Kim S K, Shousha R, Yang S M, Hu Q, Hahn S H, Jalalvand A, Park J-K, Logan N C, Nelson A O, Na Y-S, Nazikian R, Wilcox R, Hong R, Rhodes T, Paz-Soldan C, Jeon Y M, Kim M W, Ko W H, Lee J H, Battey A, Yu G, Bortolon A, Snipes J, Kolemen E
Princeton Plasma Physics Laboratory, Princeton, NJ, USA.
Korea Institute of Fusion Energy, Daejeon, South Korea.
Nat Commun. 2024 May 11;15(1):3990. doi: 10.1038/s41467-024-48415-w.
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.
托卡马克核聚变及国际热核聚变实验反应堆（ITER）的发展道路在于维持高性能等离子体以产生足够的聚变功率。这一努力受到等离子体边界不稳定性引发的瞬态能量爆发的阻碍。用于抑制这些不稳定性的传统三维磁扰动常常会降低聚变性能，并增加出现其他不稳定性的风险。本研究提出了一种创新的三维场优化方法，该方法利用机器学习和实时适应性来克服这些挑战。在DIII-D和KSTAR托卡马克装置中实施该方法后，始终实现了与反应堆相关的核心约束以及最高的聚变性能，且未引发破坏性爆发。这得益于对等离子体边缘自组织输运的物理理解的进步以及用于优化三维场谱的机器学习技术。这种复杂系统的自动化、实时自适应控制的成功，为在ITER及其他装置中最大化聚变效率、同时最小化对装置部件的损害铺平了道路。
