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In the Tokamak discharge experiment, obtaining the largest possible null field region is a necessary condition for the smooth breakdown of the plasma, and adjusting the poloidal field coil current is key to achieving a better null field region. This paper, based on the Sino-Thai Tokamak cooperation project Thailand Tokamak-1 (TT-1) device, employs an exponentially decreasing Particle Swarm Optimization (PSO) algorithm to optimize the poloidal field coil current to create the desired null field region in the vacuum chamber area. First, a calculation model for the mutual inductance coefficient and the null field region is established according to the characteristics and magnetic structure of the TT-1 device, enabling the calculation of the null field region. Then, an optimization model for the poloidal field coil current is established, aiming to create a sufficiently large null field region (less than 10 Gauss) to facilitate breakdown. The optimization is carried out using both a typical linearly decreasing PSO algorithm and an improved PSO algorithm to determine the optimal poloidal field coil current. Compared to the unmodified PSO algorithm, the improved PSO algorithm reduces the root mean square error by 31.80%. The results show that the improved PSO algorithm is more suitable for the optimization of the poloidal field coil, has stronger optimization capabilities, and can effectively create the desired null field region, providing an important reference for the smooth breakdown of plasma in the TT-1 device.

Nuclear fusion using magnetic confinement, in particular in the tokamak configuration, is a promising path towards sustainable energy. A core challenge is to shape and maintain a high-temperature plasma within the tokamak vessel. This requires high-dimensional, high-frequency, closed-loop control using magnetic actuator coils, further complicated by the diverse requirements across a wide range of plasma configurations. In this work, we introduce a previously undescribed architecture for tokamak magnetic controller design that autonomously learns to command the full set of control coils. This architecture meets control objectives specified at a high level, at the same time satisfying physical and operational constraints. This approach has unprecedented flexibility and generality in problem specification and yields a notable reduction in design effort to produce new plasma configurations. We successfully produce and control a diverse set of plasma configurations on the Tokamak à Configuration Variable 1 , 2 , including elongated, conventional shapes, as well as advanced configurations, such as negative triangularity and ‘snowflake’ configurations. Our approach achieves accurate tracking of the location, current and shape for these configurations. We also demonstrate sustained ‘droplets’ on TCV, in which two separate plasmas are maintained simultaneously within the vessel. This represents a notable advance for tokamak feedback control, showing the potential of reinforcement learning to accelerate research in the fusion domain, and is one of the most challenging real-world systems to which reinforcement learning has been applied. A newly designed control architecture uses deep reinforcement learning to learn to command the coils of a tokamak, and successfully stabilizes a wide variety of fusion plasma configurations.

The SPARC tokamak is a critical next step towards commercial fusion energy. SPARC is designed as a high-field ($B_0 = 12.2$ T), compact ($R_0 = 1.85$ m, $a = 0.57$ m), superconducting, D-T tokamak with the goal of producing fusion gain $Q>2$ from a magnetically confined fusion plasma for the first time. Currently under design, SPARC will continue the high-field path of the Alcator series of tokamaks, utilizing new magnets based on rare earth barium copper oxide high-temperature superconductors to achieve high performance in a compact device. The goal of $Q>2$ is achievable with conservative physics assumptions ($H_{98,y2} = 0.7$) and, with the nominal assumption of $H_{98,y2} = 1$, SPARC is projected to attain $Q \\approx 11$ and $P_{\\textrm {fusion}} \\approx 140$ MW. SPARC will therefore constitute a unique platform for burning plasma physics research with high density ($\\langle n_{e} \\rangle \\approx 3 \\times 10^{20}\\ \\textrm {m}^{-3}$), high temperature ($\\langle T_e \\rangle \\approx 7$ keV) and high power density ($P_{\\textrm {fusion}}/V_{\\textrm {plasma}} \\approx 7\\ \\textrm {MW}\\,\\textrm {m}^{-3}$) relevant to fusion power plants. SPARC's place in the path to commercial fusion energy, its parameters and the current status of SPARC design work are presented. This work also describes the basis for global performance projections and summarizes some of the physics analysis that is presented in greater detail in the companion articles of this collection.

The tokamak approach, utilizing a toroidal magnetic field configuration to confine a hot plasma, is one of the most promising designs for developing reactors that can exploit nuclear fusion to generate electrical energy 1 , 2 . To reach the goal of an economical reactor, most tokamak reactor designs 3 – 10 simultaneously require reaching a plasma line-averaged density above an empirical limit—the so-called Greenwald density 11 —and attaining an energy confinement quality better than the standard high-confinement mode 12 , 13 . However, such an operating regime has never been verified in experiments. In addition, a long-standing challenge in the high-confinement mode has been the compatibility between a high-performance core and avoiding large, transient edge perturbations that can cause very high heat loads on the plasma-facing-components in tokamaks. Here we report the demonstration of stable tokamak plasmas with a line-averaged density approximately 20% above the Greenwald density and an energy confinement quality of approximately 50% better than the standard high-confinement mode, which was realized by taking advantage of the enhanced suppression of turbulent transport granted by high density-gradients in the high-poloidal-beta scenario 14 , 15 . Furthermore, our experimental results show an integration of very low edge transient perturbations with the high normalized density and confinement core. The operating regime we report supports some critical requirements in many fusion reactor designs all over the world and opens a potential avenue to an operating point for producing economically attractive fusion energy. A stable tokamak plasma has been demonstrated with a high plasma density and a high energy confinement quality, both of which are simultaneously important for fusion reactors.

For stable and efficient fusion energy production using a tokamak reactor, it is essential to maintain a high-pressure hydrogenic plasma without plasma disruption. Therefore, it is necessary to actively control the tokamak based on the observed plasma state, to manoeuvre high-pressure plasma while avoiding tearing instability, the leading cause of disruptions. This presents an obstacle-avoidance problem for which artificial intelligence based on reinforcement learning has recently shown remarkable performance 1 – 4 . However, the obstacle here, the tearing instability, is difficult to forecast and is highly prone to terminating plasma operations, especially in the ITER baseline scenario. Previously, we developed a multimodal dynamic model that estimates the likelihood of future tearing instability based on signals from multiple diagnostics and actuators 5 . Here we harness this dynamic model as a training environment for reinforcement-learning artificial intelligence, facilitating automated instability prevention. We demonstrate artificial intelligence control to lower the possibility of disruptive tearing instabilities in DIII-D 6 , the largest magnetic fusion facility in the United States. The controller maintained the tearing likelihood under a given threshold, even under relatively unfavourable conditions of low safety factor and low torque. In particular, it allowed the plasma to actively track the stable path within the time-varying operational space while maintaining H-mode performance, which was challenging with traditional preprogrammed control. This controller paves the path to developing stable high-performance operational scenarios for future use in ITER. Artificial intelligence control is used to avoid the emergence of disruptive tearing instabilities in the magnetically confined fusion plasma in the DIII-D tokamak reactor.

Research on magnetic confinement of high-temperature plasmas has the ultimate goal of harnessing nuclear fusion for the production of electricity. Although the tokamak 1 is the leading toroidal magnetic-confinement concept, it is not without shortcomings and the fusion community has therefore also pursued alternative concepts such as the stellarator. Unlike axisymmetric tokamaks, stellarators possess a three-dimensional (3D) magnetic field geometry. The availability of this additional dimension opens up an extensive configuration space for computational optimization of both the field geometry itself and the current-carrying coils that produce it. Such an optimization was undertaken in designing Wendelstein 7-X (W7-X) 2 , a large helical-axis advanced stellarator (HELIAS), which began operation in 2015 at Greifswald, Germany. A major drawback of 3D magnetic field geometry, however, is that it introduces a strong temperature dependence into the stellarator’s non-turbulent ‘neoclassical’ energy transport. Indeed, such energy losses will become prohibitive in high-temperature reactor plasmas unless a strong reduction of the geometrical factor associated with this transport can be achieved; such a reduction was therefore a principal goal of the design of W7-X. In spite of the modest heating power currently available, W7-X has already been able to achieve high-temperature plasma conditions during its 2017 and 2018 experimental campaigns, producing record values of the fusion triple product for such stellarator plasmas 3 , 4 . The triple product of plasma density, ion temperature and energy confinement time is used in fusion research as a figure of merit, as it must attain a certain threshold value before net-energy-producing operation of a reactor becomes possible 1 , 5 . Here we demonstrate that such record values provide evidence for reduced neoclassical energy transport in W7-X, as the plasma profiles that produced these results could not have been obtained in stellarators lacking a comparably high level of neoclassical optimization. Previously documented record values of the fusion triple product in the stellarator Wendelstein 7-X are shown to be evidence for reduced neoclassical energy transport in this optimized device.

This work provides a general description of the self-consistent energetic particle phase space transport in burning plasmas, based on nonlinear gyrokinetic theory. The self consistency is ensured by the evolution equations of the Alfvénic fluctuations by means of nonlinear radial envelope evolution equations, while energetic particle fluxes in the phase space are explicitly constructed from long-lived phase space zonal structures, which are undamped by collisionless processes. As a result, this work provides a viable route to computing fluctuation induced energetic particle transport on long time scales in realistic tokamak plasmas.

In a tokamak, the first wall is a barrier protecting the internal parts of the machine from the intense fluxes of heat and particles coming from the plasma. This contribution presents the critical issues of a water-cooled first wall component based on steel as structural material, that is a relevant case study for the design of future fusion power plants.

The heating neutral beam injectors (HNBs) of ITER are designed to deliver 16.7 MW of 1 MeV D0 or 0.87 MeV H0 to the ITER plasma for up to 3600 s. They will be the most powerful neutral beam (NB) injectors ever, delivering higher energy NBs to the plasma in a tokamak for longer than any previous systems have done. The design of the HNBs is based on the acceleration and neutralisation of negative ions as the efficiency of conversion of accelerated positive ions is so low at the required energy that a realistic design is not possible, whereas the neutralisation of H− and D− remains acceptable ( 56%). The design of a long pulse negative ion based injector is inherently more complicated than that of short pulse positive ion based injectors because: negative ions are harder to create so that they can be extracted and accelerated from the ion source; electrons can be co-extracted from the ion source along with the negative ions, and their acceleration must be minimised to maintain an acceptable overall accelerator efficiency; negative ions are easily lost by collisions with the background gas in the accelerator; electrons created in the extractor and accelerator can impinge on the extraction and acceleration grids, leading to high power loads on the grids; positive ions are created in the accelerator by ionisation of the background gas by the accelerated negative ions and the positive ions are back-accelerated into the ion source creating a massive power load to the ion source; electrons that are co-accelerated with the negative ions can exit the accelerator and deposit power on various downstream beamline components. The design of the ITER HNBs is further complicated because ITER is a nuclear installation which will generate very large fluxes of neutrons and gamma rays. Consequently all the injector components have to survive in that harsh environment. Additionally the beamline components and the NB cell, where the beams are housed, will be activated and all maintenance will have to be performed remotely. This paper describes the design of the HNB injectors, but not the associated power supplies, cooling system, cryogenic system etc, or the high voltage bushing which separates the vacuum of the beamline from the high pressure SF6 of the high voltage (1 MV) transmission line, through which the power, gas and cooling water are supplied to the beam source. Also the magnetic field reduction system is not described.

On the road to fusion energy production, many alternative scenarios have been investigated in order to address certain well-known problems of tokamak devices; among which, anomalous transport, ELMs and disruptions. The studies on plasma shaping fall into this effort. In particular, it has been experimentally observed that when operating in L mode, negative triangularity (NT) features better confinement properties than positive triangularity (PT). However, even though the trend is quite clear, a complete and satisfying theoretical explanation for this experimental findings is still lacking. With the aim of understanding and describing these improvements starting from first principles, we present the first comparison between PT and NT with global flux-driven gyrokinetic simulations performed with the ORB5 code. The numerical setup includes: electrostatic turbulence, kinetic trapped electrons, non-linear collisional operator, ECRH source, limiter and wall as boundary conditions. The simulations have been performed on ideal MHD equilibria and kinetic profiles inspired by TCV experiments, in a mixed ITG-TEM regime. First analysis reveal a strong reduction of transport in NT; while at the edge PT shows superdiffusivity, NT does not. The limiter plays an important role that has to be further clarified.