Journal of Fusion Energy最新文献

This paper reviews the contributions of image observations to extending the duration of Ion Cyclotron Range of Frequencies (ICRF)-heated long-pulse discharges in the Large Helical Device (LHD). The plasma discharges were monitored using over 25 visible cameras, three fast-framing cameras, and various advanced plasma diagnostics, which revealed that most long-pulse discharges were interrupted by the following four events: termination of ICRF plasma heating due to arcing events in antennas, uncontrollable plasma density rise by outgassing from divertor plates, iron influx from plasma-facing components in the vacuum vessel, and carbon influx originating from the divertor regions. Image observations played a crucial role in mitigating the above four events that restricted the duration of long-pulse discharges by implementing appropriate countermeasures such as enhancing the cooling efficiency of the divertor plates, adopting new operational techniques to disperse the heat-load distribution, improving the ICRF antenna configurations, installing new additional ICRF antennas, and modifying the divertor configuration. Interruptions in long-pulse discharges were statistically analyzed using experimental data in three previous experimental campaigns, demonstrating a history of continuous efforts to extend the plasma discharge duration. This paper highlights the contributions of image observations over the past two decades, which have revealed inherent limitations in conventional magnetic plasma confinement devices that utilize carbon and iron plasma-facing components in sustaining steady-state plasma discharges. Knowledge obtained from statistical analysis provides valuable information for optimizing next-generation plasma confinement devices aiming at steady-state operation.

The EUV Imaging Spectrometer (EIS) on board the Hinode mission is capable of observing solar coronal plasma possibly in non-ionization-equilibrium. EUV emission lines from highly charged Fe ions observed in the solar corona are also produced in the Large Helical Device (LHD) and the compact electron beam ion trap (CoBIT). Time-dependent collisional-radiative model (CRM) for Fe ions is developed to diagnose those plasmas in the Sun and the laboratories by adopting the best available theoretical calculations of atomic parameters, as well as generating the experimental data.

The negative pressure ventilation system, serving as a critical subsystem within the tritium safety confinement system of the compact fusion energy experimental, is responsible for maintaining dynamic confinement functionality over the C2 and C3 confinement subzones during normal operational conditions, thereby restricting the leakage and dispersion of radioactive materials. Consequently, rational and reliable design of the tritium confinement negative pressure ventilation system is of paramount importance. Key aspects of the system process design include determining pipeline network dimensions and evaluating thermal-hydraulic characteristics. In this study, the system design requirements were established in accordance with the international standard ISO 16646:2024 and the operational specifications of the device. This involved calculating the system airflow capacity and proposing a preliminary process design scheme for the negative pressure ventilation system. A thermal-hydraulic computational model for the initial system configuration was developed using the fluid simulation software AFT Arrow. Steady-state simulations were conducted to predict the thermal-hydraulic behavior of the pipeline network under maximum ventilation load conditions, enabling the determination of critical equipment selection parameters. The findings of this study will lay the groundwork for subsequent research on negative pressure ventilation systems in compact fusion energy applications, providing essential technical insights for optimizing system performance and ensuring compliance with radiological safety standards.

Over the last few years, machine learning helped to develop advanced capabilities for fusion energy over a broad range of domains. This includes advanced algorithms to extract information from fusion diagnostics, enhanced algorithms for plasma state estimation and control, accelerated simulation tools to improve predictive capabilities, and expanded modeling capabilities for fusion materials design. This topical collection covers recent developments in machine learning applied research further enabling the path to fusion energy; in particular it covers a wide breadth of fusion subfields – from inertial confinement fusion, to magnetically confined plasma, including high temperature superconducting magnet design and optimization. This editorial summarizes the collection while also providing a critical outlook on how machine learning can be used in the future to accelerate the development of fusion energy as a reliable energy source.

This paper provides a retrospective of the BETHE (Breakthroughs Enabling THermonuclear-fusion Energy) and GAMOW (Galvanizing Advances in Market-aligned fusion for an Overabundance of Watts) fusion programs of the Advanced Research Projects Agency-Energy (ARPA-E), as well as fusion project cohorts (associated with OPEN 2018, OPEN 2021, and Exploratory Topics) initiated during the same time period (2018–2022). BETHE (announced in 2019) aimed to increase the number of higher-maturity, lower-cost fusion approaches. GAMOW (announced in 2020) aimed to expand and translate research-and-development efforts in materials, fuel cycle, and enabling technologies needed for commercial fusion energy. Both programs had a vision of enabling timely commercial fusion energy while laying the foundation for greater public-private collaborations to accelerate fusion-energy development. Finally, this paper describes ARPA-E’s fusion Technology-to-Market (T2M) activities during this era, which included supporting ARPA-E fusion performers’ commercialization pathways, improving fusion costing models, exploring cost targets for potential early markets for fusion energy, engaging with the broader fusion ecosystem (especially investors and nongovernmental organizations), and highlighting the importance of social license for timely fusion commercialization.

This article reviews various achievements in spectroscopy of highly charged ions of a variety of heavy elements injected into the Large Helical Device (LHD) plasmas. We focus on discrete and quasi-continuum spectra observed in extreme ultraviolet (EUV) and soft X-ray wavelength ranges using multiple grazing incidence spectrometers. In particular, the atomic number dependence and temperature dependence of the spectral features have been investigated more comprehensively than ever before over extremely wide ranges based on comparisons with theoretical models and other experimental data. Consequently, the series of studies could provide an experimental database valuable for investigations of basic atomic physics issues specific to highly charged heavy ions, as well as the applications to industrial light source developments.

This study investigates the density distributions of hydrogen ((mathrm {H^0}) and (mathrm {H^+})) and lithium ((mathrm {Li^0}), (mathrm {Li^+}), (mathrm {Li^{2+}}), and (mathrm {Li^{3+}})) atoms and ions in a magnetized plasma exposed to a liquid lithium surface, and evaluates the potential for magnetohydrodynamic (MHD) instability triggered by pressure variations in the plasma. The physical model employs multiple reaction rate equations for various species and charge states, including net ionization and recombination to describe density distributions. MHD stability is assessed using the energy principle associated with pressure gradients. The analysis is conducted under simplified conditions neglecting curvature and time variations in plasma temperature and toroidal magnetic field. The results strongly suggest that under high central electron temperature, the electron density and total pressure in a plasma with a liquid lithium surface peak in the core–edge transition region. This leads to a steep negative radial pressure gradient, in which a pressure-driven instability is mitigated. This simplified study demonstrates that pressure-driven instability in the edge region can be avoided if an optimal balance is maintained between central electron temperature and vapor flux from the liquid lithium surface.

Experimental research was conducted to develop a corrosion barrier for protecting the copper-chromium-zirconium (CuCrZr) and stainless steel components of future liquid tin divertors. W-based coatings with tailored properties were deposited on planar metallic samples by high power impulse magnetron sputtering (HiPIMS), optimizing the deposition parameters to enhance mechanical properties and improve corrosion resistance. A validation experimental campaign was carried out based on conditions expected in the ENEA liquid metal divertor design. To identify the highest-performing coating, coated samples were exposed to a static droplet of liquid tin at (400,^{circ })C for up to 600 min, followed by post-mortem characterization. The results indicate that tin corrosion is significantly less concerning towards steel and can be effectively prevented under these operating conditions through the developed coatings. On CuCrZr substrates, pure-W coatings demonstrated insufficient corrosion protection and reliability issues, leading to mechanical failure of the barrier. The experiments underlined the importance of substrate preparation and surface defects on corrosion barrier properties. In contrast, W-Al coatings were able to successfully and reliably prevent liquid tin corrosion, indicating that low-crystallinity and amorphous materials are more suitable as corrosion barriers for liquid tin divertor applications.

Plasma disruption prediction is essential for sustaining stable nuclear fusion reactions. Existing data-driven approaches face limitations due to their dependence on labeled datasets, which are often difficult to curate in dynamic plasma environments. Also, these models typically rely on setting a fixed threshold—a manually defined cutoff point to detect fluctuations in plasma current that may indicate an impending disruption. This threshold is manually defined and remains constant, which can make it ineffective under evolving plasma conditions, where the nature of fluctuations may change over time. To address the limitations, this study proposes an unsupervised Gated Recurrent Neural Network model with a Dynamic Threshold-based Temporal Differentiation Algorithm (GRNN-DTTD) to predict disruptions. This threshold is formed by continuously analyzing temporal variations in plasma current fluctuations, allowing it to adjust based on evolving signal patterns. This adaptive mechanism enables the GRNN-DTTD to detect abnormal trends associated with impending disruptions without the need for pre-labeled training data. By learning directly from variations in the input signals over time, the model operates in an unsupervised manner, which identifies disruptive patterns and issues early warnings. Experimental evaluation was conducted on Aditya dataset (133 training shots, 91 unseen testing shots) which demonstrates the model’s effectiveness by achieving 98.9% prediction accuracy with warning times of 12–30 ms prior to disruption events. The results show that the proposed framework avoids manual threshold setting, eliminates dependency on labeled data, and improves adaptability to changing plasma conditions.