Collisionality scaling of the tokamak density limit: data-driven analysis, cross-device prediction, and real-time avoidance

We employ machine learning to mine a 4000+ shot multi-device database (Alcator C-Mod, ASDEX-Upgrade, DIII-D, JET, TCV), revealing edge collisionality and normalized pressure β_T as the primary variables governing the density limit (DL). Density is a key lever for fusion reactor power output (P_fus ~ n²), yet high density operation in tokamaks is bounded by the poorly understood “density limit.” In this study, we assemble a large database using the DisruptionPy [1] and DEFUSE [2] frameworks and apply data-driven methods [3] to identify improved scalings for both the L-mode and H-mode DL. These new scalings reduce false-positive alarms by ≥2x when compared with the Greenwald fraction. The L-mode DL scaling in particular resembles a proposed threshold for RBM destabilization. We demonstrate the utility of these scalings by successfully avoiding the DL at DIII-D via real-time feedback control. These results (i) establish collisionality and β_T as the primary organizing parameters of the DL, (ii) demonstrate a control solution for the DL, and (iii) illustrate how machine learning workflows can both identify governing variables and deliver operational solutions.

[1] Trevisan et al. (2025), Zenodo https://doi.org/10.5281/zenodo.13935223

[2] Pau et al. (2023), 29th IAEA FEC

[3] Maris et al. (2024), NF https://doi.org/10.1088/1741-4326/ad90f0