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Physics-based forward model for near-real-time quantitative imaging of spent nuclear fuel assemblies
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Physics-based forward model for near-real-time quantitative imaging of spent nuclear fuel assemblies
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Journal Article
Physics-based forward model for near-real-time quantitative imaging of spent nuclear fuel assemblies
2025
Overview
The Passive Gamma Emission Tomography (PGET) instrument, authorized by the International Atomic Energy Agency (IAEA) for verification of spent nuclear fuel, aims to reconstruct 2D cross-sectional images of spent fuel assemblies (SFAs), identify missing or present fuel pins, and quantify fuel pin activities. Although the first two objectives are reliably achieved, accurate determination of fuel pin activities remains a challenge due to intense self-shielding and scattering effects. We have developed a linear inverse approach that addresses these effects and demonstrated superior image quality and identification accuracy in simulation studies. This approach frames the image reconstruction process as an inverse problem, relying on a physics-based forward model of the PGET system. We improved our forward model by incorporating collimator septal penetration and detector scattering effects. The enhanced forward model enables near-real-time sinogram simulation and system matrix calculation, which is> 100,000 times faster than 3D Monte Carlo simulations. The model was validated through simulations of VVER-1000 and VVER-440 SFA, and a relative difference of 3.7% in counts was achieved between MCNP and our forward model. Based on this enhanced model, we successfully reconstructed images from the simulated data, identified 100% of the fuel pins, and achieved an average uncertainty of 2.3% in activity quantification. We applied the reconstruction method to measured data of VVER-440 SFAs, successfully imaging all the pins, including the innermost ones, and identifying the water channel within the SFA. The high accuracy and low computational cost of our forward model demonstrate its potential for real-world inspection scenarios and enable future algorithm development.
Publisher
Nature Publishing Group UK,Nature Publishing Group,Nature Portfolio
