SINRD validation experiments at the time-of-flight facility GELINA

Identification of Diversions in Spent Pwr Fuel Assemblies by Pdet Signatures Using Artificial Neural Networks (Anns)

“…The dataset for the study relies on previous work performed at SCK CEN (Rossa et al, 2020(Rossa et al, , 2018. It includes the PDET responses simulated via Monte Carlo N-Particle -MCNP code (Werner, 2018) for 17 × 17 PWR spent nuclear fuel assemblies with and without diversion, see Fig.…”

“…Recent efforts have been conducted to develop methods that can enhance the processing of the measured data and extract more details of the system configuration. For example, machine learning algorithms were used to quantify the percentage of replaced fuel pins in SNF assemblies (Rossa et al, 2020(Rossa et al, , 2018Aldbissi et al, 2022), to predict parameters of SNF assemblies (Mishra et al, 2021), to detect and localize missing radioactive sources within a small grid (Durbin and Lintereur, 2020), to track elemental and isotopic material flows through material balance areas for safeguards (Shoman and Cipiti, 2018). These methods can help to reduce the inspection time and make the identification of diversion patterns more precise, so that the decision process of the inspectors is facilitated.…”