Journal of Fusion Energy最新文献

In order to meet the huge demand for millimetre-sized Li₂TiO₃ ceramic pebbles for future fusion reactors, the aim of this work was to develop a combination of microfluidic and UV curing techniques to greatly improve the preparation efficiency. By employing a cross-junction microfluidic device, large-sized droplets were controllably generated and subsequently subjected to in-situ UV curing, enabling rapid solidification of resin-based ceramic slurries. Systematic investigations revealed critical processing parameters: (1) The rheological behavior of ceramic slurries was governed by solid content and dispersant concentration, directly influencing droplet stability during microfluidic manipulation. (2) UV curing efficacy depended on exposure time(10 ~ 40 s), aging time (30 ~ 120 s) and solid content, and the optimised conditions allow complete cross-linking of 2 mm green pebbles. (3) Post-sintering at an ultra-low heating rate (0.5 °C/min) produced Li₂TiO₃ ceramic pebbles with a relatively dense microstructure and high crush load(42 N).This microfluidic and UV curing strategy demonstrates potential process controllability and scalability.

Plasma disruptions remain a major obstacle to sustained commercial operation of tokamak-based fusion devices. Although machine learning (ML) methods have shown promise for predicting disruptions, their performance and generalizability suffer from a lack of common benchmarks and comprehensive multi-device evaluations. To address this, we present DisruptionBench, a new benchmarking platform designed to standardize how ML-driven disruption prediction systems are trained and evaluated on multi-machine data. DisruptionBench spans three devices - Alcator C-Mod, DIII-D, and EAST - and includes tasks of varying difficulty: zero-shot, few-shot, and many-shot training regimes to assess each model’s ability to transfer learned representations to new or data-limited machines. We evaluate four state-of-the-art ML architectures. Two are re-implementations of notable prior work: a random forest (Cristina Rea in PPCF 60:084008, 2018) and the Hybrid Deep Learner (HDL) (Zhu in NC 61: 026607, 2020). We also propose two new approaches tailored for disruption prediction: a transformer-based model inspired by GPT-2, capable of learning long-range temporal dependencies through self-attention, and a Continuous Convolutional Neural Network (CCNN) that leverages continuous kernels to capture subtle variations in plasma signals. Across the nine benchmarking tasks, the CCNN demonstrates consistently strong performance and achieves the highest overall Area Under the ROC Curve (AUC) in intra-machine tests (up to 0.97 on C-Mod). Nevertheless, the GPT-2-based approach and HDL can outperform CCNN in specific transfer scenarios, particularly when the test machine is underrepresented in training data. We further analyze the significance of memory length in capturing precursor phenomena, providing evidence that longer context windows can boost predictive accuracy.

The study of heat flux and particle transport in the plasma boundary and divertor region is a key issue for the long-term stable operation of the fusion reactor in the future. SOLPS-ITER is one of the most widely used boundary simulation programs, however, its calculation cost is high, and the calculation time is long. To enable the effective and rapid prediction of characteristic quantities in the DSOL region and meet the physical coupling requirements between the boundary and core regions (DSOL region and plasma core), integrated simulation for fast core-edge coupling is necessary. By using the SOLPS-ITER code and combining the parameters of the HL-2A device, the influence of impurity injection on the physical characteristics of the divertor boundary is studied, and the relevant simulation data are obtained. Two reliable prediction models of plasma boundary feature quantities are constructed, which are fully connected neural network model (DSOL-NN) and convolutional neural network model (DSOL-CNN). In order to better meet the needs of fast integrated simulation of plasma core-edge coupling, a multi-input multi-output mode (MIMO) is adopted. The model considers the effects of different impurity species and injection rates on the electron temperature and particle flux density of the divertor target plate. The results show that both models can successfully predict the electron temperature of the divertor target plate, the particle flux density of the target plate and the core-edge Z_eff under different impurity injection rate conditions. In comparison, the convolutional neural network model in the two models shows better prediction performance, with a mean relative error of about 5%, which is less than 10% of the fully connected neural network. A large number of comparative predictions show that the neural network prediction model takes several orders of magnitude less than the SOLPS-ITER simulation time consuming, thus providing a basis for the rapid integrated simulation of core-edge coupling.

Nuclear fusion represents a promising solution to meet the increasing global energy demand. While it offers inherent safety advantages over nuclear fission, significant challenges persist regarding personal safety, reactor integrity, and environmental protection, particularly concerning tritium and neutron activation products. This study employs the Human-Machine-Environment Engineering (HMEE) framework to conduct a comprehensive safety management analysis for the China Fusion Engineering Test Reactor (CFETR). By integrating safety objectives from both a horizontal “whole-system” perspective and a vertical “whole-life” perspective, the management approach evaluates the safety characteristics of human operators, the fusion reactor, and the surrounding environment. It also examines their combined influence on system engineering, ultimately establishing an optimized nuclear safety strategy for CFETR.

The tritium breeding pebble bed is a core component of the fusion blanket, in which the tritium purge gas flows through. Its flow and heat transfer characteristics are crucial for achieving tritium self-sufficiency and ensuring safety operation of blanket. The internal heat source generated by tritium-producing nuclear reactions significantly impacts the flow and heat transfer in the pebble bed. This study investigates this impact in a lithium silicate pebble bed within the China Fusion Engineering Test Reactor, focusing on non-uniformly distributed heat sources. A numerical analysis coupling Discrete Element Method and Computational Fluid Dynamics was used to compare the thermal–hydraulic characteristics (flow field, temperature field, and pressure field) with and without internal heat generation. Results indicate that the variation in average flow velocity along the x-direction correlates with the porosity distribution along the same direction within the pebble bed. Furthermore, the purge gas velocity increases with the addition of internal heat sources due to the temperature rise and consequent density reduction of the heated gas. Besides, internal heat sources intensify local thermal non-equilibrium effects between the gas and solid phases. Finally, the pressure drop increases with internal heating due to the increased viscosity of the tritium purge gas.

Liquid tin constrained in a capillary porous structure could be an alternative plasma-facing component to tungsten for the divertor of a future magnetic confinement fusion reactor. However, due to the hydrogen–tin interaction droplets can be ejected, which is a potential showstopper due to an increased radiation in the plasma core. This has been recently observed in experiments in the ASDEX Upgrade tokamak. In this work, the theory of droplet ejection is reviewed, both theoretically and experimentally and potential solutions are tested in nano-PSI, a low flux unmagnetized plasma device. Droplet ejection was demonstrated via shadowgraphy observations to be driven by bubble formation and bursting followed by jetting. The generality of droplet ejection was verified by exposing liquid lithium, sodium, potassium, gallium, indium, tin, lead, and bismuth to hydrogen plasma in nano-PSI. Furthermore, the influence of the capillary structure was tested, by exposing multiple CPS targets. Ejection of droplets was observed for all post-transition metals and with all targets. Moreover, it was shown that free radicals alone are sufficient for droplet ejection, rather than plasma ions. Further, we predict and observe that the droplet ejection is suppressed by increasing the temperature above a critical value for a given radical flux. Our analysis shows that droplet production is highly challenging to prevent under expected fusion reactor conditions. Since droplet ejection cannot be prevented, the approach of using tin as a liquid metal plasma-facing material requires revision.

Understanding the laminar-turbulence transition mechanism in wall-bounded incompressible magnetohydrodynamic (MHD) flows is particularly important for liquid metal blankets of fusion reactors. However, this physical mechanism is still not thoroughly clear until now, especially there is a lack of quantitative analysis results to indicate where within the channel the transition process is likely to occur first. Moreover, the Hartmann layer thickness-based Reynolds number (R) has been found as a single parameter to control the transition process in MHD flows, but a mathematical explanation about this parameter is still absent. In this work, the turbulence transition phenomenon of the wall-bounded incompressible MHD flow is studied by a method called the energy gradient analysis. It points out that the ratio of the total mechanical energy density gradient in the transverse direction to that in the streamwise direction of the main flow (defined by a dimensionless parameter K) characterizes the development of the disturbance in the flow field. We have found that the distance between the initial turbulence transition position in the Hartmann layer and the Hartmann wall is always 69.31% of the thickness of the Hartmann layer, independent of the value of the Hartmann number (Ha). The effects of the Hartmann number and the wall conductance ratio on the initial turbulence transition position in the side layer are also investigated. At last, the reason why the Hartmann layer thickness-based Reynolds number (R) plays the role as a single control parameter in the transition process of MHD flows is explained mathematically.

Helium-3 is a rare and highly important isotope of helium, with a wide range of applications in various industries, such as energy production, cryogenic systems, and medical research. Helium-3 holds significant potential in the energy sector, in addition to its other uses (e.g., neutron detection, dilution refrigerators, ultralow temperature physics, and aneutronic fusion). As a non-radioactive isotope, it is an ideal fuel for fusion reactors when fused with deuterium, offering the advantage of not producing neutrons, unlike deuterium–tritium fusion, which is more commonly explored today. While still in the experimental stage, the ability to contain such energy in a reactor’s containment chamber could make it a viable energy source. Helium-3 is produced as a byproduct of tritium decay in CANDU reactors’ cover gas. The main goal of this article is to enrich the Helium-3 content in a mixture of ³He and ⁴He, similar to the composition of cover gas, up to 10–15% ³He. The originality and innovative aspect of this article lie in the development and characterization of a helium-3 pre-enrichment technology based on chromatographic columns and gas permeation processes. This, combined with a cryogenic distillation process, will form a comprehensive technology for helium-3 recovery from the cover gas of a CANDU-type nuclear reactor. In this context, we present two methods for helium isotope separation: one based on gas chromatography and the other on cryogenic distillation. The method will be developed and optimized for medium-throughput isotope separation facilities, such as those required for the Cernavoda Nuclear Power Plant. In the first part, we present a method for investigating and evaluating the separation and recovery of helium isotopes using gas chromatography. In the second part of the article, we describe the steps undertaken at the ICSI site regarding the development of a technology for helium-3 recovery from fusion reactor cover gas and tritium storage containers.

Stellarators as compact fusion power sources have incredible potential to help combat climate change. However, the task of making that a reality faces many challenges. This work uses Bayesian optimization, (BO) which is a method that is well suited to black-box optimizations, to address the complicated optimization problem inherent by stellarator design. In particular it focuses on the mechanical optimization necessary to withstand the Lorentz forces generated by the magnetic coils. This work leverages surrogate models that are constructed to integrate as much information as possible from the available data points, significantly reducing the number of required model evaluations. It showcases the efficacy of Bayesian optimization as a versatile tool for enhancing both magneto-static and mechanical properties within stellarator winding packs. Employing a suite of Bayesian optimization algorithms, we iteratively refine 2D and 3D models of solenoid and stellarator configurations, and demonstrate a 15% increase in optimization speed using multi-fidelity Bayesian optimization. For fusion technology to progresses from experimental stages to commercial viability, precise and efficient design methodologies will be essential. By emphasizing its modularity and transferability, our approach lays the foundation for streamlining optimization processes, facilitating the integration of fusion power into a sustainable energy infrastructure.

Capillary porous structure (CPS) based liquid metal divertors are currently being investigated as a possible alternative to the tungsten based solid plasma facing components (PFCs). The ability of CPS based technologies to withstand high heat fluxes (> 20 MW/m²) has been already demonstrated in linear devices as well as tokamaks. One of the key aspects of a liquid metal divertor is the erosion of the liquid metal with the subsequent contamination of the plasma. The liquid can be eroded by physical sputtering, evaporation and thermally enhanced sputtering. The absence of a theoretical model or detailed empirical data of Sn thermally enhanced sputtering prohibits reliable predictions of Sn erosion by fusion plasma. Especially in high density tokamak plasmas, thermally enhanced sputtering appears to be the dominant contributor to total erosion. To empirically evaluate the thermally enhanced sputtering yields an experimental campaign was conducted at the Nano-PSI device (T_e = 0.3–0.8 eV, (Gamma_{i} = 5 times 10^{18} {text{ m}}^{ - 2} ;{text{s}}^{ - 1})) with Sn surfaces exposed to homogeneous plasma of various ion species (Ar, Ne, H, He). The effect of ion impact energy on the sputtering yields was studied as well by biasing of the the liquid surface in range of − 10 to − 80 V. In case of Ar, Ne and He the Sn was exposed as a free-flowing surface and for H it was exposed in a stainless-steel capillary porous structure (CPS) to negate the observed H spitting of the free liquid surface. This work presents the measured thermally enhanced sputtering yields, with focus on the observed phenomena, such as plasma species and impact energy dependency.