MFMatthew Field

Matthew Field

*This is an algorithmically generated author record based on publicly available information.

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Matthew Field has a total of 12 co-authors. Here are the top co-authors by number of shared publications:


Latest Publications

  • Open Access

    Using deep learning for the detection of UFOs within the JET tokamak

    by Matthew Field, Steve Etches, Daniel Collishaw-Schepman, Jacob Connors, Pedro Carvalho, Scott Silburn, Eurofusion Tokamak Exploitation Team, Jet Contributors

    Components on the inner wall of fusion reactors are exposed to extremely high heat loads, and significant care must be taken to minimize the damage to these components during operation. UFOs, also known as transient impurity events, are small particles of dust within the tokamak vessel that can lead to plasma disruptions. These can cause serious damage to the device and remain a significant challenge for safe operations. This study presents a novel approach to track these UFO events by utilizing visual camera data from the Joint European Torus and a Convolutional Neural Network to identify UFOs within the tokamak. The model attained an accuracy of 95.1%, a precision of 95.4% for UFO detection, a recall of 95.1%, and a receiver operating characteristic curve area of 0.99. These metrics underscore the model's competence in reliably identifying UFOs with minimal false identifications. Consequently, this suggests the model's suitability for integration into UFO detection systems in tokamak environments, improving monitoring and efficiency, and potentially providing an avenue for real-time UFO detection in future research.


  • APECS: An Analytical Equations Toolkit for Magnet Design and Electromagnetic Analysis

    by Alexander Petrov, Nathan Welch, Richard Gowland, Shan Tan-Ya, Kristian Zarebski, Matthew Field

    There is a myriad of variables that must be considered when designing a superconducting magnet. Two of these are: the local magnetic field, dictating the response of the superconductor, and the mutual inductance matrix, dictating the generated voltage. These issues may be resolved with the help of finite element analysis tools, but this is not practical at the very beginning of coil design or when many concepts must be analysed quickly. Then comes the problem of robustly constructing the coil's winding pack such that it will operate as required. Analytical Python Electromagnetic Coil Simulator (APECS) offers one solution to the above. It uses accepted analytical and semi-numerical methods to calculate, at any point in space, the magnetic field generated by circular loop coils, such as a tokamak's poloidal field magnets, as well as straight-edge loop coils, such as a rectangular toroidal field magnet in a spherical tokamak. Combined with knowledge about the performance of the superconducting material and a user-specified cable design, APECS can construct a “minimum viable” winding pack for each poloidal field magnet to aid further analysis and design iteration. Despite that it cannot presently optimize the cable design or the thermo-hydraulics, superconductor/copper content, or the stress and failure, it is an invaluable tool used extensively in the design and analysis of the poloidal field (PF) magnet system on U.K.’s Spherical Tokamak for Energy Production (STEP).