scholarly journals Spent Fuel Assembly Modeling for Unattended Gamma Emission Tomography (UGET) Project

2013 ◽  
Author(s):  
Holly R. Trellue
Keyword(s):  
Fuel Assembly ◽  
Emission Tomography ◽  
Spent Fuel ◽  
Gamma Emission ◽  
Assembly Modeling

scholarly journals Quantitative imaging and automated fuel pin identification for passive gamma emission tomography

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ming Fang ◽  
Yoann Altmann ◽  
Daniele Della Latta ◽  
Massimiliano Salvatori ◽  
Angela Di Fulvio
Keyword(s):  
Inverse Problem ◽  
Image Reconstruction ◽  
Image Resolution ◽  
Emission Tomography ◽  
Nuclear Safeguards ◽  
Activity Levels ◽  
Spent Fuel ◽  
Gamma Emission ◽  
Cross Sectional ◽  
Fuel Rods

AbstractCompliance of member States to the Treaty on the Non-Proliferation of Nuclear Weapons is monitored through nuclear safeguards. The Passive Gamma Emission Tomography (PGET) system is a novel instrument developed within the framework of the International Atomic Energy Agency (IAEA) project JNT 1510, which included the European Commission, Finland, Hungary and Sweden. The PGET is used for the verification of spent nuclear fuel stored in water pools. Advanced image reconstruction techniques are crucial for obtaining high-quality cross-sectional images of the spent-fuel bundle to allow inspectors of the IAEA to monitor nuclear material and promptly identify its diversion. In this work, we have developed a software suite to accurately reconstruct the spent-fuel cross sectional image, automatically identify present fuel rods, and estimate their activity. Unique image reconstruction challenges are posed by the measurement of spent fuel, due to its high activity and the self-attenuation. While the former is mitigated by detector physical collimation, we implemented a linear forward model to model the detector responses to the fuel rods inside the PGET, to account for the latter. The image reconstruction is performed by solving a regularized linear inverse problem using the fast-iterative shrinkage-thresholding algorithm. We have also implemented the traditional filtered back projection (FBP) method based on the inverse Radon transform for comparison and applied both methods to reconstruct images of simulated mockup fuel assemblies. Higher image resolution and fewer reconstruction artifacts were obtained with the inverse-problem approach, with the mean-square-error reduced by 50%, and the structural-similarity improved by 200%. We then used a convolutional neural network (CNN) to automatically identify the bundle type and extract the pin locations from the images; the estimated activity levels finally being compared with the ground truth. The proposed computational methods accurately estimated the activity levels of the present pins, with an associated uncertainty of approximately 5%.

Preliminary Design of Unloading Device for MNSR LEU Conversion

2018 ◽  
Author(s):  
Hao Qian ◽  
Li Yiguo ◽  
Peng Dan ◽  
Wu Xiaobo ◽  
Lu Jin ◽  
...  
Keyword(s):  
Fuel Assembly ◽  
Reactor Vessel ◽  
Cover Plate ◽  
Spent Fuel ◽  
Long Distance ◽  
Control Rod ◽  
Small Cover ◽  
Sealing Performance ◽  
Safety And Reliability ◽  
Application Requirements

In order to solve the problem that the current unloading operation will destroy the sealing performance of Miniature Neutron Source Reactor (MNSR) reactor vessel and the tightness can’t be restored, and to meet the application requirements that the original reactor vessel will be reloaded and operated after MNSR LEU conversion, the new unloading device is designed, which can be used without separation of reactor vessel. There has only one fuel assembly in MNSR. When the fuel assembly are unload for MNSR LEU conversion, the cover plate of the pool is removed, the cadmium string is put in, and the neutron detector is placed at first. After removing the drive mechanism and the control rod, and opening the small cover plate at the top of reactor vessel, the fuel assembly can be grabbed and unloaded by unloading tool only through the opening of the small top cover plate. The MNSR spent fuel has very high radioactivity. The auxiliary mechanical device can be used with unloading tools to realize operation in a long distance by lifting and level motion, which is convenient to shield and can reduce the works’ irradiation dose level effectively. Through calculation and analysis, the results show that the structure strength of unloading device is much larger than the actual load to ensure operation safety and reliability. The unloading device is easy to process and operate, and can be used in the practical operation of MNSR LEU conversion or decommissioning at home and abroad to simplify the operation steps and improve the working efficiency.

scholarly journals Effect of Gamma-Ray Energy on Image Quality in Passive Gamma Emission Tomography of Spent Nuclear Fuel

2019 ◽  
Vol 66 (1) ◽  
pp. 487-496
Author(s):  
Camille Belanger-Champagne ◽  
Pauli Peura ◽  
Paula Eerola ◽  
Tapani Honkamaa ◽  
Timothy White ◽  
...  
Keyword(s):  
Image Quality ◽  
Nuclear Fuel ◽  
Spent Nuclear Fuel ◽  
Gamma Ray ◽  
Emission Tomography ◽  
Gamma Emission

scholarly journals Bayesian Activity Estimation and Uncertainty Quantification of Spent Nuclear Fuel Using Passive Gamma Emission Tomography

2021 ◽  
Vol 7 (10) ◽  
pp. 212
Author(s):  
Ahmed Karam Eldaly ◽  
Ming Fang ◽  
Angela Di Fulvio ◽  
Stephen McLaughlin ◽  
Mike E. Davies ◽  
...  
Keyword(s):  
Nuclear Fuel ◽  
Linear Models ◽  
Spent Nuclear Fuel ◽  
Emission Tomography ◽  
Superior Performance ◽  
Model Parameters ◽  
Gamma Emission ◽  
Unknown Parameters ◽  
Noise Models ◽  
Activity Estimation

In this paper, we address the problem of activity estimation in passive gamma emission tomography (PGET) of spent nuclear fuel. Two different noise models are considered and compared, namely, the isotropic Gaussian and the Poisson noise models. The problem is formulated within a Bayesian framework as a linear inverse problem and prior distributions are assigned to the unknown model parameters. In particular, a Bernoulli-truncated Gaussian prior model is considered to promote sparse pin configurations. A Markov chain Monte Carlo (MCMC) method, based on a split and augmented Gibbs sampler, is then used to sample the posterior distribution of the unknown parameters. The proposed algorithm is first validated by simulations conducted using synthetic data, generated using the nominal models. We then consider more realistic data simulated using a bespoke simulator, whose forward model is non-linear and not available analytically. In that case, the linear models used are mis-specified and we analyse their robustness for activity estimation. The results demonstrate superior performance of the proposed approach in estimating the pin activities in different assembly patterns, in addition to being able to quantify their uncertainty measures, in comparison with existing methods.

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