Recent progress towards a quantitative description of filamentary SOL transport

Abstract

A summary of recent results on filamentary transport, mostly obtained in the ASDEX-Upgrade tokamak (AUG), is presented and discussed in an attempt to produce a coherent picture of SOL filamentary transport: A clear correlation is found between L-mode density shoulder formation in the outer midplane and a transition between the sheath limited and the inertial filamentary regimes. Divertor collisionality is found to be the parameter triggering the transition. A clear reduction of the ion temperature takes place in the far SOL after the transition, both for the background and the filaments. This coincides with a strong variation of the ion temperature distribution, which deviates from Gaussianity and becomes dominated by a strong peak below $5$ eV. The filament transition mechanism triggered by a critical value of collisionality seems to be generally applicable to inter-ELM H-mode plasmas, although a secondary threshold related to deuterium fueling is observed. EMC3-EIRENE simulations of neutral dynamics show that an ionization front near the main chamber wall is formed after the shoulder formation. Finally, a clear increase of SOL opacity to neutrals is observed associated to the shoulder formation. A common SOL transport framework is proposed account for all these results, and their potential implications for future generation devices are discussed.

Published in Nuclear Fusion

References in corpus (2)

• An experimental investigation on the high density transition of the Scrape-off Layer transport in ASDEX Upgrade
• ExB analyzer for fast ion temperature measurements in scrape-off layer of the ASDEX Upgrade and COMPASS tokamaks

Cited by in corpus (4)

• On the role of filaments in perpendicular heat transport at the Scrape-off Layer
• Theory-based scaling laws of near and far scrape-off layer widths in single-null L-mode discharges
• Kinetic modeling of neutral transport for a continuum gyrokinetic code
• Synthesizing impurity clustering in the edge plasma of tokamaks using neural networks