Highest fusion performance without harmful edge energy bursts in tokamak

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. Jeon, M. W. Kim, W. H. Ko, J. H. Lee, A. Battey, G. Yu, A. Bortolon, J. Snipes and E. Kolemen ()
Additional contact information
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

Nature Communications, 2024, vol. 15, issue 1, 1-11

Abstract: 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.

Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.nature.com/articles/s41467-024-48415-w Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-48415-w

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-024-48415-w

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().