PhD Studentship – Data-driven Approaches to Viscoelastic Flow Control Full Time NEW

Application deadline: All year round

Research theme: Applied Mathematics, Mechanical and Aerospace Engineering, Fluid Dynamics

How to apply: uom.link/pgr-apply-2425

How many positions: 1

This 3.5 year project is funded by The Department of Mechanical, Aerospace and Civil Engineering. Home students are eligible to apply. The successful candidate will receive a tax free stipend set at the UKRI rate (£20,780 for 2025/26) and tuition fees will be paid.

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 leveraged 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 (doi.org/10.5518/100/77).

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.

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

To apply, please contact the Main supervisor, Dr Miguel beneitez –miguel.beneitez@manchester.ac.uk. 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.