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

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 $20\,%$ 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 ( R/Lnₑ ) and electron-temperature ( R/LTₑ ) gradients, we find that a range of power-law scalings R/LTₑ=A(R/Lnₑ)^α with α≈ 0.4 correctly capture the behaviour of the electron-temperature in the steep-gradient region. Fitting A and α independently for each pedestal reveals a clear one-to-one correlation, suggesting an underlying constraint in pedestal physics. The measured ηₑ = Lnₑ/LTₑ 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 R/LTₑ and R/Lnₑ , 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.