Real-time avoidance of the L-mode and H-mode density limit via machine-learned stability metrics

Reliable operation of burning plasma tokamaks will require robust control strategies to avoid macroscopic instability limits such as the L-mode and H-mode density limits (LDL, HDL). In this work, we explore closed-loop avoidance of these phenomena at DIII-D using machine-learned risk metrics: $\textrm{LDL}_{24} = \nu_{*} \beta^{0.4}_{T}$ (Ref. \cite{maris2024correlation}) and $\textrm{HDL}_{25} = \nu_{*} \beta^{0.8}_{T} \rho^{-0.6}_{*} q^{1.0}_{95}$. Feedback control is implemented via the `‘DL Supervisor’' scheme, which regulates the chosen risk metric by reducing the density target or increasing NBI heating in real-time. Using the $\textrm{LDL}_{24}$ risk metric, the LDL is reproducibly suppressed. We also introduce an HDL risk metric in this study, $\textrm{HDL}_{25}$, which reduces the False Positive Rate by 2x compared to the Greenwald fraction. Applying this scaling to a plasma current ramp-down, we successfully avoid an HDL-driven H/L back-transition. These experiments constitute the first demonstration of real-time DL avoidance using machine-learned risk metrics. These instability metrics outline a path to safer high-density operation, more reliable ramp-down scenarios, and improved off-normal control for next-step devices such as ITER and SPARC.