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Highest fusion performance without harmful edge energy bursts in tokamak

Author

Listed:
  • S. K. Kim

    (Princeton Plasma Physics Laboratory)

  • R. Shousha

    (Princeton Plasma Physics Laboratory)

  • S. M. Yang

    (Princeton Plasma Physics Laboratory)

  • Q. Hu

    (Princeton Plasma Physics Laboratory)

  • S. H. Hahn

    (Korea Institute of Fusion Energy)

  • A. Jalalvand

    (Princeton University)

  • J.-K. Park

    (Seoul National University)

  • N. C. Logan

    (Columbia University)

  • A. O. Nelson

    (Columbia University)

  • Y.-S. Na

    (Seoul National University)

  • R. Nazikian

    (General Atomics)

  • R. Wilcox

    (Oak Ridge National Laboratory)

  • R. Hong

    (University of California Los Angeles)

  • T. Rhodes

    (University of California Los Angeles)

  • C. Paz-Soldan

    (Columbia University)

  • Y. M. Jeon

    (Korea Institute of Fusion Energy)

  • M. W. Kim

    (Korea Institute of Fusion Energy)

  • W. H. Ko

    (Korea Institute of Fusion Energy)

  • J. H. Lee

    (Korea Institute of Fusion Energy)

  • A. Battey

    (Columbia University)

  • G. Yu

    (University of California Davis)

  • A. Bortolon

    (Princeton Plasma Physics Laboratory)

  • J. Snipes

    (Princeton Plasma Physics Laboratory)

  • E. Kolemen

    (Princeton Plasma Physics Laboratory
    Princeton University)

Abstract

The path of tokamak fusion and International thermonuclear experimental reactor (ITER) is maintaining high-performance plasma to produce sufficient fusion power. This effort is hindered by the transient energy burst arising from the instabilities at the boundary of plasmas. Conventional 3D magnetic perturbations used to suppress these instabilities often degrade fusion performance and increase the risk of other instabilities. This study presents an innovative 3D field optimization approach that leverages machine learning and real-time adaptability to overcome these challenges. Implemented in the DIII-D and KSTAR tokamaks, this method has consistently achieved reactor-relevant core confinement and the highest fusion performance without triggering damaging bursts. This is enabled by advances in the physics understanding of self-organized transport in the plasma edge and machine learning techniques to optimize the 3D field spectrum. The success of automated, real-time adaptive control of such complex systems paves the way for maximizing fusion efficiency in ITER and beyond while minimizing damage to device components.

Suggested Citation

  • S. K. Kim & R. Shousha & S. M. Yang & Q. Hu & S. H. Hahn & A. Jalalvand & J.-K. Park & N. C. Logan & A. O. Nelson & Y.-S. Na & R. Nazikian & R. Wilcox & R. Hong & T. Rhodes & C. Paz-Soldan & Y. M. Jeo, 2024. "Highest fusion performance without harmful edge energy bursts in tokamak," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
    • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-48415-w
    DOI: 10.1038/s41467-024-48415-w
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    References listed on IDEAS

    as
    1. Jaemin Seo & SangKyeun Kim & Azarakhsh Jalalvand & Rory Conlin & Andrew Rothstein & Joseph Abbate & Keith Erickson & Josiah Wai & Ricardo Shousha & Egemen Kolemen, 2024. "Avoiding fusion plasma tearing instability with deep reinforcement learning," Nature, Nature, vol. 626(8000), pages 746-751, February.
    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.
    4. H. Han & S. J. Park & C. Sung & J. Kang & Y. H. Lee & J. Chung & T. S. Hahm & B. Kim & J.-K. Park & J. G. Bak & M. S. Cha & G. J. Choi & M. J. Choi & J. Gwak & S. H. Hahn & J. Jang & K. C. Lee & J. H., 2022. "A sustained high-temperature fusion plasma regime facilitated by fast ions," Nature, Nature, vol. 609(7926), pages 269-275, September.
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