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Conditional recurrence plots for the investigation of sawteeth pacing with RF modulation

Peluso E.; Murari A.; Craciunescu T.; Lerche E.; Gaudio P.; Gelfusa M.; Gallart D.; Taylor D.

In many areas of research, from neurobiology to nuclear fusion, which investigate complex dynamical systems involving numerous, different and interconnected physical quantities, the application of advanced analysis tools based on Chaos theory and Information theory has provided significant improvements both by supporting theoretical models and by highlighting hidden relationships between quantities characterizing the observed phenomena. The present article is therefore devoted to the analysis of synchronization experiments in magnetically controlled plasmas at JET, involving a ‘target’ quantity (sawteeth) and a ‘driver’ (modulated injected radio frequency) one. The typical approach to such analysis would start from assuming that the coupling between driver and target is free from external influences. However, since sawteeth can occur naturally and constitute a confounding factor in the analysis, by relaxing such an assumption a more realistic description of the system can emerge. The use of the conditional joint recurrence plots, complemented by the conditional transfer entropy, has provided further evidences supporting the effective influence of the pacing on the fast ions populations of both the minority and of the main plasma species in H-mode plasmas. The results highlight specific physical factors affecting the efficiency of the pacing and are in agreement with modelling estimates. The analysis performed then paves the way for future studies on more recent DT pulses performed at JET, and on data from other synchronization experiments.

ID 468762
DOI 10.1088/1361-6587/ac757c
PRODUCT TYPE Journal Article
LAST UPDATE 2022-11-28T22:33:37Z
EU PROJECT EUROfusion
TITLE Implementation of activities described in the Roadmap to Fusion during Horizon 2020 through a Joint programme of the members of the EUROfusion consortium
FOUNDING PROGRAM H2020
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