Adaptive multilevel modelling of phase-change problems solidification and crystalization

Project description

This project would be undertaken in collaboration with the Institute of Materials Research (IMR) at the University of Leeds, following two previous successful collaborative PhD projects. Understanding the behaviour of metals and alloys as they solidify is of great importance in a range of processes, such as casting. The reason for this is that the material properties of the cast depend critically upon the behaviour at the advancing liquid-solid interface at the time of solidification. Perhaps the most important class of computational technique to have been developed for modelling such processes in recent years is called the phase-field method. This method approximates the interface by a smoothly varying function over a finite, but very narrow, region. At Leeds we have recently developed a world-leading capability for solving such phase-field models by combining mesh adaptivity, implicit adaptive time-stepping and nonlinear multigrid iterations. This has allowed us to solve a number of problems in physically realistic parameter regimes for the very first time. However, so far we have only applied these new techniques to a relatively small class of problems based upon binary alloy solidification and to simulate highly directional simulation involving primary branches only. This project would seek to extend our state-of-the-art computational algorithms to incorporate new features such as more complex (and physically realistic) alloys and more general solidification patterns, such as side-branching of dendrites.

Entry requirements

You must have achieved a bachelor degree with a 2:1 (hons), or equivalent, or a good performance in a Masters level course in a relevant subject. We also recognise relevant industrial and academic experience.

How to apply

Formal applications for research degree study should be made online through the university's website.

If you require any further information please contact the Graduate School Office
e:, t: +44 (0)113 343 8000.