Prediction of Decay Heat Using Non-Destructive Assay

This research paper introduces a novel approach to predict the decay heat of spent nuclear fuel assemblies (SNFs) using data from non-destructive gamma and neutron measurements, addressing the challenge of ensuring safety in geological repositories. Because calorimetric measurements are time-consuming, it is envisioned that gamma and neutron measurements can be used for decay heat prediction before encapsulation. This paper analyses gamma and neutron data to extract key features, specifically the activities of Cs-137, Eu154, and the total neutron count rate. A Gaussian process model is then employed to estimate SNF decay heat. The methodology involves training a prediction model on a calibrated simulated dataset designed to mimic real experimental conditions closely. The model is then successfully used to predict the decay heat for unseen experimental data. The results highlight the potential of using gamma and neutron measurements for reliable decay heat prediction. It is shown that the magnitude of the relative deviation obtained is 2-4 %. Furthermore, the study explores the impact of removing certain input features or adjusting their uncertainty levels on the decay heat prediction model precision, in particular for the Eu-154 activity and neutron count rate. This comprehensive methodology paves the way for applying these techniques to a larger experimental scale offering a significant advancement in the safety assessment of SNFs prior to encapsulation and long-term storage.