No abstract

A comparison of the L–H power threshold (Pthr) in JET with all carbon, JET-C, and beryllium/tungsten wall (the ITER-like choice), JET-ILW, has been carried out in experiments with slow input power ramps and matched plasma shapes, divertor configuration and IP/BT pairs. The low density dependence of the L–H power threshold, namely an increase below a minimum density ne,min, which was first observed in JET with the MkII-GB divertor and C wall and subsequently not observed with the current MkII-HD geometry, is observed again with JET-ILW. At plasma densities above ne,min, Pthr is reduced by ∼30%, and by ∼40% when the radiation from the bulk plasma is subtracted (Psep), with JET-ILW compared to JET-C. At the L–H transition the electron temperature at the edge, where the pedestal later develops, is also lower with JET-ILW, for a given edge density. With JET-ILW the minimum density is found to increase roughly linearly with magnetic field, , while the power threshold at the minimum density scales as . The H-mode power threshold in JET-ILW is found to be sensitive both to variations in main plasma shape (Psep decreases with increasing lower triangularity and increases with upper triangularity) and in divertor configuration. When the data are recast in terms of Psep and Zeff or subdivertor neutral pressure a linear correlation is found, pointing to a possible role of Zeff and/or subdivertor neutral pressure in the L–H transition physics. Depending on the chosen divertor configuration, Pthr can be up to a factor of two lower than the ITPA scaling law for densities above ne,min. A shallow edge radial electric field well is observed at the L–H transition. The edge impurity ion poloidal velocity remains low, close to its L-mode values, ⩽5 km s−1 ± 2–3 km s−1, at the L–H transition and throughout the H-mode phase, with no measureable increase within the experimental uncertainties. The edge toroidal rotation profile does not contribute to the depth of the negative Er well and thus may not be correlated with the formation of the edge transport barrier in JET.

Abstract. Disruption mitigation is mandatory for ITER in order to reduce forces, to mitigate heat loads during the thermal quench (TQ) and to avoid runaway electrons. A fast disruption mitigation valve (DMV) has been installed at JET to study mitigation by massive gas injection (MGI). Different gas species and amounts have been investigated with respect to timescales and mitigation efficiency. We discuss the mitigation of halo currents as well as sideways forces during vertical displacement events, the mitigation of heat loads by increased energy dissipation through radiation, the heat loads which could arise by asymmetric radiation and the suppression of runaway electrons.

JOREK is a massively parallel fully implicit non-linear extended magneto-hydrodynamic (MHD) code for realistic tokamak X-point plasmas. It has become a widely used versatile simulation code for studying large-scale plasma instabilities and their control and is continuously developed in an international community with strong involvements in the European fusion research programme and ITER organization. This article gives a comprehensive overview of the physics models implemented, numerical methods applied for solving the equations and physics studies performed with the code. A dedicated section highlights some of the verification work done for the code. A hierarchy of different physics models is available including a free boundary and resistive wall extension and hybrid kinetic-fluid models. The code allows for flux-surface aligned iso-parametric finite element grids in single and double X-point plasmas which can be extended to the true physical walls and uses a robust fully implicit time stepping. Particular focus is laid on plasma edge and scrape-off layer (SOL) physics as well as disruption related phenomena. Among the key results obtained with JOREK regarding plasma edge and SOL, are deep insights into the dynamics of edge localized modes (ELMs), ELM cycles, and ELM control by resonant magnetic perturbations, pellet injection, as well as by vertical magnetic kicks. Also ELM free regimes, detachment physics, the generation and transport of impurities during an ELM, and electrostatic turbulence in the pedestal region are investigated. Regarding disruptions, the focus is on the dynamics of the thermal quench (TQ) and current quench triggered by massive gas injection and shattered pellet injection, runaway electron (RE) dynamics as well as the RE interaction with MHD modes, and vertical displacement events. Also the seeding and suppression of tearing modes (TMs), the dynamics of naturally occurring TQs triggered by locked modes, and radiative collapses are being studied.

Disruptions are a major operational concern for next generation tokamaks, including ITER. They may generate excessive heat loads on plasma facing components, large electromagnetic forces in the machine structures and several MA of multi-MeV runaway electrons. A more complete understanding of the runaway generation processes and methods to suppress them is necessary to ensure safe and reliable operation of future tokamaks. Runaway electrons were studied at JET-ILW showing that their generation dependencies (accelerating electric field, avalanche critical field, toroidal field, MHD fluctuations) are in agreement with current theories.

In the European fusion roadmap, ITER is followed by a demonstration fusion power reactor (DEMO), for which a conceptual design is under development. This paper reports the first results of a coherent effort to develop the relevant physics knowledge for that (DEMO Physics Basis), carried out by European experts. The program currently includes investigations in the areas of scenario modeling, transport, MHD, heating & current drive, fast particles, plasma wall interaction and disruptions.

Abstract:Disruptions are a critical issue for ITER because of the high thermal and magnetic energies that are released on short time scales, which results in extreme forces and heat loads. The choice of material of the plasma facing components (PFCs) can have significant impact on the loads that arise during a disruption. With the ITER-like wall (ILW) in JET made of beryllium in the main chamber and tungsten in the divertor, the main finding is a low fraction of radiation. This has dropped significantly with the ILW from 50-100% of the total energy being dissipated in the plasma with CFC to less than 50% on average and down to just 10% for VDEs. All other changes in disruption properties and loads are consequences of this low radiation: long current quenches, high vessel forces caused by halo currents and toroidal current asymmetries as well as severe heat loads. Temperatures close to the melting limit have been locally observed on upper first wall structures during deliberate VDE and even at plasma currents as low as 1.5 MA and thermal energy of about 1.5 MJ only. A high radiation fraction can be regained by massive injection of a mixture of 10%Ar with 90%D 2 . This accelerates the current quench and by this reducing halo and sideways impact. The temperature of PFCs stays below 400 ∘ C. MGI is now a mandatory tool to mitigate disruptions in closed-loop operation for currents at and above 2.5 MA in JET.

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.