Journal of Fusion Energy最新文献

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.

The tritium self-sufficiency concept has been pursued with the development of fusion energy, which requires tritium treatment and recovery. Accordingly, the tritium transport characteristics in a breeder blanket are crucial to predict the tritium inventories and permeation. In the present work, a multiphysics coupling analysis model for a water-cooled ceramic breeder (WCCB) blanket was built, providing a method to conduct a comprehensive assessment of the tritium transport behaviors in the blanket, including the tritium concentration, tritium inventory, and tritium permeation through the structural material to the coolant. Bulk diffusion and surface processing of tritium in the blanket are considered, and the isotope exchange reaction in the purge gas and the effect of the hydrogen content on the tritium transport behavior are also considered. These results indicate that hydrogen plays a significant role in reducing the tritium inventory and permeation.

Studies on energy production based on approaches based on nuclear fusion reactions that do not produce neutron emissions have recently gained momentum. In this study, the interactions between the particles emitted from the neutron-free fusion (or aneutronic fusion) reactions of ³He(d, p)⁴He, ⁶Li(d, α)⁴He, ⁶Li(p, α)³He and ¹¹B(p, 2α)⁴He and Ti-6Al-4V alloy were modeled with SRIM, FLUKA and GEANT4 Monte Carlo simulation codes. Damage and penetrability parameters obtained from the simulations were evaluated. Evaluations were made on the most suitable aneutronic reaction for this alloy. It is thought that important outcomes have been obtained regarding fusion reactor structure and engineering.

To address the thermal management challenges under extreme operational conditions of tokamak toroidal field (TF) magnets, this study systematically compared the cryogenic performance of epoxy-wollastonite composites (EWC) implemented in ITER and Sn₅₅PbAgSb solder (SPAS) applied in EAST for helium cooling channels, based on the Comprehensive Research Facility for Fusion Technology (CRAFT) TF coil casing. Through finite element heat transfer modeling at 4.2 K with heat flux ranging from 1 W/m²to 30 W/m², the results demonstrate that cooling channels fabricated with SPAS solder exhibit a 2.12–5.32% reduction in the average cold-side temperature (T_cs) compared to EWC, with the performance gap narrowing to 0.23% at ultra-low heat flux conditions (1 W/m²). The mechanical testing under 77 K cryogenic conditions demonstrates superior crush resistance in EWC (no defects at 400 kN) compared to solder-based counterparts (crack initiation observed at 200 kN). The findings establish a material selection protocol: SPAS is optimal for high heat flux regions to enhance thermal dissipation, while EWC is preferred in mechanically critical zones to ensure structural integrity. These results offer actionable engineering guidelines, balancing thermal efficiency and mechanical robustness for future fusion reactors.