Characterization of Transition to Detachment of Magnetic Confinement Plasmas via Data Driven Approach in LHD

Abstract

The transition condition from an attached state to a detached state of magnetic confinement plasmas has been investigated by a data-drive approach in LHD. This transition is defined as a binary classification problem of two states, and Support Vector Machine together with Exhaust Search has been applied. The boundary between detachment and attachment in the physical parameter space has been identified as a decision function comprising radiation and heating power, magnetic field and the resonant magnetic flux. While resonant magnetic perturbation (RMP) secures stable detached plasmas, it has been found that the featured parameter is not externally applied RMP itself but the plasma response to RMP. The present approach gives a robust separation boundary even for the extended operation with radiation enhancement by neon gas puff. Anomaly detection with a singular value decomposition has been also applied to the temporal behavior and identified pre- and post-relationships of each physical parameter in time. Emissions from carbon impurities with low ionization potential start to change prior to the RMP penetration and then the drop of ion saturation current, that is the transition to detachment, happens. These temporal sequences do not necessarily mean causality but are helpful for approach to physical inference.