An overview of the present status of research toward the final design of the ITER disruption mitigation system (DMS) is given. The ITER DMS is based on massive injection of impurities, in order to radiate the plasma stored energy and mitigate the potentially damaging effects of disruptions. The design of this system will be extremely challenging due to many physics and engineering constraints such as limitations on port access and the amount and species of injected impurities. Additionally, many physics questions relevant to the design of the ITER disruption mitigation system remain unsolved such as the mechanisms for mixing and assimilation of injected impurities during the rapid shutdown and the mechanisms for the subsequent formation and dissipation of runaway electron current.

We analyze the dynamics of fast electrons in plasmas containing partially ionized impurity atoms, where the screening effect of bound electrons must be included. We derive analytical expressions for the deflection and slowing-down frequencies, and show that they are increased significantly compared to the results obtained with complete screening, already at sub-relativistic electron energies. Furthermore, we show that the modifications to the deflection and slowing down frequencies are of equal importance in describing the runaway current evolution. Our results greatly affect fastelectron dynamics and have important implications, e.g. for the efficacy of mitigation strategies for runaway electrons in tokamak devices, and energy loss during relativistic breakdown in atmospheric discharges.Introduction.-Fast electrons, having speeds well above the thermal speed of the bulk plasma population, are ubiquitous in space and laboratory plasmas. An important process leading to such high-energy electrons is the runaway mechanism. Runaway electrons can be produced in the presence of an accelerating electric field if it exceeds the critical value E c = n e e 3 ln Λ 0 /4πǫ

We derive a formula for the effective critical electric field for runaway generation and decay that accounts for the presence of partially ionized impurities in combination with synchrotron and bremsstrahlung radiation losses. We show that the effective critical field is drastically larger than the classical Connor-Hastie field, and even exceeds the value obtained by replacing the free electron density by the total electron density (including both free and bound electrons). Using a kinetic equation solver with an inductive electric field, we show that the runaway current decay after an impurity injection is expected to be linear in time and proportional to the effective critical electric field in highly inductive tokamak devices. This is relevant for the efficacy of mitigation strategies for runaway electrons since it reduces the required amount of injected impurities to achieve a certain current decay rate.

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.

Disruptions in large tokamaks can lead to the generation of a relativistic runaway electron beam that may cause serious damage to the first wall. To suppress the runaway beam the application of resonant magnetic perturbations (RMP) has been suggested. In this work we investigate the effect of resonant magnetic perturbations on the confinement of runaway electrons by simulating their drift orbits in magnetostatic perturbed fields and calculating the transport and orbit losses for various initial energies and different magnetic perturbation levels. In the simulations we model the ITER RMP configuration and solve the relativistic, gyro-averaged drift equations for the runaway electrons including a time-dependent electric field, radiation losses and collisions. The results indicate that runaway electrons are rapidly lost from regions where the normalised perturbation amplitude δB/B is larger than 0.1% in a properly chosen perturbation geometry. This applies to the region outside the radius corresponding to the normalised toroidal flux ψ = 0.5.