Developments toward a novel methodology for spent nuclear fuel verification
Licentiatavhandling, 2022
For the first step of the methodology, the concept of a new neutron detector has been studied via Monte Carlo simulations and it relies on the use of optical fiber-mounted neutron scintillators. The outcome of the computational study shows that the selected detector design is a viable option since it has a suitable size to be introduced inside a fuel assembly and can measure neutron flux gradients. Then, experimental work has been carried out to test and characterize two optical fiber-based neutron scintillators that can be used to build the detector, with respect to detection of thermal neutrons and sensitivity to gamma radiation.
For the second step of the methodology, a machine learning algorithm based on ANN is studied. At this initial stage, a simpler problem has been considered, i.e., an ANN has been prepared, trained and tested using a dataset of synthetic neutron flux measurements for the classification of PWR nuclear fuel assemblies according to the total amount of missing fuel, without including neutron flux gradient measurements and without localizing the anomalies. From the comparison with other machine learning methods such as decision trees and k-nearest neighbors, the ANN shows promising performance.
nuclear safeguards
partial defect
neutron scintillator
flux gradient detector
machine learning
spent nuclear fuel
artificial neural networks
Författare
Moad al-Dbissi
Chalmers, Fysik, Subatomär, högenergi- och plasmafysik
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)
Artikel i vetenskaplig tidskrift
A new approach to partial defect testing of spent nuclear fuel for safeguards applications
Strålsäkerhetsmyndigheten (SSM) (SSM2021-709), 2021-07-01 -- 2022-05-31.
Ämneskategorier
Subatomär fysik
Övrig annan teknik
Annan fysik
Infrastruktur
C3SE (Chalmers Centre for Computational Science and Engineering)
CTH-NT - Chalmers University of Technology, Nuclear Engineering: CTH-NT-347
Utgivare
Chalmers
Raven and Fox, Forskarhuset Fysik, Fysikgränd 3 (5th floor)
Opponent: Prof. Stephen Croft, Lancaster University, UK