The objective of thermonuclear fusion consists of producing electricity from the coalescence of light nuclei in high temperature plasmas. The most promising route to fusion envisages the confinement of such plasmas with magnetic fields, whose most studied configuration is the tokamak. Disruptions are catastrophic collapses affecting all tokamak devices and one of the main potential showstoppers on the route to a commercial reactor. In this work we report how, deploying innovative analysis methods on thousands of JET experiments covering the isotopic compositions from hydrogen to full tritium and including the major D-T campaign, the nature of the various forms of collapse is investigated in all phases of the discharges. An original approach to proximity detection has been developed, which allows determining both the probability of and the time interval remaining before an incoming disruption, with adaptive, from scratch, real time compatible techniques. The results indicate that physics based prediction and control tools can be developed, to deploy realistic strategies of disruption avoidance and prevention, meeting the requirements of the next generation of devices.

A model for the pedestal density prediction based on neutral penetration combined with pedestal transport is presented. The model is tested against a pedestal database of JET-ILW Type I ELMy H-modes showing good agreement over a wide range of parameters both in standalone modelling (using the experimental temperature profile) and in full Europed modelling that predicts both density and temperature pedestals simultaneously. The model is further tested for ASDEX Upgrade and MAST-U Type I ELMy H-modes and both are found to agree with the same model parameters as for JET-ILW. The JET-ILW experiment where the isotope of the main ion is varied in a D/T scan at constant gas rate and constant βN is successfully modelled as long as the separatrix density (ne,sep) and pedestal transport coefficient ratio (D/χ) are varied in accordance with the experimentally observed variation of ne,sep and the isotope dependence of D/χ found in gyrokinetic simulations. The predictions are found to be sensitive to ne,sep which is why the model is combined with an ne,sep model to predict the pedestal for the STEP fusion reactor.

The neutral ionisation model proposed by Groebner et al (2002 Phys. Plasmas 9 2134) to determine the plasma density profile in the H-mode pedestal, is extended to include charge exchange processes in the pedestal stimulated by the ideas of Mahdavi et al (2003 Phys. Plasmas 10 3984). The model is then tested against JET H-mode pedestal data, both in a 'standalone' version using experimental temperature profiles and also by incorporating it in the Europed version of EPED. The model is able to predict the density pedestal over a wide range of conditions with good accuracy. It is also able to predict the experimentally observed isotope effect on the density pedestal that eludes simpler neutral ionization models.

In nuclear fusion reactors, plasmas are heated to very high temperatures of more than 100 million kelvin and, in so-called tokamaks, they are confined by magnetic fields in the shape of a torus. Light nuclei, such as deuterium and tritium, undergo a fusion reaction that releases energy, making fusion a promising option for a sustainable and clean energy source. Tokamak plasmas, however, are prone to disruptions as a result of a sudden collapse of the system terminating the fusion reactions. As disruptions lead to an abrupt loss of confinement, they can cause irreversible damage to present-day fusion devices and are expected to have a more devastating effect in future devices. Disruptions expected in the next-generation tokamak, ITER, for example, could cause electromagnetic forces larger than the weight of an Airbus A380. Furthermore, the thermal loads in such an event could exceed the melting threshold of the most resistant state-of-the-art materials by more than an order of magnitude. To prevent disruptions or at least mitigate their detrimental effects, empirical models obtained with artificial intelligence methods, of which an overview is given here, are commonly employed to predict their occurrence—and ideally give enough time to introduce counteracting measures.

Alpha particles with energies on the order of megaelectronvolts will be the main source of plasma heating in future magnetic confinement fusion reactors. Instead of heating fuel ions, most of the energy of alpha particles is transferred to electrons in the plasma. Furthermore, alpha particles can also excite Alfvénic instabilities, which were previously considered to be detrimental to the performance of the fusion device. Here we report improved thermal ion confinement in the presence of megaelectronvolts ions and strong fast ion-driven Alfvénic instabilities in recent experiments on the Joint European Torus. Detailed transport analysis of these experiments reveals turbulence suppression through a complex multi-scale mechanism that generates large-scale zonal flows. This holds promise for more economical operation of fusion reactors with dominant alpha particle heating and ultimately cheaper fusion electricity.

The JET 2019-2020 scientific and technological programme exploited the results of years of concerted scientific and engineering work, including the ITER-like wall (ILW: Be wall and W divertor) installed in 2010, improved diagnostic capabilities now fully available, a major neutral beam injection upgrade providing record power in 2019-2020, and tested the technical and procedural preparation for safe operation with tritium. Research along three complementary axes yielded a wealth of new results. Firstly, the JET plasma programme delivered scenarios suitable for high fusion power and alpha particle (alpha) physics in the coming D-T campaign (DTE2), with record sustained neutron rates, as well as plasmas for clarifying the impact of isotope mass on plasma core, edge and plasma-wall interactions, and for ITER pre-fusion power operation. The efficacy of the newly installed shattered pellet injector for mitigating disruption forces and runaway electrons was demonstrated. Secondly, research on the consequences of long-term exposure to JET-ILW plasma was completed, with emphasis on wall damage and fuel retention, and with analyses of wall materials and dust particles that will help validate assumptions and codes for design and operation of ITER and DEMO. Thirdly, the nuclear technology programme aiming to deliver maximum technological return from operations in D, T and D-T benefited from the highest D-D neutron yield in years, securing results for validating radiation transport and activation codes, and nuclear data for ITER.

The EUROfusion JET-ILW pedestal database is described, with emphasis on three main issues. First, the technical aspects are introduced, including a description of the data selection, the datasets, the diagnostics used, the experimental and theoretical methods implemented and the main definitions. Second, the JET-ILW pedestal structure and stability are described. In particular, the work describes the links between the engineering parameters (power, gas and divertor configuration) and the disagreement with the peeling-ballooning (PB) model implemented with ideal magnetohydrodynamics equations. Specifically, the work clarifies why the JET-ILW pedestal tends to be far from the PB boundary at high gas and high power, showing that a universal threshold in power and gas cannot be found but that the relative shift (the distance between the position of the pedestal density and of the pedestal temperature) plays a key role. These links are then used to achieve an empirical explanation of the behavior of the JET-ILW pedestal pressure with gas, power and divertor configuration. Third, the pedestal database is used to revise the scaling law of the pedestal stored energy. The work shows a reasonable agreement with the earlier Cordey scaling in terms of plasma current and triangularity dependence, but highlights some differences in terms of power and isotope mass dependence.

Two high performance JET-ILW pulses, pertaining to the 2016 experimental campaign, have been numerically simulated with the self-consistent code COREDIV with the aim of predicting the ELM-averaged power load to the target when extrapolated to DT plasmas. The input power of about 33 MW as well as the total radiated power and the average density are similar in the two pulses, but for one of them the density is provided by combined low gas puff and pellet injection, characterized by low SOL density, for the other one by gas fuelling only, at higher SOT. density. Considering the magnetic configuration of theses pulses and the presence of a significant amount of Ni (not included in the version of the code used for these simulations), a number of assumptions are made in order to reproduce numerically the main core and SOL experimental data. The extrapolation to DT plasmas at the original input power of 33 MW, and taking into account only the thermal component of the alpha-power, does not show any significant difference regarding the power to the target with respect to the DD case. In contrast, the simulations at auxiliary power 40 MW, both at the original I-p = 3 MA and at I-p = 4 MA, show that the power to the target for both pulses is possibly too high to be sustained for about 5 s by strike-point sweeping alone without any control by Ne seeding. Even though the target power load may decrease to about 13-15 MW with substantial Ne seeding for both pulses, as from numerical predictions, there are indications suggesting that the control of the power load may be more critical for the pulse with pellet injection, due to the reduced SOL radiation.

Convolutional neural networks (CNNs) have found applications in many image processing tasks, such as feature extraction, image classification, and object recognition. It has also been shown that the inverse of CNNs, so-called deconvolutional neural networks, can be used for inverse problems such as plasma tomography. In essence, plasma tomography consists in reconstructing the 2D plasma profile on a poloidal cross-section of a fusion device, based on line-integrated measurements from multiple radiation detectors. Since the reconstruction process is computationally intensive, a deconvolutional neural network trained to produce the same results will yield a significant computational speedup, at the expense of a small error which can be assessed using different metrics. In this work, we discuss the design principles behind such networks, including the use of multiple layers, how they can be stacked, and how their dimensions can be tuned according to the number of detectors and the desired tomographic resolution for a given fusion device. We describe the application of such networks at JET and COMPASS, where at JET we use the bolometer system, and at COMPASS we use the soft X-ray diagnostic based on photodiode arrays.

In ASDEX Upgrade, the propagation of cold pulses induced by type-I edge localized modes (ELMs) is studied using electron cyclotron emission measurements, in a dataset of plasmas with moderate triangularity. It is found that the edge safety factor or the plasma current are the main determining parameters for the inward penetration of the T-e perturbations. With increasing plasma current the ELM penetration is more shallow in spite of the stronger ELMs. Estimates of the heat pulse diffusivity show that the corresponding transport is too large to be representative of the inter-ELM phase. Ergodization of the plasma edge during ELMs is a possible explanation for the observed properties of the cold pulse propagation, which is qualitatively consistent with non-linear magneto-hydro-dynamic simulations.