The scope of this paper is to present and benchmark the first version of a quasilinear calculation, QuaLiKiz, based on a fast linear gyrokinetic code, Kinezero [C. Bourdelle, X. Garbet, G. T. Hoang, J. Ongena, and R. V. Budny, Nucl. Fusion 42, 892 (2002)] accounting for all unstable modes and summing over a wave-number spectrum. The fluctuating electrostatic potential frequency and wave-number spectra are chosen based on turbulence measurements and nonlinear simulations results. A peculiar focus on particle transport is developed. The directions of compressibility and thermodiffusion convections of ions and electrons are analytically derived for passing and trapped particles in both ion and electron turbulence. Also, the charge and mass dependence of trace heavy impurity convection is analytically estimated. These results are compared with quasilinear simulations done by QuaLiKiz. Finally, the impact of accounting for all unstable modes and of summing over the wave-number spectrum is shown to reverse in some cases the direction of particle fluxes.

CRONOS is a suite of numerical codes for the predictive/interpretative simulation of a full tokamak discharge. It integrates, in a modular structure, a 1D transport solver with general 2D magnetic equilibria, several heat, particle and impurities transport models, as well as heat, particle and momentum sources. This paper gives a first comprehensive description of the CRONOS suite: overall structure of the code, main available models, details on the simulation workflow and numerical implementation. Some examples of applications to the analysis of experimental discharges and the predictions of ITER scenarios are also given.

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.