The EUROfusion JET-ILW pedestal database is described, with emphasis on three main issues. First, the technical aspects are introduced, including a description of the data selection, the datasets, the diagnostics used, the experimental and theoretical methods implemented and the main definitions. Second, the JET-ILW pedestal structure and stability are described. In particular, the work describes the links between the engineering parameters (power, gas and divertor configuration) and the disagreement with the peeling-ballooning (PB) model implemented with ideal MHD equations. Specifically, the work clarifies why the JET-ILW pedestal tends to be far from the PB boundary at high gas and high power, showing that a universal threshold in power and gas cannot be found but that the relative shift (the distance between the position of the pedestal density and of the pedestal temperature) plays a key role. These links are then used to achieve an empirical explanation of the behavior of the JET-ILW pedestal pressure with gas, power and divertor configuration. Third, the pedestal database is used to revise the scaling law of the pedestal stored energy. The work shows a reasonable agreement with the earlier Cordey scaling in terms of plasma current and triangularity dependence, but highlights some differences in terms of power and isotope mass dependence.

We present an ultrafast neural network (NN) model, QLKNN, which predicts core tokamak transport heat and particle fluxes. QLKNN is a surrogate model based on a database of 300 million flux calculations of the quasilinear gyrokinetic transport model QuaLiKiz. The database covers a wide range of realistic tokamak core parameters. Physical features such as the existence of a critical gradient for the onset of turbulent transport were integrated into the neural network training methodology. We have coupled QLKNN to the tokamak modelling framework JINTRAC and rapid control-oriented tokamak transport solver RAPTOR. The coupled frameworks are demonstrated and validated through application to three JET shots covering a representative spread of H-mode operating space, predicting turbulent transport of energy and particles in the plasma core. JINTRAC-QLKNN and RAPTOR-QLKNN are able to accurately reproduce JINTRAC-QuaLiKiz T i,e and n e profiles, but 3 to 5 orders of magnitude faster. Simulations which take hours are reduced down to only a few tens of seconds. The discrepancy in the final source-driven predicted profiles between QLKNN and QuaLiKiz is on the order 1%-15%. Also the dynamic behaviour was well captured by QLKNN, with differences of only 4%-10% compared to JINTRAC-QuaLiKiz observed at mid-radius, for a study of density buildup following the L-H transition. Deployment of neural network surrogate models in multi-physics integrated tokamak modelling is a promising route towards enabling accurate and fast tokamak scenario optimization, Uncertainty Quantification, and control applications.

Studies that validate statistical methods for functional magnetic resonance imaging (fMRI) data often use simulated data to ensure that the ground truth is known. However, simulated fMRI data are almost always generated using in-house procedures because a well-accepted simulation method is lacking. In this article we describe the R package neuRosim, which is a collection of data generation functions for neuroimaging data. We will demonstrate the possibilities to generate data from simple time series to complete 4D images and the possibilities for the user to create her own data generation method.

A power-balance model, with radiation losses from impurities and neutrals, gives a unified description of the density limit (DL) of the stellarator, the L-mode tokamak, and the reversed field pinch (RFP). The model predicts a Sudo-like scaling for the stellarator, a Greenwald-like scaling, , for the RFP and the ohmic tokamak, a mixed scaling, , for the additionally heated L-mode tokamak. In a previous paper (Zanca et al 2017 Nucl. Fusion 57 056010) the model was compared with ohmic tokamak, RFP and stellarator experiments. Here, we address the issue of the DL dependence on heating power in the L-mode tokamak. Experimental data from high-density disrupted L-mode discharges performed at JET, as well as in other machines, are taken as a term of comparison. The model fits the observed maximum densities better than the pure Greenwald limit.