Fusion performance in tokamaks hinges critically on the efficacy of the Edge Transport Barrier (ETB) at suppressing energy losses. The new concept of "fingerprints" is introduced to identify the instabilities that cause the transport losses in the ETB of many of today's experiments, from widely posited candidates. Analysis of the Gyrokinetic-Maxwell equations, and gyrokinetic simulations of experiments, find that each mode type produces characteristic ratios of transport in the various channels: density, heat and impurities. This, together with experimental observations of transport in some channel, or, of the relative size of the driving sources of channels, can identify or determine the dominant modes causing energy transport. In multiple ELMy H-mode cases that are examined, these fingerprints indicate that MHD-like modes are apparently not the dominant agent of energy transport; rather, this role is played by Micro-Tearing Modes (MTM) and Electron Temperature Gradient (ETG) modes, and in addition, possibly Ion Temperature Gradient (ITG)/Trapped Electron Modes (ITG/TEM) on JET. MHD-like modes may dominate the electron particle losses. Fluctuation frequency can also be an important means of identification, and is often closely related to the transport fingerprint. The analytical arguments unify and explain previously disparate experimental observations on multiple devices, including DIII-D, JET and ASDEX-U, and detailed simulations of two DIII-D ETBs also demonstrate and corroborate this.

Quasilinear turbulent transport models are a successful tool for prediction of core tokamak plasma profiles in many regimes. Their success hinges on the reproduction of local nonlinear gyrokinetic fluxes. We focus on significant progress in the quasilinear gyrokinetic transport model QuaLiKiz [C. Bourdelle et al. 2016 Plasma Phys. Control. Fusion 58 014036], which employs an approximated solution of the mode structures to significantly speed up computation time compared to full linear gyrokinetic solvers. Optimization of the dispersion relation solution algorithm within integrated modelling applications leads to flux calculations ×10 6−7 faster than local nonlinear simulations. This allows tractable simulation of flux-driven dynamic profile evolution including all transport channels: ion and electron heat, main particles, impurities, and momentum. Furthermore, QuaLiKiz now includes the impact of rotation and temperature anisotropy induced poloidal asymmetry on heavy impurity transport, important for W-transport applications. Application within the JETTO arXiv:1708.01224v2 [physics.plasm-ph] 7 Aug 2017Tractable flux-driven temperature, density, and rotation profile evolution with the quasilinear gyrokinetic tran integrated modelling code results in 1 s of JET plasma simulation within 10 hours using 10 CPUs. Simultaneous predictions of core density, temperature, and toroidal rotation profiles for both JET hybrid and baseline experiments are presented, covering both ion and electron turbulence scales. The simulations are successfully compared to measured profiles, with agreement mostly in the 5-25% range according to standard figures of merit. QuaLiKiz is now open source and available at www.qualikiz.com.Tractable flux-driven temperature, density, and rotation profile evolution with the quasilinear gyrokinetic tran

Abstract. The poloidal field (PF) coil system on ITER, which provides both feedforward and feedback control of plasma position, shape, and current, is a critical element for achieving mission performance. Analysis of PF capabilities has focused on the 15 MA Q = 10 scenario with a 300-500 s flattop burn phase. The operating space available for the 15 MA ELMy H-mode plasma discharges in ITER and upgrades to the PF coils or associated systems to establish confidence that ITER mission objectives can be reached have been identified. Time dependent self-consistent free-boundary calculations were performed to examine the impact of plasma variability, discharge programming, and plasma disturbances. Based on these calculations a new reference scenario was developed based upon a large bore initial plasma, early divertor transition, low level heating in L-mode, and a late H-mode onset. Equilibrium analyses for this scenario indicate that the original PF coil limitations do not allow low l i (<0.8) operation or lower flux states, and the flattop burn durations were predicted to be less than the desired 400 s. This finding motivates the expansion of the operating space, considering several upgrade options to the PF coils. Analysis was also carried out to examine the feedback current reserve required in the CS and PF coils during a series of disturbances and a feasibility assessment of the 17 MA scenario was undertaken. Results of the studies show that the new scenario and modified PF system will allow a wide range of 15 MA 300-500 s operation and more limited but finite 17 MA operation.

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.

The evolution of the JET high performance hybrid scenario, including central accumulation of the tungsten (W) impurity, is reproduced with predictive multi-channel integrated modelling over multiple confinement times using first-principle based core transport models. 8 transport channels (𝑇 𝑖 , 𝑇 𝑒 , 𝑗, 𝑛 𝐷 , 𝑛 𝐵𝑒 , 𝑛 𝑁𝑖 , 𝑛 𝑊 , 𝜔) are modelled predictively, with self-consistent sources, radiation and magnetic equilibrium, yielding a system with multiple non-linearities: This system can reproduce the observed radiative temperature collapse after several confinement times. W is transported inward by neoclassical convection driven by the main ion density gradients and enhanced by poloidal asymmetries due to centrifugal acceleration. The slow evolution of the bulk density profile sets the timescale for W accumulation. Modelling this phenomenon requires a turbulent transport model capable of accurately predicting particle and momentum transport (QuaLiKiz) and a neoclassical transport model including the effects of poloidal asymmetries (NEO) coupled to an integrated plasma simulator (JINTRAC). The modelling capability is applied to optimise the available actuators to prevent W accumulation, and to extrapolate in power and pulse length. Central NBI heating is preferred for high performance, but gives central deposition of particles and torque which increase the risk of W accumulation by increasing density peaking and poloidal asymmetry. The primary mechanism for ICRH to control W in JET is via its impact through turbulence in reducing main ion density peaking (which drives inward neoclassical convection), increased temperature screening and turbulent W diffusion. The anisotropy from ICRH also reduces poloidal asymmetry, but this effect is negligible in high rotation JET discharges. High power ICRH near the axis can sensitively mitigate against W accumulation, and dominant ion heating (e.g. He-3 minority) is predicted to provide more resilience to W accumulation than dominant electron heating (e.g. H minority) in the JET hybrid scenario. Extrapolation to DT plasmas finds 17.5MW of fusion power and improved confinement compared to DD, due to reduced ion-electron energy exchange, and increased Ti/Te stabilisation of ITG instabilities. The turbulence reduction in DT increases density peaking and accelerates the arrival of W on axis; this may be mitigated by reducing the penetration of the beam particle source with an increased pedestal density.