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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. Experiments at the Joint European Torus tokamak show improved thermal ion confinement in the presence of highly energetic ions and Alfvénic instabilities in the plasma.

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. Tokamak plasmas are prone to sudden collapses that terminate the nuclear fusion reactions. This perspective discusses the prediction of these so-called disruptions with artificial intelligence techniques.

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. Confining plasma and managing disruptions in tokamak devices is a challenge. Here the authors demonstrate a method predicting and possibly preventing disruptions and macroscopic instabilities in tokamak plasma using data from JET.

The most efficient and promising operational regime for the International Thermonuclear Experimental Reactor tokamak is the high-confinement mode. In this regime, however, periodic relaxations of the plasma edge can occur. These edge-localized modes pose a threat to the integrity of the fusion device. Here we reveal the strong impact of energetic ions on the spatio-temporal structure of edge-localized modes in tokamaks using nonlinear hybrid kinetic–magnetohydrodynamic simulations. A resonant interaction between the fast ions at the plasma edge and the electromagnetic perturbations from the edge-localized mode leads to an energy and momentum exchange. Energetic ions modify, for example, the amplitude, frequency spectrum and crash timing of edge-localized modes. The simulations reproduce some observations that feature abrupt and large edge-localized mode crashes. The results indicate that, in the International Thermonuclear Experimental Reactor, a strong interaction between the fusion-born alpha particles and ions from neutral beam injection, a main heating and fast particle source, is expected with predicted edge-localized mode perturbations. This work advances the understanding of the physics underlying edge-localized mode crashes in the presence of energetic particles and highlights the importance of including energetic ion kinetic effects in the optimization of edge-localized mode control techniques and regimes that are free of such modes. Edge-localized plasma modes in a tokamak can damage its innermost wall. Simulations now show that fast ions can modify the spatio-temporal structure of these modes. These effects need to be considered in the optimization of control techniques.

Progress is reviewed on the simulation of wave-particle interactions in the ion cyclotron range of frequencies (ICRF). Two important aspects of this problem are described. First, mode conversion from a long wavelength fast magnetosonic wave to short wavelength ion Bernstein waves (IBW) and ion cyclotron waves (ICW) is simulated and validation tests of the simulations against experiment are presented. Second, simulations of the quasilinear evolution of nonthermal ion tails during the minority heating are reviewed and experimental validation tests are also discussed. In this paper we describe how access to teraflop computing capability has made it possible to advance the state of the art in this area. We also discuss two aspects of the wave-particle interaction where future work is needed and where in particular access to sub-petaflop and petaflop computing capability would be highly desirable. This work involves the interaction of ICRF waves with energetic neutral beam ions at high ion cyclotron harmonic number and addresses the inclusion of finite ion drift orbit effects in the nonthermal ion tail evolution and the inclusion of nonlinear effects such as RF sheaths in the antenna – edge plasma coupling.

The ITER ICRF system will gain in complexity relative to the existing systems on modern devices, and the same will hold true for DEMO. The accumulated experience can help greatly in designing an ICRF system for DEMO. In this paper the current status of the pre-conceptual design of the DEMO ICRF antenna and some related components is presented. While many aspects strongly resemble the ITER system, in some design solutions we had to take an alternative route to be able to adapt to DEMO specific. One of the key points is the toroidal antenna extent needed for the requested ICRF heating performance, achieved by splitting the antenna in halves, with appropriate installation. Modelling of the so far largest ICRF antenna in RAPLICASOL and associated challenges are presented. Calculation are benchmarked with TOPICA. Results of the analysis of the latest model and an outlook for future steps are given.

The major aspects linked to the use of the JET auxiliary heating systems: NBI, ICRF and LHCD, in the new JET ITER-like wall (JET-ILW) are presented. We show that although there were issues related to the operation of each system, efficient and safe plasma heating was obtained with room for higher power. For the NBI up to 25.7MW was safely injected; issues that had to be tackled were mainly the beam shine-through and beam re-ionisation before its entrance into the plasma. For the ICRF system, 5MW were coupled in L-mode and 4MW in H-mode; the main areas of concern were RF-sheaths related heat loads and impurities production. For the LH, 2.5 MW were delivered without problems; arcing and generation of fast electron beams in front of the launcher that can lead to high heat loads were the keys issues. For each system, an overview will be given of: the main modifications implemented for safe use, their compatibility with the new metallic wall, the differences in behavior compared with the previous carbon wall, with emphasis on heat loads and impurity content in the plasma.

INTRODUCTION Incorporating blood‐based Alzheimer's disease biomarkers such as tau and amyloid beta (Aβ) into screening algorithms may improve screening efficiency. METHODS Plasma Aβ, phosphorylated tau (p‐tau)181, and p‐tau217 concentration levels from AHEAD 3–45 study participants were measured using mass spectrometry. Tau concentration ratios for each proteoform were calculated to normalize for inter‐individual differences. Receiver operating characteristic (ROC) curve analysis was performed for each biomarker against amyloid positivity, defined by > 20 Centiloids. Mixture of experts analysis assessed the value of including tau concentration ratios into the existing predictive algorithm for amyloid positron emission tomography status. RESULTS The area under the receiver operating curve (AUC) was 0.87 for Aβ42/Aβ40, 0.74 for phosphorylated variant p‐tau181 ratio (p‐tau181/np‐tau181), and 0.92 for phosphorylated variant p‐tau217 ratio (p‐tau217/np‐tau217). The Plasma Predicted Centiloid (PPC), a predictive model including p‐tau217/np‐tau217, Aβ42/Aβ40, age, and apolipoprotein E improved AUC to 0.95. DISCUSSION Including plasma p‐tau217/np‐tau217 along with Aβ42/Aβ40 in predictive algorithms may streamline screening preclinical individuals into anti‐amyloid clinical trials. ClinicalTrials.gov Identifier: NCT04468659 Highlights The addition of plasma phosphorylated variant p‐tau217 ratio (p‐tau217/np‐tau217) significantly improved plasma biomarker algorithms for identifying preclinical amyloid positron emission tomography positivity. Prediction performance at higher NAV Centiloid levels was improved with p‐tau217/np‐tau217. All models generated for this study are incorporated into the Plasma Predicted Centiloid (PPC) app for public use.