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PhD studentship: Intelligent Systems for the diagnostics of MHD wave dynamics in Magnetically Confined Fusion

An interdisciplinary project between Physics and Engineering

Start Date: October 2013

Supervisor: Dr Erwin Verwichte (Physics)
Co-supervisor: Prof Evor Hines (School of Engineering)
Enquiries: Erwin.Verwichte (at)

A candidate is sought for an interdisciplinary PhD project between the University’s Department of Physics and School of Engineering that aims to develop advanced data-analysis tools for the analysis and interpretation of magnetohydrodynamic (MHD) waves signatures in magnetically confined fusion plasmas. The tools will be applied to MHD wave data from the Mega-Amp Spherical Tokamak (MAST).

The understanding of the fundamental physical processes acting in magnetised plasmas responsible for stability and transport of particles, momentum and energy is essential for the successful development of viable fusion energy via the route of magnetically confined fusion envisaged in the international ITER and DEMO tokamak programs.

‘Intelligent Systems’ are a class of problem-solving techniques that includes evolutionary strategies such as genetic algorithms, which takes inspiration from nature, as well as neural nets, fuzzy logic and self-organising maps. The basic evolutionary strategy uses Darwinian evolution to solve optimisation problems. Such techniques have found application in fields as far and wide as social sciences, robotics and astrophysics. For instance, neural nets had been developed in the past for real-time control of tokamak plasma in order to improve energy confinement.

We shall apply Intelligent Systems techniques to data of MHD wave activity seen in tokamaks. MHD waves play an important role in the redistribution of fast fusion-born ions (α-particles) whose energy is required to heat and sustain fusion. They are driven unstable by fast ions leading to anomalously high diffusion away from the plasma core and could lead to losses that damage the tokamak walls.

MAST at the Culham Centre for Fusion Energy (CCFE) is ideally suited for studying MHD waves because of the lower magnetic field employed in spherical tokamaks compared with conventional tokamaks. This makes it easier to produce super-Alfvénic fast ions by neutral beam injection. Also, MAST has a range of superior diagnostic capabilities, which together with the increased power in neutral beam heating, makes possible comprehensive experimental studies of MHD wave phenomena. The diversity and complexity of experimental wave data would greatly benefit from the development of ‘smart’ data-analysis tools that can identify and categorize the various wave signatures.

The project provides the student with the opportunity to gain valuable experience in multiple disciplines and develop a rich skill-set that includes data-analysis techniques and plasma physics.

For information on how to apply please visit Physics Postgraduate Admissions.