Quasicontinuous Exhaust Scenario for a Fusion Reactor: The Renaissance of Small Edge Localized Modes

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.

Mutual interactions between plasma filaments in a tokamak evidenced by fast imaging and machine learning

Magnetically confined fusion plasmas are subject to various instabilities that cause turbulent transport of particles and heat across the magnetic field. In the edge plasma region, this transport takes the form of long filaments stretched along the magnetic field lines. Understanding the dynamics of these filaments, referred to as blobs, is crucial for predicting and controlling their impact on reactor performance. To achieve this, highly resolved passive fast camera measurements have been conducted on the COMPASS tokamak. These measurements are analyzed using both conventional tracking methods and a custom-developed machine-learning approach designed to characterize more particularly the mutual interactions between filaments. Our findings demonstrate that up to 18% of blobs exhibit mutual interactions in the investigated area close to the separatrix, at the border between confined and nonconfined plasma. Notably, we present direct observations and radial dependence of blob coalescence and splitting, rapid reversals in the propagation direction of the blob, as well as their dependence on the radial position. The comparison between observations realized with passive and gas puff imaging does not evidence any significant bias due to the use of the latter technique.