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Accès ouvert déclaré 2022 article

Disruption prediction with artificial intelligence techniques in tokamak plasmas

91Citations signalées, ce qui n’est pas une note de qualité
119Institutions déclarées
30Pays d’affiliation déclarés

Rattachement africain : it, gb, es, pt, pl, ru, fi, jp, gr, fr, se, de, us, hu, lv, ch, cz, ua, hr, ro, br, nl, be, si, ie, dk, sk, kr, at, lt. Niveau de preuve : code pays fourni par la source.

Le résumé fourni par la source

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.

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Le contrôle bibliographique ouvert

DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.

Titre Crossref
Disruption prediction with artificial intelligence techniques in tokamak plasmas
Date Crossref
06/06/2022
Éditeur
Springer Science and Business Media LLC
Type
journal-article

Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.

Les institutions déclarées

National Agency for New Technologies, Energy and Sustainable Economic DevelopmentUniversity of PaduaCulham Science CentreUniversidad Nacional de Educación a DistanciaUniversity of Rome Tor VergataUnited Kingdom Atomic Energy AuthorityUniversity of LisbonNational Centre for Nuclear ResearchPhysico-Technical InstituteUniversity of HelsinkiVTT Technical Research Centre of FinlandNational Institutes for Quantum Science and TechnologyConsorzio CREO (Italy)National Centre of Scientific Research "Demokritos"Kurchatov InstituteInstitute for the Science and Technology of PlasmasITERTroitsk Institute for Innovation and Fusion ResearchUppsala UniversityMax Planck Institute for Plasma PhysicsNational Institute for Fusion ScienceFusion (United States)Plasma Technology (United States)Fusion AcademyUniversidad Politécnica de MadridHUN-REN Centre for Energy ResearchUniversity of LatviaUniversity of CagliariNational Technical University of AthensCommissariat à l'Énergie Atomique et aux Énergies AlternativesCEA CadaracheUniversity of CataniaOak Ridge National LaboratoryKarlsruhe Institute of TechnologyGeneral Atomics (United States)University of BaselKTH Royal Institute of TechnologyCentre National de la Recherche ScientifiqueInstitut Jean LamourUniversité de LorraineMax Planck Institute for Plasma Physics - GreifswaldMaritime University of SzczecinInstitute of Nuclear Physics, Polish Academy of SciencesCzech Academy of Sciences, Institute of Plasma PhysicsÉcole Polytechnique Fédérale de LausanneUniversity of Wisconsin–MadisonLviv Polytechnic National UniversityPrinceton Plasma Physics LaboratoryForschungszentrum JülichUniversité Côte d'AzurInstitut de Biologie ValroseRuđer Bošković InstituteNational Institute of Research and Development for OptoelectronicsThe University of Texas at AustinIstituto Nazionale di Fisica Nucleare, Sezione di PadovaUniversità degli Studi della TusciaUniversidade de São PauloUniversidade Cidade de São PauloUniversity of Milano-BicoccaUniversity of WarwickInstitute of Plasma Physics and Laser MicrofusionAalto UniversityDutch Institute for Fundamental Energy ResearchWarsaw University of TechnologyQueen's University BelfastNational Institute for Laser Plasma and Radiation PhysicsGhent UniversityJožef Stefan InstituteJožef Stefan International Postgraduate SchoolNational Institute for Research and Development of Isotopic and Molecular TechnologiesDublin City UniversityUniversity of California San DiegoKharkiv Institute of Physics and TechnologyUniversity of YorkChalmers University of TechnologyEuropean CommissionUniversity of Tennessee at KnoxvilleUniversitat Politècnica de CatalunyaBarcelona Supercomputing CenterUniversidad de SevillaAix-Marseille UniversitéInstitut Universitaire des Systèmes Thermiques IndustrielsSapienza University of RomeInstitute for Nuclear ResearchBelgian Nuclear Research CentreUniversity of ToyamaUniversity of California, IrvineTechnical University of DenmarkComenius University BratislavaUniversity College CorkUniversity of OpoleInstitute of PhysicsDaegu UniversitySeoul National UniversityFusion for EnergyArizona State UniversityPolitecnico di TorinoInstitució Catalana de Recerca i Estudis AvançatsUniversidad Complutense de MadridInstitute for Complex SystemsEindhoven University of TechnologyPurdue University West LafayetteShimane UniversityCzech Technical University in PragueWilliam & MaryWilliams (United States)University of California SystemUniversity of StrathclydeKindai UniversityShizuoka UniversityUniversity of OxfordColumbia UniversityUniversity of IoanninaUniversidade do PortoThe University of TokyoAustrian Academy of SciencesTU WienLithuanian Energy InstituteIbaraki University

Une affiliation ne permet pas de déduire la nationalité d’un auteur.

Les sujets associés

Anomaly Detection Techniques and ApplicationsMagnetic confinement fusion researchNetwork Security and Intrusion Detection

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