Vertical instability forecasting and controllability assessment of multi-device tokamak plasmas in DECAF with data-driven optimization
M. Tobin, S.A. Sabbagh, V. Zamkovska, Juan Riquezes, Jalal Butt, G. Cunningham, L. A. Kogan, J. Measures, Stephen Blackmore, Christopher Ham, J. Harrison, J.W. Berkery, S. P. Gerhardt, J.G. Bak, Jeongwon Lee, S. W. Yoon, the MAST Upgrade Team
Abstract
Abstract Reliable vertical position control will be an essential element of any future tokamak-based fusion power plant in order to reduce disruptions and maximize performance. We investigate methods to improve vertical controllability boundary determination in plasma operational space and demonstrate a data-driven approach based on direct pseudoinversion of operational space data that is rigorously quantitative, applicable in real-time plasma control systems, and physically intuitive to interpret. Applied to historical shot data from entire run campaigns on the MAST-U, KSTAR, and NSTX tokamaks, this approach, implemented in DECAF, improves vertical displacement event identification accuracy to 98.9%–100%. Further, we explore the application of a physics-based vertical stability metric as an early warning forecaster for vertical displacement events. The development of a linear surrogate model for the plasma current density profile, with a coefficient of determination of 0.992 on the training dataset, enables potential employment of this forecaster in real-time. The application of this approach on historical data from the MAST-U MU02 campaign yields a forecaster with 62.6% accuracy, indicating promise for this method when further refined and potentially coupled with other stability metrics.