PhD in Mechanical And Aerospace Engineering

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./nMany 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./nThis 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.