Real-time classification of L-H transition and ELM in KSTAR

Shin, Giwook; Juhn, June-Woo; Kwon, Giil; Hahn, S.H.

doi:10.1016/j.fusengdes.2020.111634

Cited by 10 publications

(12 citation statements)

References 13 publications

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“…However, for an efficient and stable β N -enhanced suppression phase, it is necessary to introduce the latest achievements related to the RMP technique. The pre-emptive RMP onset based on the real-time Machine Learning (ML) classifier [36], which automatically triggers RMP before the first ELM after the L-H transition, can obtain a higher ion temperature at the plasma core region compared to the conventional pre-set RMP onset [37]. The interactive I RMP control by the adaptive feedback RMP ELM controller balances β N enhancement and ELM crash suppression by optimizing I RMP [15,38,39], in contrast to the conventional pre-set I RMP control.…”

Section: Introductionmentioning

confidence: 99%

Integrated RMP-based ELM-crash-control process for plasma performance enhancement during ELM crash suppression in KSTAR

et al. 2023

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The integrated resonant magnetic perturbation (RMP)-based ELM-crash-control process aims to enhance the plasma performance during the RMP-driven ELM crash suppression, where the RMP induces an unwanted confinement degradation. In this study, the normalized beta (βN) is introduced as a metric for plasma performance. The integrated process incorporates the latest achievements in the RMP technique to enhance βN efficiently. The integrated process triggers the n = 1 edge-localized RMP (ERMP) at the L-H transition timing using the real-time machine learning (ML) classifier. The pre-emptive RMP onset can reduce the required external heating power for achieving the same βN by over 10 % compared to the conventional RMP onset. During the RMP phase, the adaptive feedback RMP ELM controller, demonstrating its performance in previous experiments, plays a crucial role in maximizing βN during the suppression phase and sustaining the βN-enhanced suppression state by optimizing the RMP strength. The integrated process achieves βN up to ~2.65 during the suppression phase, which is ~10 % higher than the previous KSTAR record but ~6 % lower than the target of the K-DEMO first phase (βN = 2.8), and maintains the suppression phase above the lower limit of target βN (= 2.4) for ~4 s (~60 τE). In addition to βN enhancement, the integrated process demonstrates quicker restoration of the suppression phase and recovery of βN compared to the adaptive control with the n = 1 conventional RMP (CRMP). The post-analysis of the experiment shows the localized effect of the ERMP spectrum in radial and the close relationship between the evolution of βN and the electron temperature.

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Section: Introductionmentioning

confidence: 99%

Integrated RMP-based ELM-crash-control process for plasma performance enhancement during ELM crash suppression in KSTAR

et al. 2023

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“…This section explains the proposed algorithm for real-time MLbased control of the ELM. As shown in figure 1, the algorithm uses two real-time diagnostic signals consisting of the poloidal D α emission [22] and the line-averaged electron density from the KSTAR two-color interferometer [23,24] for classifying plasma states as low-confinement mode (L), intermediate state (I), H-mode (H), or ELM (E) following the definition of the plasma state in previous work [19]. The D α signal detects from photomultiplier tubes the neutralizing emission amplitude of deuterium, measuring the characteristic drop of the edge turbulence level during the L-H transition and the ELM burst amplitude, which needs to be suppressed.…”

Section: Overviewmentioning

confidence: 99%

“…The patterns of the first L-H transition and ELMs in KSTAR fusion plasmas can be classified by the diagnostic features of D α emission and line-averaged electron density [18,19] because the time-series diagnostic signals have characteristic patterns that allow the classification of the H-mode transition and the ELMs. As previously reported in reference [19], the LSTM, which is a supervised machine learning method, performs well for memorizing patterns from long sequential data with a relatively short inference time and high classification accuracy. In the KSTAR PCS, the LSTM classifier is the core part of the real-time ELM control algorithm.…”

Section: Description Of Lstm Classifier In Kstar Pcsmentioning

confidence: 99%

“…According to the reference [19], the inference time of the LSTM classifier for one time point of classification is about 250 μs. The classifier implemented in the KSTAR PCS has a slow inference time of about 800 μs.…”

Section: Description Of Lstm Classifier In Kstar Pcsmentioning

confidence: 99%

“…The onset of the RMP-driven ELM control can be automated if we can determine in real-time when the first L-H transition occurs. A machine-learning (ML) approach has advantages for such automation of preemptive control [18,19]. Based on the output of an ML classifier that classifies the plasma states as L-mode, H-mode, and ELMy, a control algorithm has been implemented in the KSTAR plasma control system (PCS) [20] that triggers the use of RMP based on the real-time change in the ML classifier output, which indicates the first L-H transition and the first ELM burst.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Preemptive RMP-driven ELM crash suppression automated by a real-time machine-learning classifier in KSTAR

Shin¹,

Hahn²,

Kim³

et al. 2022

Nucl. Fusion

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Suppression or mitigation of edge-localized mode (ELM) crashes is necessary for ITER. The strategy to suppress all the ELM crashes by the resonant magnetic perturbation (RMP) should be applied as soon as the first low-to-high confinement (L-H) transition occurs. A control algorithm based on real-time machine learning (ML) enables such an approach: it classifies the H-mode transition and the ELMy phase in real-time and automatically applies the preemptive RMP. This paper reports the algorithm design, which is now implemented in the KSTAR plasma-control system, and the corresponding experimental demonstration of typical high-δ KSTAR H-mode plasmas. As a result, all initial ELM crashes are suppressed with an acceptable safety factor at the edge (q95) and with RMP field adjustment. Moreover, the ML-driven ELM-crash-suppression discharges remain stable without further degradation due to the regularization of the plasma pedestal.

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Overview of recent progress in 3D field physics in KSTAR

Park

et al. 2022

J. Korean Phys. Soc.

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Real-time classification of L-H transition and ELM in KSTAR

Cited by 10 publications

References 13 publications

Integrated RMP-based ELM-crash-control process for plasma performance enhancement during ELM crash suppression in KSTAR

Integrated RMP-based ELM-crash-control process for plasma performance enhancement during ELM crash suppression in KSTAR

Preemptive RMP-driven ELM crash suppression automated by a real-time machine-learning classifier in KSTAR

Overview of recent progress in 3D field physics in KSTAR

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