Data-driven Approaches to Viscoelastic Flow Control

About the Project

Many liquids in industry and biology are viscoelastic (like paints, blood, saliva, and DNA suspensions among many others), displaying a mixture of both viscous and elastic properties. These fluids are fundamental for a myriad of industrial processes (such as mixing of chemicals or cooling of microprocessors), however they are still not well understood due to the complexity of the mathematical models that describe them. The current consensus is that there are three “types” of viscoelastic chaos: modified Newtonian turbulence, elastic turbulence, and elasto-inertial turbulence. Understanding the origins of and the connections between these chaotic states is a major scientific problem with substantial industrial implications.

This project will apply cutting-edge machine learning (ML) techniques to gain new physical insights into fundamental questions about viscoelastic flows in both canonical configurations and porous media applications. ML techniques will be applied alongside numerical simulations relying on high-performance computing and reduced order modelling. We aim to gain new insights about the physical coherent structures which are most relevant to viscoelastic turbulence, and use this knowledge to identify control strategies through deep reinforcement learning. The methods developed in this project will directly contribute to designing novel porous media that enhance mixing efficiency, a capability with wide-ranging industrial applications.

Project goals:

• Apply explainable deep learning to identify key coherent structures in viscoelastic turbulence and design effective flow control strategies
• Develop ML models to predict complex flows in porous media configurations
• Design optimised porous media geometries for enhanced mixing efficiency.

Training Opportunities

The student will benefit from working alongside a multidisciplinary team of engineers, mathematicians, and physicists at the University of Manchester as well as a wide collaboration network within the UK and overseas. Training can be provided in computational fluid dynamics, machine learning, and nonlinear dynamics. These skills are highly valued across a wide range of industries. Recent data reveals that Fluid Dynamics generates £14 billion worth of output from over 2,200 firms and employs 45,000 people in the UK [1].

This project would suit a student with a strong background in computational science and interest in fluid dynamics. Prior knowledge about viscoelastic flows and/or porous media is beneficial but not required.

Eligibility

Applicants should have, or expect to achieve, at least a 2.1 honours degree or a master’s (or international equivalent) in a relevant science, mathematics or engineering related discipline.

• Excellence in computational science and mathematics
• Programming skills in any language
• Strong written and verbal communication skills

Funding

At Manchester we offer a range of scholarships, studentships and awards at university, faculty and department level, to support both UK and overseas postgraduate researchers applying for competition and self-funded projects.

For more information, visit our funding page or search our funding database for specific scholarships, studentships and awards you may be eligible for.

Before you apply

We strongly recommend that you contact the supervisors for this project before you apply. Please include details of your current level of study, academic background and any relevant experience and include a paragraph about your motivation to study this PhD project.

How to apply

Apply online through our website: https://uom.link/pgr-apply-2425

When applying, you’ll need to specify the full name of this project, the name of your supervisor, if you already having funding or if you wish to be considered for available funding through the university, details of your previous study, and names and contact details of two referees.

Your application will not be processed without all of the required documents submitted at the time of application, and we cannot accept responsibility for late or missed deadlines. Incomplete applications will not be considered.

After you have applied you will be asked to upload the following supporting documents:

• Final Transcript and certificates of all awarded university level qualifications
• Interim Transcript of any university level qualifications in progress
• CV
• Supporting statement: A one or two page statement outlining your motivation to pursue postgraduate research and why you want to undertake postgraduate research at Manchester, any relevant research or work experience, the key findings of your previous research experience, and techniques and skills you’ve developed. (This is mandatory for all applicants and the application will be put on hold without it).
• Contact details for two referees (please make sure that the contact email you provide is an official university/work email address as we may need to verify the reference)
• English Language certificate (if applicable)

If you have any questions about making an application, please contact our admissions team by emailing FSE.doctoralacademy.admissions@manchester.ac.uk.

Funding Notes

This 3.5 year project is funded by The Department of Mechanical, Aerospace and Civil Engineering. Tuition fees will be paid (at home rate) and you will receive a tax free stipend set at the UKRI rate (£20,780 for 2025/26).