Characterisation of divertor detachment onset in JET-ILW hydrogen, deuterium, tritium and deuterium–tritium low-confinement mode plasmas

Measurements of the ion currents to and plasma conditions at the low-field side (LFS) divertor target plate in low-confinement mode plasmas in the JET ITER-like wall materials configuration show that the core plasma density required to detach the LFS divertor plasma is independent of the hydrogenic species protium, deuterium and tritium, and a 40 %/60 % deuterium–tritium mixture. This observation applies to a divertor plasma configuration with the LFS strike line connected to the horizontal part of the LFS divertor chosen because of its superior diagnostic coverage. The finding is independent of the operational status of the JET cryogenic pump. The electron temperature (Te) at the LFS strike line was markedly reduced from 25 eV to 5 eV over a narrow range of increasing core plasma density, and observed to be between 2 eV and 3 eV at the onset of detachment. The electron density (ne) peaks across the LFS plasma when Te at the target plate is 1 eV, and spatially moves to the X-point for higher core densities. The density limit was found approximately 20 % higher in protium than in tritium and deuterium–tritium plasmas

CNN disruption predictor at JET: Early versus late data fusion approach

This work focuses on the development of a data driven model, based on Convolutional Neural Networks (CNNs), for the real-time detection of disruptive events at JET. The predictor exploits the ability of CNNs in learning relevant spatiotemporal information straight from 1D plasma profiles, avoiding hand-engineered feature extraction procedures. In this paper, the radiation profiles from both the bolometer horizontal and vertical cameras have been considered amongst the predictor inputs, with the aim of discriminating between the core radiation due to impurity accumulations and the outboard radiation phenomena. Moreover, an innovative predictor architecture is proposed, where two separate CNNs are trained to focus on events with different timescales, that is, the destabilization of radiation, electron density and temperature profiles, and the mode-locking and current profile variations. The outputs of the two CNNs are combined with a logic OR function to provide the disruption alarm trigger. The advantages of this data fusion approach impact on the predictor performance, with a very limited number of false alarms (only 1 in the considered test set), and on the model output interpretability as the two different branches are triggered by different types of events

Performance Comparison of Machine Learning Disruption Predictors at JET

Reliable disruption prediction (DP) and disruption mitigation systems are considered unavoidable during international thermonuclear experimental reactor (ITER) operations and in the view of the next fusion reactors such as the DEMOnstration Power Plant (DEMO) and China Fusion Engineering Test Reactor (CFETR). In the last two decades, a great number of DP systems have been developed using data-driven methods. The performance of the DP models has been improved over the years both for a more appropriate choice of diagnostics and input features and for the availability of increasingly powerful data-driven modelling techniques. However, a direct comparison among the proposals has not yet been conducted. Such a comparison is mandatory, at least for the same device, to learn lessons from all these efforts and finally choose the best set of diagnostic signals and the best modelling approach. A first effort towards this goal is made in this paper, where different DP models will be compared using the same performance indices and the same device. In particular, the performance of a conventional Multilayer Perceptron Neural Network (MLP-NN) model is compared with those of two more sophisticated models, based on Generative Topographic Mapping (GTM) and Convolutional Neural Networks (CNN), on the same real time diagnostic signals from several experiments at the JET tokamak. The most common performance indices have been used to compare the different DP models and the results are deeply discussed. The comparison confirms the soundness of all the investigated machine learning approaches and the chosen diagnostics, enables us to highlight the pros and cons of each model, and helps to consciously choose the approach that best matches with the plasma protection needs

Core micro-instability analysis of JET hybrid and baseline discharges with carbon wall

The core micro-instability characteristics of hybrid and baseline plasmas in a selected set of JET plasmas with carbon wall are investigated through local linear and non-linear and global linear gyro-kinetic simulations with the GYRO code [J. Candy and E. Belli, General Atomics Report GA-A26818 (2011)]. In particular, we study the role of plasma pressure on the micro-instabilities, and scan the parameter space for the important plasma parameters responsible for the onset and stabilization of the modes under experimental conditions. We find that a good core confinement due to strong stabilization of the micro-turbulence driven transport can be expected in the hybrid plasmas due to the stabilizing effect of the fast ion pressure that is more effective at the low magnetic shear of the hybrid discharges. While parallel velocity gradient destabilization is important for the inner core, at outer radii the hybrid plasmas may benefit from a strong quench of the turbulence transport by $\mathbf{E}\times\mathbf{B}$ rotation shear.Comment: accepted for publication in Nuclear Fusio

3D simulations of gas puff effects on edge plasma and ICRF coupling in JET

Recent JET (ITER-Like Wall) experiments have shown that the fueling gas puffed from different locations of the vessel can result in different scrape-off layer (SOL) density profiles and therefore different radio frequency (RF) coupling. To reproduce the experimental observations, to understand the associated physics and to optimize the gas puff methods, we have carried out three-dimensional (3D) simulations with the EMC3-EIRENE code in JET-ILW including a realistic description of the vessel geometry and the gas injection modules (GIMs) configuration. Various gas puffing methods have been investigated, in which the location of gas fueling is the only variable parameter. The simulation results are in quantitative agreement with the experimental measurements. They confirm that compared to divertor gas fueling, mid-plane gas puffing increases the SOL density most significantly but locally, while top gas puffing increases it uniformly in toroidal direction but to a lower degree. Moreover, the present analysis corroborates the experimental findings that combined gas puff scenarios-based on distributed main chamber gas puffing-can be effective in increasing the RF coupling for multiple antennas simultaneously. The results indicate that the spreading of the gas, the local ionization and the transport of the ionized gas along the magnetic field lines connecting the local gas cloud in front of the GIMs to the antennas are responsible for the enhanced SOL density and thus the larger RF coupling

Gyrokinetic analysis and simulation of pedestals, to identify the culprits for energy losses using fingerprints

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

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Comparison of Theory with Rotation Measurements in JET ICRH Plasmas

Plasma rotation appears to improve plasma performance by increasing the E x B flow shearing rate, thus decreasing radial correlations in the microturbulence. Also, plasma rotation can increase the stability to resistive MHD modes. In the Joint European Torus (JET), toroidal rotation rates omega (subscript ''tor'') with high Mach numbers are generally measured in NBI-heated plasmas (since the neutral beams aim in the co-plasma current direction). They are considerably lower with only ICRH (and Ohmic) heating, but still surprisingly large considering that ICRH appears to inject relatively small amounts of angular momentum. Either the applied torques are larger than naively expected, or the anomalous transport of angular momentum is smaller than expected. Since ICRH is one of the main candidates for heating next-step tokamaks, and for creating burning plasmas in future tokamak reactors, this paper attempts to understand ICRH-induced plasma rotation