Subatomic, High Energy and Plasma Physics seminar series: Moad al-Dbissi

Abstract

One of the main tasks in nuclear safeguards is the inspection of spent nuclear fuel (SNF) assemblies to detect possible diversions of special nuclear material such as 235U and 239Pu. In the inspection, measurements of relevant observable quantities are acquired from the assembly, e.g., neutrons emitted by the spent fuel, and used to verify whether they are consistent with the declared configuration of the assembly or not. The procedure requires a physical model that can estimate the response of the detectors for a given arrangement of fuel pins in the assembly, and an unfolding technique, based on the physical model, that can be applied to retrieve, from the detector responses, the parameters of the system configuration. In this work, the use of neutron flux and its gradient for the identification and characterization of diversions in a SNF assembly is investigated. The unfolding procedure relies on an artificial neural network (ANN), which has the advantage of generalizing in an efficient manner the mapping of the input (in this case, the measurements from the SNF assembly) to the output (i.e., the fuel pins that are intact or replaced with dummy pins in the assembly). The training and the testing of the ANN make use of a dataset generated using Monte Carlo simulations of a typical nuclear fuel assembly with different patterns of missing fuel pins. The dataset is built of unique scenarios so that the ANN can be tested and assessed over scenarios that are not part of the learning phase.