Analysis of MHD activity in pellet enhanced performance (PEP) pulses is used to determine the position of rational surfaces associated with the safety factor q. This gives evidence for negative shear in the central region of the plasma. The plasma equilibrium calculated from the measured q values yields a Shafranov shift in reasonable agreement with the experimental value of about 0.2 m. The corresponding current profile has two large off-axis maxima in agreement with the bootstrap current calculated from the electron temperature and density measurements. A transport simulation shows that the bootstrap current is driven by the steep density gradient, which results from improved confinement in the plasma core where the shear is negative. During the PEP phase (m,n)=(1,1) fast MHD events are correlated with collapses in the neutron rate. The dominant mode preceding these events usually is n=3, whereas the mode following them is dominantly n=2. Toroidal linear MHD stability calculations assuming a non-monotonic q-profile with an off-axis minimum decreasing from above 1 to below 1 describe this sequence of modes (n=3,1,2), but always give a larger growth rate for the n=1 mode than for the n=2 mode. This large growth rate is due to the high central poloidal beta of 1.5 observed in the PEP pulses. Finally, a rotating (m,n)=(1,1) mode is observed as a hot spot with a ballooning character on the low field side. The hot spot has some of the properties of a 'hot' island consistent with the presence of a region of negative shear

A number of possible designs of external and in-vessel coils generating resonant magnetic perturbations (RMPs) for Type I edge localized modes (ELMs) control in ITER are analysed for the reference scenarios (H-mode, Hybrid and Steady-State) taking into account physical, technical and spatial constraints. The level of stochasticity (Chirikov parameter ∼1 at ψ1/2 ∼ 0.95) generated by the I-coils in the DIII-D experiments on ELMs suppression was taken as a reference. Designs with a toroidal symmetry n = 3 were considered to avoid lower n numbers producing larger central islands, a potential trigger of MHD instabilities. The evaluation of RMP coils designs is done with respect to the RMPs spectrum that should produce enough edge ergodisation and minimum central perturbations at minimum current. The proposed designs include in-vessel, mid-ports and external coils. Changes in the equilibrium due to changes in the internal inductance l i, the poloidal beta βp and the edge magnetic shear in a reasonable range for ITER scenarios were demonstrated to have a small effect on the edge ergodisation. Present estimations were done without margins and for vacuum fields neglecting plasma response on RMPs. The validity of the vacuum approach was estimated analytically in the visco-resistive linear response regime using [1]. The typical radial magnetic field amplitudes produced by RMP coils in DIII-D and ITER are an order of magnitude or slightly above the critical values for the ‘downward’ bifurcation to the reconnected stage indicating the possibility of the islands formation in the pedestal region. Central islands (from the top of the pedestal) are expected to be screened.

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.

Equilibrium reconstruction is the essential tool for determining the field configuration and current density in a tokamak discharge. Most equilibrium reconstruction codes use the Grad-Shafranov equation, which relies on the assumption of isotropic pressure. This property is often violated for additionally heated discharges. We report on the implementation of an anisotropic pressure model for the equilibrium reconstruction code EFIT. The anisotropy model exhibits more degrees of freedom and makes the reconstruction more sensitive to experimental errors. We use a regularization technique (L-curve) that attempts to generate an optimal equilibrium. The algorithm is applied to selected highperformance discharges of the tokamaks JET and Tore Supra.