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Detection of changes in the dynamics of thermonuclear plasmas to improve the prediction of disruptions

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Abstract

In particular circumstances, nonlinear systems can collapse suddenly and abruptly. Anomalous detection is therefore an important task. Unfortunately, many phenomena occurring in complex systems out of equilibrium, such as disruptions in tokamak thermonuclear plasmas, cannot be modelled from first principles in real-time compatible form and therefore data-driven, machine learning techniques are often deployed. A typical issue, for training these tools, is the choice of the most adequate examples. Determining the intervals, in which the anomalous behaviours manifest themselves, is consequently a challenging but essential objective. In this paper, a series of methods are deployed to determine when the plasma dynamics of the tokamak configuration varies, indicating the onset of drifts towards a form of collapse called disruption. The techniques rely on changes in various quantities derived from the time series of the main signals: from the embedding dimensions to the properties of recurrence plots and to indicators of transition to chaotic dynamics. The methods, being mathematically completely independent, provide quite robust indications about the intervals, in which the various signals manifest a pre-disruptive behaviour. Consequently, the signal samples, to be used for supervised machine learning predictors, can be defined precisely, on the basis of the plasma dynamics. This information can improve significantly not only the performance of machine learning classifiers but also the physical understanding of the phenomenon.

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Acknowledgements

This work has been carried out within the framework of the EUROfusion Consortium and has received funding from the Euratom Research and Training Programme 2014–2018 and 2019–2020 under grant agreement No 633053. The views and opinions expressed herein do not necessarily reflect those of the European Commission. This work has been carried out within the framework of the EUROfusion Consortium, funded by the European Union via the Euratom Research and Training Programme (Grant Agreement No. 101052200-EUROfusion). Views and opinions expressed are, however, those of the author(s) only and do not necessarily reflect those of the European Union or the European Commission. Neither the European Union nor the European Commission can be held responsible for them.

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Authors and Affiliations

  1. National Institute for Laser, Plasma and Radiation Physics, 077126, Măgurele, Romania

    Teddy Craciunescu

  2. Consorzio RFX (CNR, ENEA, INFN, Università Di Padova, Acciaierie Venete SpA), Corso Stati Uniti 4, 35127, Padua, Italy

    Andrea Murari

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  1. Teddy Craciunescu
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JET Contributors*

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Correspondence to Teddy Craciunescu.

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*See the author list of ‘Overview of JET results for optimising ITER operation’ by J.Mailloux et al. l 2022 Nucl. Fusion 62 042026.

Appendices

Appendix 1: Database

This appendix provides some details (see Table

Table 1 Analysed pulses
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1) about the characteristics of the discharges analysed in the paper. The choice of the individual discharges has been motivated by the intention to cover a wider range of experimental conditions.

Appendix 2: Embedding dimension

See Table

Table 2 Time instances when significant changes of the embedding dimension occur and time intervals until the disruption
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2.

Appendix 3: 0–1 chaos test results

See Tables

Table 3 Results for the 0–1 chaos test when using the ML signal
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3 and

Table 4 Results for the 0–1 chaos test when using the I signal
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4.

Appendix 4: Results for the indicators evaluating the similarity between recurrence plots

See Tables

Table 5 Time instances of dynamical changes evaluated from the average edge overlap \(\omega\) evolution
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5 and

Table 6 Time instances of dynamical changes evaluated from the interlayer mutual information IMI evolution
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6.

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Craciunescu, T., Murari, A. & JET Contributors*. Detection of changes in the dynamics of thermonuclear plasmas to improve the prediction of disruptions. Nonlinear Dyn 111, 3509–3523 (2023). https://doi.org/10.1007/s11071-022-08009-x

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