Identification of diversions in spent PWR fuel assemblies by PDET signatures using Artificial Neural Networks (ANNs)
Journal article, 2023
Radiation detection
Artificial Neural Networks
Spent nuclear fuel
Machine learning
Nuclear safeguards
Author
Moad al-Dbissi
Belgian Nuclear Research Center (SCK CEN)
Chalmers, Physics, Subatomic, High Energy and Plasma Physics
Riccardo Rossa
Belgian Nuclear Research Center (SCK CEN)
Alessandro Borella
Belgian Nuclear Research Center (SCK CEN)
Imre Pazsit
Chalmers, Physics, Subatomic, High Energy and Plasma Physics
Paolo Vinai
Chalmers, Physics, Subatomic, High Energy and Plasma Physics
Annals of Nuclear Energy
0306-4549 (ISSN) 1873-2100 (eISSN)
Vol. 193 110005A new approach to partial defect testing of spent nuclear fuel for safeguards applications
The Swedish Radiation Safety authority (SSM) (SSM2021-709), 2021-07-01 -- 2022-05-31.
Driving Forces
Sustainable development
Subject Categories
Subatomic Physics
Control Engineering
Areas of Advance
Energy
DOI
10.1016/j.anucene.2023.110005