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 (NBI) upgrade providing record power in 2019-2020, and tested the technical & 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 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 & 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.

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.

No abstract

We report a discovery of a fusion plasma regime suitable for commercial fusion reactor where the ion temperature was sustained above 100 million degree about 20 s for the rst time. Nuclear fusion as a promising technology for replacing carbon-dependent energy sources has currently many issues to be resolved to enable its large-scale use as a sustainable energy source. State-of-the-art fusion reactors cannot yet achieve the high levels of fusion performance, high temperature, and absence of instabilities required for steady-state operation for a long period of time on the order of hundreds of seconds. This is a pressing challenge within the eld, as the development of methods that would enable such capabilities is essential for the successful construction of commercial fusion reactor. Here, a new plasma con nement regime called fast ion roled enhancement (FIRE) mode is presented. This mode is realized at Korea Superconducting Tokamak Advanced Research (KSTAR) and subsequently characterized to show that it meets most of the requirements for fusion reactor commercialization. Through a comparison to other well-known plasma con nement regimes, the favourable properties of FIRE mode are further elucidated and concluded that the novelty lies in the high fraction of fast ions, which acts to stabilize turbulence and achieve steady-state operation for up to 20 s by self-organization. We propose this mode as a promising path towards commercial fusion reactors.

In this work, we address a new feedforward control scheme for the normalized beta (β N) in tokamak plasmas, using the deep reinforcement learning (RL) technique. The deep RL algorithm optimizes an artificial decision-making agent that adjusts the discharge scenario to obtain a given target β N from the state–action–reward sets explored by its own trial and error in a virtual tokamak environment. The virtual environment for the RL training is constructed using a long short-term memory (LSTM) network that imitates the plasma responses to external actuator controls, which is trained using five years’ worth of KSTAR experimental data. The RL agent then experiences numerous discharges with different actuator controls in the LSTM simulator, and its internal parameters are optimized in the direction of maximizing the reward. We analyze a series of KSTAR experiments conducted with the RL-determined scenarios to validate the feasibility of the beta control scheme in a real device. We discuss the successes and limitations of feedforward beta control by RL, and suggest a future research path for this area of study.