Journal of Fusion Energy最新文献

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.

It has widely been recognized that the particle control and heat removal capabilities of plasma-facing components (PFCs) such as divertor will affect the overall performance of a steady-state magnetic fusion power reactor. The existing divertor technologies developed for ITER with a heating power of ~ 100 MW may not readily allow us to expect the successful operation of DEMO reactors often with a heating power of > 500 WM if the P (heating power)/R (major radius) ratio scaling law by Kotschenreuther is applied, details of which will be described in this paper. Over the past several decades, a variety of innovative PFC concepts have been proposed to resolve these divertor issues. Conducted in the present work are proof-of-principle experiments on one of these innovative PFC concepts, employing a liquid metal as the plasma-facing material with the particular emphasis on the effects of forced liquid convection on heat and particle transport, both observed simultaneously.

Tritium breeding calculation is a crucial aspect of fusion blanket design. Currently, the typical particle transport code MCNP is extensively utilized in tritium breeding research, though the distribution of its new version is strictly restricted. The open-source Monte Carlo particle transport code Geant4 and OpenMC are regarded as possible substitutes, but their accuracy in tritium breeding calculation for fusion blanket has not been verified sufficiently. In this paper, we evaluated the suitability of Geant4 and OpenMC for tritium breeding calculation based on experimental data and the MCNP results, focusing on two types of blanket mock-ups and a novel blanket made of spent nuclear fuel. The results indicate that both Geant4 and OpenMC are suitable for tritium breeding calculations, though the overall deviation in Geant4 is slightly larger. OpenMC has better pertinence and ease of use for neutron transport problems and smaller TBR deviation. However, discrepancies are observed in the calculation of fission nuclide reaction rates for the spent nuclear fuel blanket.

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.