Automated software analysis of nuclear core discharge data

Monitoring the fueling process of an on-load nuclear reactor is a full-time job for nuclear safeguarding agencies. Nuclear core discharge monitors (CDMS) can provide continuous, unattended recording of the reactor's fueling activity for later, qualitative review by a safeguards inspector. A quantitative analysis of this collected data could prove to be a great asset to inspectors because more information can be extracted from the data and the analysis time can be reduced considerably. This paper presents a prototype for an automated software analysis system capable of identifying when fuel bundle pushes occurred and monitoring the power level of the reactor. Neural network models were developed for calculating the region on the reactor face from which the fuel was discharged and predicting the burnup. These models were created and tested using actual data collected from a CDM system at an on-load reactor facility. Collectively, these automated quantitative analysis programs could help safeguarding agencies to gain a better perspective on the complete picture of the fueling activity of an on-load nuclear reactor. This type of system can provide a cost-effective solution for automated monitoring of on-load reactors significantly reducing time and effort