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

Minwoo Kim, G. Shin, J. Lee, W. H. Ko, H. Han, S. H. Hahn, S. K. Kim, S. M. Yang, R. Shousha, H. S. Kim, J. W. Juhn, G. Y. Park, E. Kolemen

Research output: Contribution to journalArticlepeer-review

Abstract

The integrated Resonant Magnetic Perturbation (RMP)-based Edge-Localized Mode (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 ( β N ) is introduced as a metric for plasma performance. The integrated process incorporates the latest achievements in the RMP technique to enhance β N 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 β N by over 10% compared to the conventional onset. During the RMP phase, the adaptive feedback RMP ELM controller, demonstrating its performance in previous experiments, plays a crucial role in maximizing β N during the suppression phase and sustaining the β N -enhanced suppression state by optimizing the RMP strength. The integrated process achieves β N 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 ( β N = 2.8), and maintains the suppression phase above the lower limit of target β N (= 2.4) for ∼4 s (∼60 τ E ). In addition to β N enhancement, the integrated process demonstrates quicker restoration of the suppression phase and recovery of β N 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 β N and the electron temperature.

Original languageEnglish (US)
Article number086032
JournalNuclear Fusion
Volume63
Issue number8
DOIs
StatePublished - Aug 2023

All Science Journal Classification (ASJC) codes

  • Nuclear and High Energy Physics
  • Condensed Matter Physics

Keywords

  • ELMs
  • KSTAR
  • RMP
  • normalized beta
  • real-time ELM control

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