oil recovery and biofuel synthesis. A major challenge is modelling how droplets break up and coalesce, since this depends on local turbulence and is difficult to measure experimentally.

This PhD will address this challenge by developing high-fidelity experimental datasets to inform and validate models of droplet population dynamics. The project will involve designing and operating a Taylor–Couette flow rig using Refractive Index Matching (RIM) to generate optically accessible turbulent emulsions. Advanced Particle Tracking Velocimetry (PTV) and Laser-Induced Fluorescence (LIF) measurements will be combined with modern Computer Vision (CV) and Machine Learning (ML) methods to quantify droplet size distributions and local turbulence characteristics.

You will join a thriving research community of over 50 PhDs, postdoctoral researchers and academics within the Aerodynamics and Flight Mechanics Group, gaining access to a world-class experimental fluid mechanics laboratory equipped with state-of-the-art flow diagnostics. The project offers specialist training in optical measurement techniques, scientific computing, and data-driven modelling - preparing you for careers in academia or industry.

You will also have opportunities to publish in leading journals, present at international conferences, and collaborate with global research partners.

Entry requirements

Applicants should have a strong background in fluid mechanics and scientific computing. A demonstrable aptitude for practical laboratory work is essential.

Applications are invited from candidates who possess (or expect to gain) a first-class honours MEng, MSc or higher degree equivalent in Engineering, Physics or allied disciplines.

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.

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

• programme type: research
• academic year: 2026/27
• if you will be full time or part time
• faculty: Engineering and Physical Sciences
• search for programme PhD Engineering & the Environment (7175)
• please add the name of the supervisor in section 2 of the application.

Applications should include:

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

Contact us

Faculty of Engineering and Physical Sciences

If you have a general question, email: feps-pgr-apply@soton.ac.uk

Project leader

For an initial conversation, email Dr John Lawson - j.m.lawson@soton.ac.uk