Reconstructions of electron-temperature profiles from EUROfusion pedestal database using turbulence models and machine learning

Journal article

This study makes use of plasma-profile data from the EUROfusion pedestal database (Frassinetti et al. 2020 Nucl. Fusion vol. 61, p. 016001), focusing on the electron-temperature and electron-density profiles in the edge region of H-mode ELMy JET ITER-Like-Wall (ILW) pulses. We make systematic predictions of the electron-temperature pedestal, taking engineering parameters of the plasma pulses and the density profiles as inputs. We first present a machine-learning (ML) algorithm which, given more inputs than theory-based modelling, is able to reconstruct unseen temperature profiles within of the experimental values. We find a hierarchy of the most consequential engineering parameters for such predictions. This result confirms the conceptual possibility of accurate data-driven prediction. Next, taking a simple theoretical approach that assumes a definite local relationship between the electron-density ( ) and electron-temperature ( ) gradients, we find that a range of power-law scalings with correctly capture the behaviour of the electron-temperature in the steep-gradient region. Fitting and independently for each pedestal reveals a clear one-to-one correlation, suggesting an underlying constraint in pedestal physics. The measured values across the pedestal exhibit a wide distribution, significantly exceeding the slab-ETG linear stability threshold, implying either a non-linear threshold shift or a measurably supercritical saturated turbulent state. Finally, we fit parameters for scalings that relate the turbulent heat flux to the gradients and , similarly to models extracted from gyrokinetic simulations. The inclusion of more experimental parameters is necessary for such models to match the accuracy of our ML results.

Authors: Turica, L; Field, A; Schekochihin, A; Frassinetti, L; JET Contributors

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Cambridge University Press
• Journal: Journal of Plasma Physics
• Volume: 91
• Issue: 6
• Article number: E155
• Publication date: 2026-01-30
• Acceptance date: 2025-08-05
• DOI: 10.1017/s0022377825100779
• EISSN: 1469-7807
• ISSN: 0022-3778
• Language: English
• Keywords: fusion plasma; plasma nonlinear phenomena; plasma confinement