My interest in nuclear fusion began when an undergraduate internship in Nigeria directly exposed me to the negative impact of burning fossil fuels for electricity generation. Following this experience, I pursued a Masters degree with the Erasmus Mundus Joint Masters Degree program in nuclear physics, focusing on fusion in my thesis. For my MSc thesis, I studied the feasibility of using machine learning techniques to study charge exchange spectra from the ASDEX Upgrade tokamak in Germany. I am thrilled to be continuing fusion training and research with the Fusion-CDT program.
For my PhD, I will be under the supervision of Roddy Vann and David Dickinson to characterize turbulence at the plasma edge using microwaves. Turbulence is the dominant mechanism for the loss of heat from tokamaks and understanding it is critical to successful operation of a tokamak reactor.
Injected microwaves at sufficiently low frequency bounce off the plasma edge at a density related to the wave frequency. It is proposed that comparing the polarisation of the back-scattered microwaves to the polarisation of the injected beam can be used to determine the extent to which the scattering turbulence is electromagnetic or electrostatic – thereby discriminating between two different models for edge turbulence. My project will use existing high performance computing codes to study the feasibility of this approach; the computational models will then be validated with experiment by using the Synthetic Aperture Microwave Imaging (SAMI) diagnostic at MAST-U.