In this investigation, a novel glass system was fabricated via melt quenching. Effect of adding TeO2 on the composition (40-x)B2O3 + (15 + x + y)TeO2 + 25ZnO + (20-y)CaO was stud-ied, where x = 0, 2, 4, 6 mol % and y = 0, 3, 6, and 9 mol % in order to give four fabricated glasses coded by BTZC-1, BTZC-2, BTZC-3, andBTZC-4, respectively. The densities were measured by Archimedes' principle and were 3700, 3860, 4015, and 4166 gcm-3, respec-tively, due to the increasing additives of TeO2. The attenuation properties of the synthesized glass were determined experimentally and theoretically at four energies 0.060, 0.662, 1.173, and 1.333 MeV. A germanium detector was used as a spectrometer, and theoretically the Phy-X software was used, with the findings in agreement between the two techniques. For the BTZC-1 glass at 0.059 MeV, the linear attenuation coefficient was 7.512 cm-1 using the ex-perimental approach and 7.975 cm-1 via Phy-X. The maximum linear attenuation coefficient values were 7.975, 9.826, 11.622, and 13.368 cm-1, respectively for BTZC-1, BTZC-2, BTZC-3 and BTZC-4 at 0.060 MeV, with minimum values of 0.194, 0.201, 0.207, and 0.214 cm-1 at 1.333 MeV.
Ac ti va tion anal y sis and dose as sess ment of re ac tor com po nents are pre req ui sites for de com mis sion ing, cost es ti ma tion, and ra di a tion pro tec tion. At pres ent, im prov ing the ac cu racy of nu clear safety anal y sis re mains the key area of re search. To this end, based on the cou pling method of Monte Carlo code and the fuel de ple tion code, the his tor i cal nu clide de ple tion and ra dio ac tiv ity of de tec tors in spent fuel as sem blies, are cal cu lated and an a lyzed, the law of typ i cal nu clide ac tiv ity with burnup depth is stud ied, and the dose rate dis tri bu tion af ter the cool ing of spent fuel as sem blies, is eval u ated. The re sults of the cal cu la tions are in good agree ment with the ac tual mea sure ments, and the er ror is within an ac cept able range. Cru cially, it was found that af ter a cool ing pe riod of 3 years, with out ap ply ing shield ing mea sures, the dose rate at a dis tance of 30 cm from the sur face drops to be low 1 mSvh-1. On this ba sis, the shield ing de sign scheme of re ac tor de tec tors is pro posed, which pro vides a ref er ence for the re search on the ra di a tion shield ing of a de tec tor in the spent fuel as sem blies.
This study employs computational fluid dynamics methods to analyze bag filters with three structural configurations: non-guided, flat flow-guided, and curved flow-guided systems. The investigation specifically focuses on the influence of these configurations on key parameters, including internal flow field distribution patterns and filter bag load distribution. The evaluation is conducted using indicators such as flow amplitude and the speed variation coefficient. The results indicate that the flow amplitudes for the three structures are 0.41, 0.13, and 0.10, respectively, with notable differences observed in both the flow distribution coefficient and speed variation coefficient. Analysis of flow field uniformity of and particles motion demonstrates that the curved flow guide effectively improves flow field uniformity, providing improved speed distribution and flow allocation at all section heights.
Loss of regulatory control over radioactive sources pose significant threats to public health and environmental safety. This paper proposes a deep-learning-based method for detecting multiple similar radioactive sources. We construct multiple single-source datasets and one multi-source dataset through Geant4. The single-source datasets vary in radiation-pixel-map resolution and in detection-window settings, while the multi-source dataset contains up to three unknown sources. We convert 1-D count-rate data into 2-D image features to generate radiation pixel maps, and we define detection windows to reduce the search space. The YOLOv8n is then employed to detect and locate radioactive sources within these images. Through comparative experiments, we determine the optimal settings for the radiation pixel map and detection windows, achieving an accuracy of over 95 % in detecting multiple un- known radioactive sources. The results demonstrate that the trained deep learning model is able to accurately and effectively detect radioactive sources.
Silicon drift detectors are the core sensors for next-generation space-based X-ray detection, where precise efficiency calibration is crucial for both astronomical observation and radiation monitoring. In this study, calibration was performed using a ground-based monochromatic X-ray calibration facility that employs Bragg diffraction to establish the energy linearity and detection efficiency of an silicon drift detectors detector. A linear relationship between incident energy and channel number was observed, with a correlation coefficient of R² = 0.9987. Monte Carlo simulations were used to model the physical structure of the detector and to calculate its detection efficiency curve across the 2-40 keV range. Comparison with experimental measurements revealed the minimum deviation of 0.53 % at 10 keV and the maximum deviation of 4.77 % at 14 keV. In the 7-32 keV range, measured and simulated detection efficiencies closely matched, with the detector achieving a peak detection efficiency of 94.5 % at approximately 9 keV. The energy resolution of the detector was also calibrated, and a nonlinear curve fitting relationship between intrinsic energy resolution and energy was established, highlighting the variation in monochromaticity with energy. These results validate the feasibility and superiority of using continuously tunable mono-energetic X-rays for silicon drift detectors calibration. This approach lays the groundwork for future space astronomy missions and provides traceable physical references for in-orbit data.
