Integrated RMP-based ELM-crash-control process for plasma performance enhancement during ELM crash suppression in KSTAR (vol 63, 086032, 2023)

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 (beta(N)) is introduced as a metric for plasma performance. The integrated process incorporates the latest achievements in the RMP technique to enhance beta(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 beta(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 beta(N) during the suppression phase and sustaining the beta(N)-enhanced suppression state by optimizing the RMP strength. The integrated process achieves beta(N) up to similar to 2.65 during the suppression phase, which is similar to 10% higher than the previous KSTAR record but similar to 6% lower than the target of the K-DEMO first phase (beta(N) = 2.8), and maintains the suppression phase above the lower limit of target beta(N) (= 2.4) for similar to 4 s (similar to 60 tau(E)). In addition to beta(N) enhancement, the integrated process demonstrates quicker restoration of the suppression phase and recovery of beta(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 beta(N) and the electron temperature.