High temporal resolution of pedestal dynamics via machine learning on density diagnostics
Artikel i vetenskaplig tidskrift, 2024

At the Joint European Torus, the reference diagnostic to measure electron density is Thomson scattering. However, this diagnostic has a low sampling rate, which makes it impractical to study the temporal dynamics of fast processes, such as edge localized modes. In this work, we use machine learning to predict the density profile based on data from another diagnostic, namely reflectometry. By learning to transform reflectometry data into Thomson scattering profiles, the model is able to generate the density profile at a much higher sampling rate than Thomson scattering, and more accurately than reflectometry alone. This enables the study of pedestal dynamics, by analyzing the time evolution of the pedestal height, width, position and gradient. We also discuss the accuracy of the model when applied on experimental campaigns that are different from the one it was trained on.

machine learning

reflectometry

plasma diagnostics

thomson scattering

Författare

Diogo R. Ferreira

Universidade de Lisboa

Andreas Gillgren

Chalmers, Rymd-, geo- och miljövetenskap, Astronomi och plasmafysik

Andrei Osipov

Chalmers, Rymd-, geo- och miljövetenskap, Astronomi och plasmafysik

Pär Strand

Chalmers, Rymd-, geo- och miljövetenskap, Astronomi och plasmafysik

JET contributors

Plasma Physics and Controlled Fusion

0741-3335 (ISSN) 1361-6587 (eISSN)

Vol. 66 2 025001

Implementation of activities described in the Roadmap to Fusion during Horizon Europe through a joint programme of the members of the EUROfusion consortium

Europeiska kommissionen (EU) (101052200), 2021-01-01 -- 2025-12-31.

Infrastructure for research and devlopment of fusion reactors: ITER and DEMO

Vetenskapsrådet (VR) (2021-00182), 2022-01-01 -- 2026-12-31.

Ämneskategorier

Fusion, plasma och rymdfysik

DOI

10.1088/1361-6587/ad15ef

Mer information

Senast uppdaterat

2024-01-09