Rapid remote radiation monitoring is important for identifying contaminated regions while reducing personnel exposure. This study developed an unmanned aerial radiation monitoring system (UARMS) equipped with a coded-aperture gamma camera and evaluated a staged workflow integrating flight-based spatial screening with hovering-based gamma-ray imaging. A controlled outdoor experiment was conducted using 60Co and 137Cs point sources. The flight-based survey identified two candidate source-related regions, and the corresponding region-of-interest spectra showed photopeaks of the two sources. The UARMS and ground-based backpack surveys showed broadly corresponding spatial patterns, although their relative intensities and spatial extents differed. During hovering measurements, gamma-ray images corresponding to the two source directions were reconstructed, and ambient dose equivalent rates were estimated. A comparison of altitude-only, image-derived, and UAV-GNSS-referenced distance corrections showed that the horizontal source offset affected the estimates but did not consistently improve agreement with the theoretical source-only values for both sources. These results demonstrate the field feasibility of sequentially performing spatial screening, radionuclide identification, hotspot localization, and ambient dose equivalent rate estimation using a single coded-aperture gamma camera under controlled point-source conditions.
This study quantitatively evaluates the initial residual water remaining after drainage in a pressurized water reactor (PLUS7) fuel assembly, a critical boundary condition determining the efficiency and success of the vacuum drying process in spent nuclear fuel dry storage. To investigate the hydrodynamic trapping mechanisms, multi-mode drainage tests—including gravity drainage, 2 bar compressed air purge, 2 bar helium gas purge, and a hybrid helium purge with a water pump—were conducted on systematically modularized assembly components (top and bottom nozzles, spacer grids, and active fuel rod unit sections). Results demonstrated that simple gravity drainage would leave a massive 8460.42 ± 20.30 g of water across a full-scale assembly, necessitating active pressurized drainage. The application of a compressed air purge reduced the total residual water to 3957.00 ± 20.30 g, whereas helium gas purging further decreased it to 3550.28 ± 20.30 g due to its superior diffusivity and micro-flow-channel penetrability. Notably, the hybrid method combining a downstream water pump and helium gas recorded the lowest total residual water of 3495.08 ± 20.30 g. Although the pump's hydraulic resistance slightly degraded drainage efficiency in small single components, it significantly extended the flow residence time in the vast active fuel rod sections—which account for over 96% of the trapped water—thereby maximizing the duration of the helium gas's aerodynamic shear force to effectively remove the viscous liquid film. The established quantitative database provides a data-driven operational strategy to minimize residual water, supporting safety evaluations and process reliability for long-term dry storage licensing.
Spiral-ribbed fuel rods provide a larger surface area-to-volume ratio than conventional cylindrical rods and can eliminate spacer grids through self-supporting contact. In this study, an AP1000-based 17×17 spiral-ribbed fuel assembly is modeled using OpenMC and loaded with five fuels: (238U+239Pu)O2+AlSi, (232Th+233U)O2+AlSi, (232Th+235U)O2+AlSi, (235U+238U)O2+AlSi, and U-50Zr. Comparison with a conventional cylindrical assembly shows that the spiral-ribbed geometry reduces k∞ and softens the neutron spectrum. The LMDI-CF method is applied to quantify four-factor contributions. Among the fuels, (232Th+233U)O2+AlSi achieves the highest k∞ of 1.83362 and a lifetime of 780 EFPD, reflecting the favorable neutron economy of the thorium-233U cycle. U-50Zr exhibits the hardest neutron spectrum and the longest lifetime of 1920 EFPD, 146.15% longer than that of (232Th+233U)O2+AlSi. Its discharge burnup is 143,914 MWd/tHM, slightly lower than the 147,153 MWd/tHM of (232Th+233U)O2+AlSi. These results provide quantitative support for fuel selection and neutronic optimization of spacer-grid-free spiral-ribbed assemblies.
Current model validation methods employ standard statistical analysis (to measure statistical differences between data populations) or machine learning (to identify response surface from data) methods to quantitatively measure the difference between simulated and experimental data sets. Such methods do not explicitly encode the causal or mechanistic structure of the processes that generated the data (e.g., boundary conditions, governing physical relationships) into the comparison metric itself. This can generate the erroneous conclusion that, when two data populations are close enough, the models that have generated them are similar. In addition, when simulated and experimental data differ beyond the acceptance criteria, model calibration techniques are used to tweak simulation model parameters to reduce the gap between simulated and experimental data. This might give the false expectation that a simulation model matches reality. This paper presents an alternative approach that moves away from current data-driven methods for validation toward model-driven methods based on causal inference. The presented methods are designed to identify and quantify the causal relationships between data elements rather than looking at their associations. The outcome of such an analysis when applied to each data set (e.g., simulated and experimental data sets) is a structural causal model that analytically represents such causal associations. Validation is then performed by comparing structural causal models generated by simulated and measured data.
Neutron radiographic images are often degraded by insufficient beam intensity, exposure time, scattering, detector noise, and collimator size limitations, resulting in low contrast, complex noise, spatial blur, and white spots. Current studies in neutron radiography often treat image restoration and quality assessment as isolated tasks. To achieve a unified framework, we developed a two-step improvement scheme by utilizing the same spatial–frequency attention module and consistent datasets on both the restoration and assessment networks. For the proprietary dataset construction, an updated simulation method for multiple degradations is employed to mitigate the discrepancy between simulated and real-world images. For the network design, centered on the same spatial-frequency attention module, our approach integrates the U-Net-based restoration network with a no-reference image quality assessment network specifically designed for seed screening tasks. Experimental results show that the proposed restoration-assessment scheme significantly enhances the visual quality of authentic neutron radiographic images, thereby providing a potential image-preprocessing method for the subsequent industrial defect detection task.
