PhD Defense | Moad Al-dbissi | Investigation of a Methodology for the Detection of Diversions in Spent Nuclear Fuel

Investigation of a Methodology for the Detection of Diversions in Spent Nuclear Fuel

One of the main tasks in nuclear safeguards is the inspection of Spent Nuclear Fuel (SNF) to detect possible diversions of their special nuclear material content, e.g., Uranium-235 and Plutonium-239. These inspections verify the declared SNF via passive measurements of characteristic signatures such as the emissions of neutrons and gamma rays. The current PhD research investigates different aspects for the development of a novel non-intrusive methodology that can enhance safeguards inspections of SNF assemblies, and it includes two main parts. In the first part, simulations are performed to evaluate the feasibility of measuring the neutron flux and its gradient inside the empty guide tubes of a SNF assembly with a miniaturized detector made of an array of optical fiber-based neutron scintillators. In addition, experiments are carried out to characterize these types of neutron scintillators. The results of this preparatory work show that neutron flux gradient measurements in SNF assemblies may be a viable option and provide insights for the construction of a prototype of a detector for the purpose. In the second part of the research, the application of machine learning models based on Artificial Neural Networks (ANNs) is studied to process measured SNF signatures and reconstruct the arrangement of the fuel pins in an assembly. The objective of this part is two-fold. On one hand, ANN models are explored for the task of determining possible diversion patterns from SNF signatures collected inside the accessible guide tubes. On the other hand, the advantage of providing the neutron flux gradient as input feature to the algorithm is evaluated. The training and testing of the ANN models are performed with synthetic datasets generated from Monte-Carlo simulations of a typical PWR SNF assembly, considering the intact configuration and different degrees and patterns of diversion. The results show that the models effectively predict diversions and characterize most of them to a good extent. In addition, the use of the neutron flux gradient, which is not analyzed during standard inspections, is proven to be advantageous.

Promotor:

• Imre Pazsit (Chalmers Universtity of Technology)

Co-promotor:

• Paolo Vinai (Chalmers Universtity of Technology)

SCK CEN mentors:

• Alessandro Borella (SCK CEN)
• Riccardo Rossa (SCK CEN)

Click here for a list of obtained PhD degrees.