Highest fusion performance without harmful edge energy bursts in tokamak.

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. Damaging energy bursts in a tokamak are a major obstacle to achieving stable high-fusion performance. Here, the authors demonstrate the use of adaptive and machine-learning control to optimize the 3D magnetic field to prevent edge bursts and maximize fusion performance in two different fusion devices, DIII-D and KSTAR. [ABSTRACT FROM AUTHOR]