Vol.37,

DAYU3D is a modern three-dimensional (3D) computer code for thermal-hydraulic design and accident analysis in high temperature gas-cooled reactors (HTGRs), developed by the Institute of Nuclear and New Energy Technology (INET) at Tsinghua University. Compared to the traditional codes like TINTE, the DAYU3D code has advantages due to its refined framework, improved models, and more efficient algorithms. It is able to simulate the continuous movement of control rods, and is more rigorous in treating radiation heat transfer and the break mass flow. Advanced computational methods significantly improve the computational efficiency of DAYU3D, achieving a time reduction of over 60% compared to TINTE. Extensive verification and validation with more than 100 cases demonstrate that DAYU3D is promising for HTGR 3D thermal-hydraulic design and accident analyses.

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This study proposes a method for ⁹⁹Mo production via electron accelerator irradiation of a natural–uranium–bearing liquid molten salt target, with advantages including low nuclear proliferation risk, online extraction capability, and low construction costs. The approach primarily produces ⁹⁹Mo through photofission of uranium (~95%), specifically ²³⁸U(γ,f). Secondary neutrons, originating from photonuclear interactions or fission processes, contribute minimally (~5%) to ⁹⁹Mo production owing to their high energies and low fission cross-sections. Key parameter analyses revealed that fluoride salt systems exhibit higher ⁹⁹Mo yield. Their performance stems from high bremsstrahlung energy loss rate and superior photon yield, making them optimal molten salt target materials. To maximize photofission and photoneutron cross-sections while minimizing high-energy gamma ray shielding requirements, an electron beam energy range of 40–80 MeV is recommended. To suppress local hot spots and prevent molten salt boiling, flow conditions were introduced to enhance convective heat transfer, effectively reducing the peak temperature. At a flow velocity of 0.5 m/s and under 80 MeV energy conditions, the maximum system temperature is only 808.9 K, which is significantly lower than the boiling point of 1773 K. Under optimized parameters, the maximum annual production capacity of ⁹⁹Mo reaches 4486.49 Ci, sufficient for millions of diagnostic procedures and equivalent to 16.37% of China’s projected demand for 2030. This method provides a viable pathway for stable, large-scale ⁹⁹Mo production.

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Deep learning methods have achieved significant progress in solving partial differential equations. However, when applied to the widely used anisotropic scattering neutron transport equations in reactor engineering, these encounter significant challenges. To address this issue, this study introduces a multi-antiderivative transformation alternating iterative deep learning method (M-AIM). This method transforms the integral terms of the scattering and fission sources in the transport equation into multiple antiderivative functions corresponding to the integrand, converts the differential–integral form of the transport equation into an exact differential equation, and establishes the necessary constraints for a unique solution. The M-AIM uses multiple deep neural networks to map the unknown angular flux density of transport equations and represents various forms of antiderivative functions. It constructs the corresponding weighted loss functions. By alternating iterative training with deep learning methods applied to these neural networks, the loss is reduced gradually. When the loss decreases to a preset minimum, the neural network approaches a numerical solution for both angular flux density and antiderivative functions. This paper presents a numerical verification of geometries such as flat plates and spheres. It verifies the validity of the theoretical framework and associated methods. The study contributes to the development of novel technical approaches for applying deep learning to solve anisotropic scattering neutron transport equations in reactor engineering.

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N,N,N’,N’-tetraoctyl diglycolamide (TODGA) is a potential extractant for the co-extraction of lanthanides and actinides in high-level liquid waste. In this study, the radiolysis and extraction properties of TODGA in kerosene solvents contacted with the aqueous phase of varying HNO₃ concentrations were systematically investigated, and the complexation mechanism was analyzed in conjunction with density functional theory (DFT) calculations. After γ-irradiation, the variation of TODGA concentration were detected, and the variation trends in the relative content of radiolysis products (RPs) with sample type and absorbed dose were demonstrated. Results indicated that the breaking of the amide bond, ether bond, and C_amide–C_ether bond were the primary radiolysis routes. The aqueous-phase precipitate was studied as a potential new mode of TODGA radiolysis in ultrapure water aqueous phase. Moreover, TODGA/kerosene exhibited excellent extraction capabilities for lanthanides even after absorbing 100 kGy, and HNO₃ can maintain a portion of TODGA’s extraction capacity. The DFT method was applied to calculate and evaluate the complexing ability of TODGA and some of its RPs toward lanthanides. The results revealed that the complexing ability of TODGA for Ce(III), Eu(III), and Dy(III) was enhanced successively, and the complexing ability of the RPs with intact oxygen-containing structures could not be neglected.

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The aerosolization and diffusion of radioactive materials caused by chemical explosions represent a typical nuclear accident scenario that poses severe radioactive hazards to human health and the environment. This study examines the diffusion of plutonium aerosol generated by a chemical explosion within a typical representative underground facility. The state of explosion products following a single-point detonation of explosives was simulated. Subsequently, a numerical simulation of plutonium aerosol diffusion using the Discrete Phase Model (DPM) was conducted based on the outcomes of the chemical explosion simulation. The simulation results indicate that plutonium aerosols diffuse throughout underground facilities after a chemical explosion; small particle size aerosols primarily accumulate in the upper part of the room after the accident; the concentrations of plutonium aerosol in the Room and Tunnel are significantly higher than those in the other areas; and the temporal variations in aerosol concentration in each area were quantified. Based on the particle concentration distribution and the effective dose computation approach, the study computes the internal irradiation dose received by personnel in seven areas over various time periods post-accident. Recommendations for emergency decision-making were derived from these calculations. These findings provide important theoretical insight and practical engineering application value for understanding the diffusion of radioactive aerosol in confined spaces following chemical explosions and for evaluating personnel radiation dose.

Cosmic-ray muon sources exhibit distinct scattering angle distributions when interacting with materials of different atomic numbers (Z values), facilitating the identification of various Z-class materials, particularly radioactive high-Z nuclear elements. Most traditional identification methods are based on complex statistical iterative reconstruction or simple trajectory approximation. Supervised machine learning methods offer some improvement but rely heavily on prior knowledge of the target materials, significantly limiting their practical applicability in detecting concealed materials. To the best of our knowledge, this is the first study to introduce transfer learning into muon tomography. We propose two lightweight neural network models for fine-tuning and adversarial transfer learning, utilizing muon scattering data of bare materials to predict the Z-class of materials coated by typical shieldings (e.g., aluminum or polyethylene), simulating practical scenarios such as cargo inspection and arms control. By introducing a novel inverse cumulative distribution-based sampling method, more accurate scattering angle distributions could be obtained from the data, leading to an improvement of nearly 4% in prediction accuracy compared with the traditional random sampling-based training. When applied to coated materials with limited labeled or even unlabeled muon tomography data, the proposed method achieved an overall prediction accuracy exceeding 96%, with high-Z materials reaching nearly 99%. The simulation results indicate that transfer learning improves the prediction accuracy by approximately 10% compared to direct prediction without transfer. This study demonstrates the effectiveness of transfer learning in overcoming the physical challenges associated with limited labeled/unlabeled data and highlights the promising potential of transfer learning in the field of muon tomography.

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Three-quasiparticle K-isomeric states in odd-mass N=106 isotones within the A~180 mass region were systematically investigated using configuration-constrained potential energy surface calculations. The calculations successfully reproduced the excitation energies and deformations of the known high-K isomers in nuclei from ¹⁷⁵Tm to ¹⁸¹Re. For the nuclei closer to the Z=82 shell closure (¹⁸³Ir, ¹⁸⁵Au, and ¹⁸⁷Tl), predictions of the configurations of the observed and yet-to-be-observed isomers are provided. The results reveal strong shape polarization, where the three-quasiparticle states are driven to larger deformations compared to the often shape-soft or spherical ground states. A particularly rich spectrum of shape coexistence is predicted in ¹⁸⁷Tl, where several high-K three-quasiparticle configurations with distinct prolate, oblate, and triaxial shapes are found to coexist at similar excitation energies. Notably, the oblate-deformed K^π=29/2⁺ configuration at E_x = 1839 keV was proposed to be responsible for a long-lived isomer. This study provides a comprehensive picture of shape evolution and coexistence in high-K multi-quasiparticle states, offering valuable insights for future experimental studies.

Accurate fission cross-section data for actinide nuclides are critical for nuclear energy, astrophysics, and defense applications. Traditional detectors, such as fission chambers, face limitations in achieving sub-3% uncertainty owing to particle identification challenges and dynamic range constraints. The Time Projection Chamber (TPC) can record both the energy deposition dE/dx and the three-dimensional track of an event, providing the ability to identify particles and fission fragments. Based on this characteristic, we developed a novel TPC, INPC-TPC, featuring a symmetrical dual-chamber structure and Gas Electron Multiplier (GEM)-based readout technology. The dual-chamber design isolates fission fragments and recoils protons, thereby reducing the dynamic range requirements of a single chamber, whereas the GEM ensures high spatial resolution and stable gain. Experiments conducted at the Chinese Spallation Neutron Source (CSNS) Back-n white neutron beamline validated the performance of the proposed detector. The INPC-TPC demonstrated effective fission fragment identification through particle energy-length correlation measurements and accurately measured the neutron beam spot size with a diameter relative error of < 2%. The results highlight the capability of the system to achieve high-precision measurements of neutron-induced fission cross-sections, particularly for ²³⁵U and ²³⁸U.

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The neutron-rich nucleus ²²C, located at the neutron drip line, exhibits intriguing structural properties, such as its Borromean nature and potential two-neutron halo configuration. Despite experimental advancements, uncertainties persist in the two-neutron separation energy S_2n and the radius of matter for this attractive nucleus ²²C. In this study, we employed the three-body Faddeev approach to investigate the ground-state properties of ²²C, constrained by the recently deduced matter radius. By optimizing the neutron-core and three-body interactions to reproduce the experimental radius, the two-neutron separation energy S_2n was redetermined, revealing a weakly bound system dominated by the s-wave configuration. Additionally, an excited state exhibiting an Efimov-like pattern was identified by analyzing the specific density distributions and relative distances in the three-body system, highlighting the geometric similarity between the ground and excited states.

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Muon Scattering Tomography (MST) is a powerful noninvasive imaging technique with significant applications in nuclear material detection and security screening. Traditional MST usually relies on the Point of Closest Approach (PoCA) algorithm to reconstruct images from muon scattering data; however, PoCA often suffers from suboptimal image clarity and resolution. To overcome these challenges, we propose a novel approach that leverages Reinforcement Learning (RL) to enhance MST reconstruction, termed the μRL-enhanced method. By framing the MST optimization task as an RL problem, we developed an intelligent agent capable of dynamically adjusting the key PoCA parameters. The agent is trained using a multi-objective reward function that guides the optimization toward higher-quality reconstructions. Our experimental results show that the μRL-enhanced method significantly outperforms the traditional PoCA baseline across multiple benchmark metrics. Specifically, the proposed approach on average attains a 307% improvement in the Intersection over Union (IoU), a 79% increase in the Structural Similarity Index Measure (SSIM), and a 8.4% enhancement in the Peak Signal-to-Noise Ratio (PSNR) across four experiments. Furthermore, when benchmarked against the Maximum Likelihood Scattering and Displacement (MLSD) algorithm, the μRL-enhanced method offers modest gains in PSNR and IoU, together with a one-third increase in SSIM. These improvements demonstrate the enhanced reconstruction accuracy and structural fidelity of the μRL-enhanced method, highlighting its potential to advance MST technologies and their applications.

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Aerial surveys are dynamic and continuous processes, and there are different height distributions of the ground in the measurement area, which leads to problems such as overlapping measurement areas and inaccurate altitude correction during the survey process. Commonly used terrain correction methods are based on the concept of finite elementization of ground surface radioactive sources, using GPS coordinates, radar altitude, and ground elevation distribution information from aerial surveys, combined with the sourceless efficiency calibration method to construct a response matrix, which is then inverted for surface nuclide content. However, most of the sourceless efficiency calibration methods used are numerical calculations that consider the body detector as a point detector and do not consider the changes in intrinsic detection efficiency under different incident directions of gamma rays. Therefore, when the altitude of the measurement area varies significantly or the flight altitude of the aerial survey is relatively low, such sourceless efficiency calibration method calculations tend to have a large bias, which affects the accuracy of the terrain correction. To address the above problems, this study employs a novel sourceless efficiency calibration method based on the Boolean operation of the ray deposition process and simplifies the traditional body source measurement model to a surface source measurement model to achieve fast and accurate efficiency calibration. Then, through the discretization of the measurement process, the static measurement process is superposed as equivalent to the dynamic measurement process, and the dynamic measurement response matrix is built and optimized based on the calibration method. Finally, the PSO-MLEM algorithm was used to solve the dynamic measurement response matrix to achieve dynamic terrain correction of aerial survey data. Analysis of the Baiyun’ebo test area revealed that, after applying dynamic terrain correction, the inverted anomalies in uranium (eU), thorium (eTh), and potassium (K) concentrations were closer to ground measurements (within 5.72%–30.79%) and exhibited clearer anomaly boundaries compared to traditional height-based corrections. However, owing to the inherent statistical fluctuations and characteristics of matrix inversion, higher measurement values tend to absorb lower ones, potentially enlarging the anomalous regions. Nevertheless, the high-anomaly regions after inversion largely coincided with the ground truth validation, demonstrating that the proposed method can effectively correct airborne gamma spectrometry data.

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A compact TM₀₂₀-mode RF cavity was proposed and studied by KEK and RIKEN for the storage ring of the NanoTerasu facility. However, performance limitations due to accelerating mode leakage into the coaxial slots have been identified. This paper presents an improved TM₀₂₀-mode cavity design to solve this issue. By employing an elliptical choke, the leakage power can be significantly reduced. Harmful parasitic modes other than the TM₀₂₀-mode are effectively suppressed using the elliptical choke placed at the magnetic node of the TM₀₂₀-mode. Through optimization, this improved TM₀₂₀-mode RF cavity meets the requirements of the Super Tau-Charm Facility (STCF) collider rings with a beam current of up to 2 A. Detailed mechanical design and thermal analysis confirm the feasibility and stability of the improved cavity.

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Under certain accident conditions in particle accelerators, high-power beam irradiation may damage vacuum pipes, magnets, and other key equipment. Therefore, machine protection for high-power accelerators is critical to ensure safe operation. It is important to study radiation damage to materials to support the design and operation of machine protection systems. In the shock-wave regime, a pronounced hydrodynamic tunneling effect occurs within materials. The traditional one-way coupling simulation method results in substantial errors in this regime. Therefore, a bidirectional iterative coupling simulation method was developed. This method enables the bidirectional coupling of the Monte Carlo code FLUKA and the thermodynamic program Ansys-Autodyn. Density changes are monitored during the simulations and the updated density is promptly fed back to FLUKA. The program remodels the target with the new density distribution to calculate the new energy deposition distribution, which is then returned to Autodyn for subsequent simulations. This iterative process continues until the entire beam has completed the energy deposition process. Compared to existing methods, this automated method significantly improves the efficiency of the coupled simulations and reduces the possibility of human error. The HRMT-12 beam irradiation experiment at CERN was used for a benchmark study, and simulations were conducted and compared using different equations of state. The results demonstrate the efficiency and accuracy of this simulation method. Compared to complex and costly beam irradiation experiments, this approach is expected to provide fast and cost-effective scientific guidance for the machine protection of high-power accelerators. Considering the severe consequences of the hydrodynamic tunneling effect, machine protection components such as beam collimators, absorbers, and dump blocks should adopt low-density materials to reduce the energy deposition density. Beam dilution may be required in beam dumping systems to avoid target damage. This method can be applied to the redundancy design of such beam dumping systems.

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In clinical diagnosis, conventional X-ray absorption-contrast computed tomography (XACT) technology cannot effectively differentiate diseased tissues from the healthy ones. X-ray phase-contrast CT (XPCT) and dual-energy CT (DECT), emerging X-ray imaging technologies with superior diagnostic capabilities, address this issue through different principles. While both XPCT and DECT have advantages and disadvantages in medical applications, their systematic comparison is lacking. Using GEANT4 and MATLAB, in this study, we established an X-ray phase-contrast imaging (XPCI) model based on single-mask and single-shot edge illumination for fast XPCT imaging, comparing it with DECT on soft-tissue phantom. XACT served as a reference for comparison. The study introduces an evaluation system using statistical measures including absolute error, mean absolute error, structure similarity index measure, peak signal-to-noise ratio, and contrast-to-noise ratio. Results show XPCT images are superior to DECT. The XPCI model can be improved on existing medical CT for widespread medical application.

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The quasi-monochromatic, continuously energy-tunable, and high-brightness gamma rays that are produced by an inverse Compton scattering (ICS) light source provide an ideal probe for gamma-ray imaging. However, owing to the influence of the intrinsic energy-angle correlation spectrum of this type of light source, monochromatic computed tomography (CT), especially in the gamma-ray energy region, can only be realized in a low-efficiency manner, similar to first-generation CT. A dual-energy scan scheme with a large imaging field of view (FOV) was developed in this study to improve the imaging efficiency. The effectiveness of this scheme was demonstrated based on the beam parameters of a typical ICS light source using Monte Carlo simulations. By leveraging the principle of basis material decomposition, the influence of the energy-angle correlation spectrum on CT reconstruction was corrected, and a monochromatic CT image of the imaging object was accurately reconstructed. Furthermore, the electron density and effective atomic number of the imaging object could be obtained simultaneously.

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Heavy-ion collisions (HICs) is a unique experimental tool for investigating the properties of nuclear matter under extreme conditions in the laboratory. At HIRFL-CSR energies, HICs can create nuclear matter with 2–3 times the saturation density (ρ₀). The HIRFL-CSR External-target Experiment (CEE) is a large-acceptance spectrometer designed to explore frontier topics in high-energy nuclear physics, such as the QCD phase structure and nuclear matter equation of states. In this letter, we introduce simulation and analysis software for the CEE experiment (CeeROOT). Based on the CEE conceptual design and CeeROOT software, the configurations of its subdetectors were optimized by considering foreseeable physical constraints. The final detector layout of the CEE spectrometer and its acceptances were validated through simulations of U+U collisions at 500 MeV/u and pp collisions at 2.8 GeV, which demonstrated that the CEE experiment will serve as a detector with wide acceptance and multi-particle identification capabilities for studying high-energy nuclear physics topics at HIRFL-CSR energies with pp, pA, and AA collisions.

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An important feature of Quantum Chromodynamics (QCD), is that the strong force grows as the distance between partons increases, which confines partons into hadrons, commonly known as QCD confinement. Perturbative QCD (pQCD) does not work at large distance, such as the length scale of a hadron, which is the regime of nonperturbative QCD. The detailed QCD mechanisms through which confinement occurs from partons to hadrons (usually known as hadronization), and how it manifests itself in partonic structure of hadrons (usually known as parton distribution), remain unresolved puzzles of first-principle QCD calculations.

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A deep neural network (DNN) was developed to accurately predict the nuclear charge density distributions for nuclei with proton numbers Z≥8. By incorporating essential nuclear structure features, the model achieved a significant improvement in predictive accuracy over conventional methods. The charge density distributions were analyzed using a Fourier-Bessel (FB) series expansion, and the DNN was trained on a comprehensive dataset derived from relativistic continuum Hartree-Bogoliubov (RCHB) theory calculations. The model demonstrated exceptional performance, with root-mean-square deviations of 0.0123 fm and 0.0198 fm for the charge radii on the training and validation sets, respectively, which remarkably surpassed the precision of the original RCHB calculations. In addition to advancing nuclear physics research, this high-precision model provides critical data for applications in atomic physics, nuclear astrophysics, and related fields.

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