Investigation of a Methodology for the Detection of Diversions in Spent Nuclear Fuel
Doctoral thesis, 2024
flux gradient detector
artificial neural networks
partial defects
neutron scintillator
spent nuclear fuel
nuclear safeguards
machine learning
Author
Moad al-Dbissi
Chalmers, Physics, Subatomic, High Energy and Plasma Physics
Identification of diversions in spent PWR fuel assemblies by PDET signatures using Artificial Neural Networks (ANNs)
Annals of Nuclear Energy,;Vol. 193(2023)
Journal article
Conceptual design and initial evaluation of a neutron flux gradient detector
Nuclear Instruments and Methods in Physics Research, Section A: Accelerators, Spectrometers, Detectors and Associated Equipment,;Vol. 1026(2022)
Journal article
Aldbissi, M., Pázsit, I., Rossa, R., Borella, A., Vinai, P. On the use of neutron flux gradient with ANNs for the detection of diverted spent nuclear fuel.
The majority of nuclear materials placed under safeguards are in the form of Spent Nuclear Fuel (SNF) assemblies that are discharged from nuclear power reactors. SNF contains residual amounts of fissile materials such as Uranium-235 and Plutonium-239 which makes it attractive for proliferation purposes, e.g., the construction of nuclear weapons. Therefore, safeguards inspections are performed regularly to ensure that these materials are not being used for illicit activities.
The PhD research presented in this thesis investigates different aspects of a novel non-destructive methodology that can support more accurate safeguards inspections of SNF assemblies. The first aspect is related to the evaluation of the concept of a new detector and the preparatory experimental work for the construction of a prototype. The detector can measure the neutrons emitted inside SNF assemblies and provide the spatial distribution of the neutron flux and its gradient. The gradient, which has not been considered in safeguards inspections before, contains more and independent information compared to the simple neutron flux.
The second aspect of the methodology concerns the development of machine learning models based on Artificial Neural Networks (ANNs) for processing measurements from SNF assemblies and predicting whether sensitive nuclear material has been removed from the assemblies or not. The models show good performance in classifying SNF assemblies based on the percentage of replaced fuel pins, and in reconstructing diversion patterns. The use of the neutron flux gradient as input future to the ANN models was proved advantageous in detecting replaced fuel pins.
The described methodology has the potential of reducing the amount of expert judgement required for the interpretation of the measurements and providing more detailed estimations, and therefore it can facilitate the safeguards inspection process.
A 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.
Subject Categories
Subatomic Physics
Other Engineering and Technologies not elsewhere specified
Roots
Basic sciences
Infrastructure
C3SE (Chalmers Centre for Computational Science and Engineering)
Driving Forces
Innovation and entrepreneurship
ISBN
978-91-7905-987-3
Doktorsavhandlingar vid Chalmers tekniska högskola. Ny serie: 5453
CTH-NT - Chalmers University of Technology, Nuclear Engineering: 352
Publisher
Chalmers
PJ lecture room, Physics Origo building, Chalmers University of Technology
Opponent: Prof. Anna Erickson, Georgia Institute of Technology, USA