Nuclear Science and Techniques最新文献

Aluminum is the primary structural material in nuclear engineering, and its cross section induced by 14-MeV neutrons is of great significance. To address the issue of insufficient accuracy for the (^{27}hbox {Al})(n,2n)(^{26}hbox {Al}) reaction cross section, the activation method and accelerator mass spectrometry (AMS) technique were used to determine the (^{27}hbox {Al})(n,2n)(^{26}hbox {Al}) cross section, which could be used as a D-T plasma ion temperature monitor in fusion reactors. At the China Academy of Engineering Physics, neutron activation was performed using a K-400 neutron generator produced by the T(d,n)(^{4}hbox {He}) reaction. The (^{26}hbox {Al}/^{27}hbox {Al}) isotope ratios were measured using the newly installed GYIG 1 MV AMS at the Institute of Geochemistry, Chinese Academy of Sciences. The neutron flux was monitored by measuring the activity of (^{mathrm{92,m}}hbox {Nb}) produced by the (^{93}hbox {Nb})(n,2n)(^{mathrm{92,m}}hbox {Nb}) reaction. The measured results were compared with available data in the experimental nuclear reaction database, and the measured values showed a reasonable degree of consistency with partially available literature data. The newly acquired cross-sectional data at 12 neutron energy points through systematic measurements clarified the divergence, which has two different growth trends from the existing experimental values. The obtained results are also compared with the corresponding evaluated database, and the newly calculated excitation functions with TALYS(-)1.95 and EMPIRE(-)3.2 codes, the agreement with CENDL(-)3.2, TENDL-2021 and EMPIRE(-)3.2 results are generally acceptable. A substantial improvement in the knowledge of the (^{27}hbox {Al})(n,2n)(^{26}hbox {Al}) reaction excitation function was obtained in the present work, which will lay the foundation for the diagnosis of the fusion ion temperature, testing of the nuclear physics model, evaluation of nuclear data, etc.

Neutron-skin thickness is a key parameter for a neutron-rich nucleus; however, it is difficult to determine. In the framework of the Lanzhou Quantum Molecular Dynamics (LQMD) model, a possible probe for the neutron-skin thickness ((delta _text {np})) of neutron-rich (^{48})Ca was studied in the 140A MeV (^{48})Ca + (^{9})Be projectile fragmentation reaction based on the parallel momentum distribution ((p_parallel )) of the residual fragments. A Fermi-type density distribution was employed to initiate the neutron density distributions in the LQMD simulations. A combined Gaussian function with different width parameters for the left side ((Gamma _text {L})) and the right side ((Gamma _text {R})) in the distribution was used to describe the (p_parallel ) of the residual fragments. Taking neutron-rich sulfur isotopes as examples, (Gamma _text {L}) shows a sensitive correlation with (delta _text {np}) of (^{48})Ca, and is proposed as a probe for determining the neutron skin thickness of the projectile nucleus.

The utilization of a proton beam from the China Spallation Neutron Source (CSNS) for producing medical radioisotopes is appealing owing to its high current intensity and high energy. The medical isotope production based on the proton beam at the CSNS is significant for the development of future radiopharmaceuticals, particularly for the α-emitting radiopharmaceuticals. The production yield and activity of typical medical isotopes were estimated using the FLUKA simulation. The results indicate that the 300-MeV proton beam with a power of 100 kW at CSNS-II is highly suitable for proof-of-principle studies of most medical radioisotopes. In particular, this proton beam offers tremendous advantages for the large-scale production of alpha radioisotopes, such as ²²⁵Ac, whose theoretical production yield can reach approximately 57 Ci/week. Based on these results, we provide perspectives on the use of CSNS proton beams to produce radioisotopes for medical applications.

The safety assessment of high-level radioactive waste repositories requires a high predictive accuracy for radionuclide diffusion and a comprehensive understanding of the diffusion mechanism. In this study, a through-diffusion method and six machine-learning methods were employed to investigate the diffusion of ({hbox {ReO}_{4}^{-}}), ({hbox {HCrO}_{4}^{-}}), and ({hbox {I}^{-}}) in saturated compacted bentonite under different salinities and compacted dry densities. The machine-learning models were trained using two datasets. One dataset contained six input features and 293 instances obtained from the diffusion database system of the Japan Atomic Energy Agency (JAEA-DDB) and 15 publications. The other dataset, comprising 15,000 pseudo-instances, was produced using a multi-porosity model and contained eight input features. The results indicate that the former dataset yielded a higher predictive accuracy than the latter. Light gradient-boosting exhibited a higher prediction accuracy ((R^2 = 0.92)) and lower error ((MSE = 0.01)) than the other machine-learning algorithms. In addition, Shapley Additive Explanations, Feature Importance, and Partial Dependence Plot analysis results indicate that the rock capacity factor and compacted dry density had the two most significant effects on predicting the effective diffusion coefficient, thereby offering valuable insights.

The study of nuclide production and its properties in the ({N}=126) neutron-rich region is prevalent in nuclear physics and astrophysics research. The upcoming High-energy FRagment Separator (HFRS) at the High-Intensity heavy-ion Accelerator Facility (HIAF), an in-flight separator at relativistic energies, is characterized by high beam intensity, large ion-optical acceptance, high magnetic rigidity, and high momentum resolution power. This provides an opportunity to study the production and properties of neutron-rich nuclei around ({N}=126). In this paper, an experimental scheme is proposed to produce neutron-rich nuclei around ({N}=126) and simultaneously measure their mass and lifetime based on the HFRS separator; the feasibility of this scheme is evaluated through simulations. The results show that under the high-resolution optical mode, many new neutron-rich nuclei approaching the r-process abundance peak around ({A}=195) can be produced for the first time, and many nuclei with unknown masses and lifetimes can be produced with high statistics. Using the time-of-flight corrected by the measured dispersive position and energy loss information, the cocktails produced from ({}^{208})Pb fragmentation can be unambiguously identified. Moreover, the masses of some neutron-rich nuclei near ({N}=126) can be measured with high precision using the time-of-flight magnetic rigidity technique. This indicates that the HIAF-HFRS facility has the potential for the production and property research of neutron-rich nuclei around ({N}=126), which is of great significance for expanding the chart of nuclides, developing nuclear theories, and understanding the origin of heavy elements in the universe.

Small-break superposed station blackout (SBO) accidents are the basic design accidents of nuclear power plants. Under the condition of a small break in the cold leg, SBO further increases the severity of the accident, and the steam bypass discharging system (GCT) in the second circuit can play an important role in guaranteeing core safety. To explore the influence of the GCT on the thermal–hydraulic characteristics of the primary circuit, RELAP5 software was used to establish a numerical model based on a typical pressurized water reactor nuclear power plant. Five different small breaks in the cold-leg superposed SBO were selected, and the impact of the GCT operation on the transient response characteristics of the primary and secondary circuit systems was analyzed. The results show that the GCT plays an indispensable role in core heat removal during an accident; otherwise, core safety cannot be guaranteed. The GCT was used in conjunction with the primary safety injection system during the placement process. When the break diameter was greater than a certain critical value, the core cooling rate could not be guaranteed to be less than 100 K/h; however, the core remained in a safe state.

Based on the dinuclear system model, the calculated evaporation residue cross sections matched well with the current experimental results. The synthesis of superheavy elements (Z=121) was systematically studied through combinations of stable projectiles with (Z = 21)–30 and targets with half-lives exceeding 50 d. The influence of mass asymmetry and isotopic dependence on the projectile and target nuclei was investigated in detail. The reactions (^{254})Es ((^{46})Ti, 3n) (^{297}121) and (^{252})Es ((^{46})Ti, 3n) (^{295}121) were found to be experimentally feasible for synthesizing superheavy element (Z = 121), with maximal evaporation residue cross sections of 6.619 and 4.123 fb at 219.9 and 223.9 MeV, respectively.

The superconducting magnet system of a fusion reactor plays a vital role in plasma confinement, a process that can be disrupted by various operational factors. A critical parameter for evaluating the temperature margin of superconducting magnets during normal operation is the nuclear heating caused by D–T neutrons. This study investigates the impact of nuclear heating on a superconducting magnet system by employing an improved analysis method that combines neutronics and thermal hydraulics. In the magnet system, toroidal field (TF) magnets are positioned closest to the plasma and bear the highest nuclear-heat load, making them prime candidates for evaluating the influence of nuclear heating on stability. To enhance the modeling accuracy and facilitate design modifications, a parametric TF model that incorporates heterogeneity is established to expedite the optimization design process and enhance the accuracy of the computations. A comparative analysis with a homogeneous TF model reveals that the heterogeneous model improves accuracy by over 12%. Considering factors such as heat load, magnetic-field strength, and cooling conditions, the cooling circuit facing the most severe conditions is selected to calculate the temperature of the superconductor. This selection streamlines the workload associated with thermal-hydraulic analysis. This approach enables a more efficient and precise evaluation of the temperature margin of TF magnets. Moreover, it offers insights that can guide the optimization of both the structure and cooling strategy of superconducting magnet systems.

Reliable calculations of nuclear binding energies are crucial for advancing the research of nuclear physics. Machine learning provides an innovative approach to exploring complex physical problems. In this study, the nuclear binding energies are modeled directly using a machine-learning method called the Gaussian process. First, the binding energies for 2238 nuclei with (Z > 20) and (N > 20) are calculated using the Gaussian process in a physically motivated feature space, yielding an average deviation of 0.046 MeV and a standard deviation of 0.066 MeV. The results show the good learning ability of the Gaussian process in the studies of binding energies. Then, the predictive power of the Gaussian process is studied by calculating the binding energies for 108 nuclei newly included in AME2020. The theoretical results are in good agreement with the experimental data, reflecting the good predictive power of the Gaussian process. Moreover, the (alpha)-decay energies for 1169 nuclei with (50 le Z le 110) are derived from the theoretical binding energies calculated using the Gaussian process. The average deviation and the standard deviation are, respectively, 0.047 MeV and 0.070 MeV. Noticeably, the calculated (alpha)-decay energies for the two new isotopes (^{204})Ac (Huang et al. Phys Lett B 834, 137484 (2022)) and (^{207})Th (Yang et al. Phys Rev C 105, L051302 (2022)) agree well with the latest experimental data. These results demonstrate that the Gaussian process is reliable for the calculations of nuclear binding energies. Finally, the (alpha)-decay properties of some unknown actinide nuclei are predicted using the Gaussian process. The predicted results can be useful guides for future research on binding energies and (alpha)-decay properties.

Cs and I can migrate through fuel-cladding interfaces and accelerate the cladding corrosion process induced by the fuel-cladding chemical interaction. Cr coating has emerged as an important candidate for mitigating this chemical interaction. In this study, first-principles calculations were employed to investigate the diffusion behavior of Cs and I in the Cr bulk and grain boundaries to reveal the microscopic interaction mitigation mechanisms at the fuel-cladding interface. The interaction between these two fission products and the Cr coating were studied systematically, and the Cs and I temperature-dependent diffusion coefficients in Cr were obtained using Bocquet’s oversized solute-atom model and Le Claire’s nine-frequency model, respectively. The results showed that the Cs and I migration barriers were significantly lower than that of Cr, and the Cs and I diffusion coefficients were more than three orders of magnitude larger than the Cr self-diffusion coefficient within the temperature range of Generation-IV fast reactors (below 1000 K), demonstrating the strong penetration ability of Cs and I. Furthermore, Cs and I are more likely to diffuse along the grain boundary because of the generally low migration barriers, indicating that the grain boundary serves as a fast diffusion channel for Cs and I.