Journal Article

FZJ-2022-00867

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Divertor power load predictions based on machine learning

Brenzke, M. (Corresponding author)FZJ* ; Wiesen, S.FZJ* ; Bernert, M. ; Coster, D. ; Jitsev, J.FZJ* ; Liang, Y.FZJ* ; von Toussaint, U. ; ASDEX Upgrade Team ; EUROfusion MST1 Team

2021
IAEA Vienna

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Please use a persistent id in citations: doi:10.1088/1741-4326/abdb94

Abstract: Machine learning based data-driven approaches to thermal load prediction on the divertor targets of ASDEX upgrade (AUG) are presented. After selecting time averaged data from almost six years of operation of AUG and applying basic physics-motivated cuts to the data we find that we are able to train machine learning models to predict a scalar quantifying the steady state thermal loads on the outer divertor target given scalar operational parameters. With both random forest and neural network based models we manage to achieve decent agreement between the model predictions and the observed values from experiments. Furthermore, we investigate the dependencies of the models and observe that the models manage to extract trends expected from previous physics analyses.

Note: kein Zugriff auf Postprint

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Contributing Institute(s):

1. Jülich Supercomputing Center (JSC)
2. Plasmaphysik (IEK-4)

Research Program(s):

1. 5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs) and Research Groups (POF4-511) (POF4-511)
2. 134 - Plasma-Wand-Wechselwirkung (POF4-134) (POF4-134)

Appears in the scientific report 2021

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Database coverage:
Clarivate Analytics Master Journal List ; Current Contents - Physical, Chemical and Earth Sciences ; Ebsco Academic Search ; Essential Science Indicators ; IF < 5 ; JCR ; National-Konsortium ; Nationallizenz ; SCOPUS ; Science Citation Index Expanded ; Web of Science Core Collection

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