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Disruption prediction with artificial intelligence techniques in tokamak plasmas

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dc.contributor.authorVega, J.
dc.contributor.authorMurari, A.
dc.contributor.authorRattá, Giuseppe A.
dc.contributor.authorGelfusa, Michela
dc.contributor.authorContributors, JET.
dc.contributor.authorDormido Canto, Sebastián
dc.date.accessioned2024-05-20T11:37:26Z
dc.date.available2024-05-20T11:37:26Z
dc.date.issued2022-06-06
dc.description.abstractIn 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.en
dc.description.versionversión final
dc.identifier.doihttps://doi.org/10.1038/s41567-022-01602-2
dc.identifier.issn1745-2481
dc.identifier.urihttps://hdl.handle.net/20.500.14468/12324
dc.journal.titleNature Physics
dc.journal.volume18
dc.language.isoen
dc.publisherSpringer Nature
dc.relation.centerE.T.S. de Ingeniería Informática
dc.relation.departmentInformática y Automática
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0
dc.titleDisruption prediction with artificial intelligence techniques in tokamak plasmases
dc.typejournal articleen
dc.typeartículoes
dspace.entity.typePublication
relation.isAuthorOfPublicationf5f57d8a-f3c0-40a1-a93c-80d6237a2bcb
relation.isAuthorOfPublication.latestForDiscoveryf5f57d8a-f3c0-40a1-a93c-80d6237a2bcb
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