Prior to starting my PHD, I completed an integrated master’s in Physics (MPhys) at the University of Manchester. During this time, I discovered my passion for computational and applied physics. Personally, some of the stand out areas were fluid mechanics, statistical physics and dynamical systems. My master’s research project involved the application of techniques from machine learning and neural networks to the analysis of medical images taken by computed tomography.
My PhD will involve the full PDE modelling of the flow physics relevant to the circulating lithium-lead eutectic present in tritium breeder blankets. In these systems, the flow is strongly influenced by radiative heating, magnetohydrodynamic effects, buoyancy and turbulence.
During the first year of my research, I will be directly solving the Navier-Stokes and magnetohydrodynamic equations using a high-order finite difference scheme. As direct numerical simulation (DNS) is computationally expensive, a supercomputer (Archer2) will be used for this. The results of the DNS will be used to train a physics informed neural network – this will be the focus of the second year of my PhD. After this, the direction of the project will depend upon the results of years 1 and 2. A possibility could be exploring the use of surrogate modelling (LES, RANS) to aid in the design of future fusion plants.
This project will allow me to apply and further develop my current knowledge of physics and machine learning. What is most exciting for me, is the opportunity to learn about a wide range of new areas including computational fluid dynamics, high performance computing, plasma physics and nuclear materials. This is why I think the CDT is such a great opportunity!