Disruption prediction with artificial intelligence techniques in tokamak plasmas

• Published in: Nature physics Vol. 18; no. 7; pp. 741 - 750
• Main Authors: Vega, J., Murari, A., Dormido-Canto, S., Rattá, G. A., Gelfusa, M.
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 01.07.2022; Nature Publishing Group; Nature Publishing Group (NPG)
• Subjects: 639/4077/4091/4093; 639/766/1960/1136; 70 PLASMA PHYSICS AND FUSION TECHNOLOGY; Artificial intelligence; Atomic; Classical and Continuum Physics; Clean energy; Complex Systems; Condensed Matter Physics; Deuterium; Electromagnetic forces; Energy sources; Fusion reactors; High temperature; Magnetic fields; Mathematical and Computational Physics; Melting; Molecular; Nuclear fusion; Nuclear fusion reactors; Nuclear reactions; Optical and Plasma Physics; Perspective; Physics; Physics and Astronomy; Plasmas (physics); Sustainable energy; Theoretical; Thermal analysis; Tokamak devices; Toruses; Tritium
• ISSN: 1745-2473; 1745-2481; 1745-2481; 1476-4636
• DOI: 10.1038/s41567-022-01602-2

In nuclear fusion reactors, plasmas are heated to very high temperatures of more than 100 million kelvin and, in so-called tokamaks, they are confined by magnetic fields in the shape of a torus. Light nuclei, such as deuterium and tritium, undergo a fusion reaction that releases energy, making fusion a promising option for a sustainable and clean energy source. Tokamak plasmas, however, are prone to disruptions as a result of a sudden collapse of the system terminating the fusion reactions. As disruptions lead to an abrupt loss of confinement, they can cause irreversible damage to present-day fusion devices and are expected to have a more devastating effect in future devices. Disruptions expected in the next-generation tokamak, ITER, for example, could cause electromagnetic forces larger than the weight of an Airbus A380. Furthermore, the thermal loads in such an event could exceed the melting threshold of the most resistant state-of-the-art materials by more than an order of magnitude. To prevent disruptions or at least mitigate their detrimental effects, empirical models obtained with artificial intelligence methods, of which an overview is given here, are commonly employed to predict their occurrence—and ideally give enough time to introduce counteracting measures. Tokamak plasmas are prone to sudden collapses that terminate the nuclear fusion reactions. This perspective discusses the prediction of these so-called disruptions with artificial intelligence techniques.