Classification Methodologies for Nuclear Spent Fuel Forensic

2017 ◽  
pp. 711-721
Author(s):  
Sanghwa Lee ◽  
Seungmin Lee ◽  
Gyunyoung Heo
Keyword(s):  
Spent Fuel

DUPIC (PWR Spent Fuel to CANDU) and AECL Activity

2006 ◽  
Vol 48 (2) ◽  
pp. 108-112
Author(s):  
Osamu AMANO ◽  
Junichi YAMASHITA
Keyword(s):  
Spent Fuel

THERMAL HYDRAULIC ANALYSIS OF SPENT FUEL SUBASSEMBLY DURING TRANSPORTATION FROM FBTR TO REPROCESSING FACILITY

Equipment ◽  
2006 ◽  
Author(s):  
D. Sujish ◽  
C. Meikandamurthy ◽  
T. R. Ellappan ◽  
M. Rajan ◽  
G. Vaidyanathan
Keyword(s):  
Spent Fuel ◽  
Hydraulic Analysis ◽  
Thermal Hydraulic Analysis

Management of Nuclear Waste and Spent Fuel

1982 ◽  
Author(s):  
E. DRAPER ◽  
GEORGE COULBOURN
Keyword(s):  
Nuclear Waste ◽  
Spent Fuel

Evaluation of spent fuel transport cask from the radiological point of view

Kerntechnik ◽  
2019 ◽  
Vol 84 (2) ◽  
pp. 131-134
Author(s):  
A. Abdelhady
Keyword(s):  
Point Of View ◽  
Spent Fuel

Sensitivity of the neutron multiplication factor to water ingress into a spent fuel cask

Kerntechnik ◽  
2020 ◽  
Vol 85 (1) ◽  
pp. 38-53
Author(s):  
M. J. Leotlela ◽  
I. Petr ◽  
A. Mathye
Keyword(s):  
Multiplication Factor ◽  
Spent Fuel ◽  
Water Ingress ◽  
Neutron Multiplication

Value/impact analyses of accident preventive and mitigative options for spent fuel pools

1989 ◽  
Author(s):  
J. H. Jo ◽  
P. F. Rose ◽  
S. D. Unwin ◽  
V. L. Sailor ◽  
K. R. Perkins ◽  
...  
Keyword(s):  
Spent Fuel

scholarly journals SiLiF Neutron Counters to Monitor Nuclear Materials in the MICADO Project

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2630
Author(s):  
Luigi Cosentino ◽  
Quentin Ducasse ◽  
Martina Giuffrida ◽  
Sergio Lo Meo ◽  
Fabio Longhitano ◽  
...  
Keyword(s):  
Radioactive Waste ◽  
Detector Response ◽  
Nuclear Materials ◽  
Spent Fuel ◽  
Neutron Detectors ◽  
Intermediate Level Radioactive Waste ◽  
Eu Project ◽  
Interim Storage ◽  
Waste Drums

In the framework of the MICADO (Measurement and Instrumentation for Cleaning And Decommissioning Operations) European Union (EU) project, aimed at the full digitization of low- and intermediate-level radioactive waste management, a set of 32 solid state thermal neutron detectors named SiLiF has been built and characterized. MICADO encompasses a complete active and passive characterization of the radwaste drums with neutrons and gamma rays, followed by a longer-term monitoring phase. The SiLiF detectors are suitable for the monitoring of nuclear materials and can be used around radioactive waste drums possibly containing small quantities of actinides, as well as around spent fuel casks in interim storage or during transportation. Suitable polyethylene moderators can be exploited to better shape the detector response to the expected neutron spectrum, according to Monte Carlo simulations that were performed. These detectors were extensively tested with an AmBe neutron source, and the results show a quite uniform and reproducible behavior.

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%.

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