Virginie Solans: Analysis of Gamma and Neutron Emission for Predicting Decay Heat in Spent Nuclear Fuel

  • Datum: 27 september 2024, kl. 9.30
  • Plats: Lecture hall Heinz-Otto Kreiss, Ångström laboratory, Lägerhyddsvägen 2, Uppsala
  • Typ: Disputation
  • Respondent: Virginie Solans
  • Opponent: Stefano Caruso
  • Handledare: Henrik Sjöstrand, Sophie Grape, Erik Branger, Anders Sjöland
  • DiVA

Abstract

Geological repositories are the preferred option for many countries for the disposal of spent nuclear fuel assemblies.  The thesis work has been part of the European program EURAD, which aims to contribute to increasing the safety of nuclear waste management. The thesis is focused on the Swedish geological repository system due to the availability of the data. 

In Sweden, the spent nuclear fuel assemblies will be placed in copper canisters and positioned 500 m underground. The filling of the canisters is limited by safety, safeguards and operational constraints. In order to verify some of these limitations, experimental measurements will be performed on every spent nuclear fuel assembly before their encapsulation. An important part of this thesis is the analysis of data from previously performed measurements of spent nuclear fuel assemblies’ neutron and gamma emissions at the Swedish interim storage. The neutron and gamma measurements are complementary and give different information about the spent nuclear fuel. These types of measurements are extremely scarce, and they have previously been realized using an HPGe detector and a prototype DDSI instrument. The analyses performed as part of this thesis quantify the measurement uncertainties, assess the reproducibility of the results, and identify potential signatures that could aid in determining safety parameters. This thesis is particularly focused on one safety parameter, namely the decay heat.  From the signatures obtained from the analyzed experimental measurements, a machine-learning model has been developed to predict the decay heat for the spent nuclear fuel assemblies. The predicted decay heat is compared to results from previously conducted calorimetric measurements, and the developed machine learning model demonstrates a strong predictive capability.  An additional aim of this thesis is to understand the measurable signatures needed to predict the decay heat. Therefore, this thesis also focuses on making recommendations on the type of signatures needed from the experimental measurements, with the ultimate goal of guiding the selection of measurements for spent nuclear fuel assemblies both in Sweden and internationally.

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