Characterization of MOX and gadolinia-doped Spent Nuclear Fuel samples from SFCOMPO database

The Belgian Royal Decree of 22nd October 2022 designates deep geological disposal (DGD) as Belgium’s reference strategy for the long-term management of high-level and long-lived radioactive waste. This fact motivates a project of ONDRAF with the collaboration of SCK CEN to develop technical solutions for the challenges that developing a DGD poses. Within the framework of this research, the objective of this thesis is related to the development of reliable methods to predict the isotopic evolution Mixed Oxides and gadolinia doped fuels as a preliminary step toward elaborating the DGD’s technical specifications and the corresponding waste-storage and management strategy. To achieve this goal, the methodology includes the collection of the irradiation histories of different samples found in the SFCOMPO database (Nuclear Energy Agency, 2017), in addition to the geometrical specifications, the initial fuel composition and the compositions of the fuel after the irradiation from the corresponding reports of the laboratories that carried the analysis of these samples. This information was used to build an input for the Serpent Monte Carlo code to carry the burnup calculations needed to predict the final composition of the fuel. Three different fuel assemblies (from Beznau I, Dodewaard I and Takahama III reactors) were modeled in a 2-Dimensional geometry with reflective boundaries, and different assumptions were taken for each case such as averaging boron dilution or steam quality per cycle. The analysis indicates a mean absolute deviation of 7 % for uranium- and plutonium-isotope inventories, with a slight overall over-prediction. Fission products show a much larger mean absolute deviation of 23 % and are strongly over-predicted, whereas minor actinides display a mean absolute deviation of 21 % and tend to be slightly under-predicted. From the above mentioned, Serpent2 code was validated as a software to predict spent fuel composition for these type of fuel, and particular biases of the model were identified laying a foundation for MOX and Gd-doped spent fuel characterization.