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A control oriented strategy of disruption prediction to avoid the configuration collapse of tokamak reactors

Author

Listed:
  • Andrea Murari

    (Corso Stati Uniti 4
    CNR)

  • Riccardo Rossi

    (University of Rome “Tor Vergata”)

  • Teddy Craciunescu

    (Plasma and Radiation Physics)

  • Jesús Vega

    (Laboratorio Nacional de Fusión)

  • Michela Gelfusa

    (University of Rome “Tor Vergata”)

Abstract

The objective of thermonuclear fusion consists of producing electricity from the coalescence of light nuclei in high temperature plasmas. The most promising route to fusion envisages the confinement of such plasmas with magnetic fields, whose most studied configuration is the tokamak. Disruptions are catastrophic collapses affecting all tokamak devices and one of the main potential showstoppers on the route to a commercial reactor. In this work we report how, deploying innovative analysis methods on thousands of JET experiments covering the isotopic compositions from hydrogen to full tritium and including the major D-T campaign, the nature of the various forms of collapse is investigated in all phases of the discharges. An original approach to proximity detection has been developed, which allows determining both the probability of and the time interval remaining before an incoming disruption, with adaptive, from scratch, real time compatible techniques. The results indicate that physics based prediction and control tools can be developed, to deploy realistic strategies of disruption avoidance and prevention, meeting the requirements of the next generation of devices.

Suggested Citation

  • Andrea Murari & Riccardo Rossi & Teddy Craciunescu & Jesús Vega & Michela Gelfusa, 2024. "A control oriented strategy of disruption prediction to avoid the configuration collapse of tokamak reactors," Nature Communications, Nature, vol. 15(1), pages 1-19, December.
  • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-46242-7
    DOI: 10.1038/s41467-024-46242-7
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    References listed on IDEAS

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    1. Sergio Ciattaglia & Maria Carmen Falvo & Alessandro Lampasi & Matteo Proietti Cosimi, 2020. "Energy Analysis for the Connection of the Nuclear Reactor DEMO to the European Electrical Grid," Energies, MDPI, vol. 13(9), pages 1-19, May.
    2. Jonas Degrave & Federico Felici & Jonas Buchli & Michael Neunert & Brendan Tracey & Francesco Carpanese & Timo Ewalds & Roland Hafner & Abbas Abdolmaleki & Diego de las Casas & Craig Donner & Leslie F, 2022. "Magnetic control of tokamak plasmas through deep reinforcement learning," Nature, Nature, vol. 602(7897), pages 414-419, February.
    3. Julian Kates-Harbeck & Alexey Svyatkovskiy & William Tang, 2019. "Predicting disruptive instabilities in controlled fusion plasmas through deep learning," Nature, Nature, vol. 568(7753), pages 526-531, April.
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