Nuclear data sensitivity and uncertainty quantification for MYRRHA

This project operates within the sector of nuclear data uncertainty reduction, with an emphasis on creating a tool that can conduct comprehensive, simple and versatile Target Accuracy Requirements (TAR) analyses. To this end, the project centres around the development and verification of a Python-driven tool tailored for these intricate analyses, showcasting its flexibility and efficiency. The advantages of this tool lie in its adaptability and flexibility, demonstrated through the application of the TAR tool to two distinct reactor designs, the Multi-purpose hYbrid Research Reactor for High-tech Applications (MYRRHA) and the Advanced Lead Fast Reactor European Demonstrator (ALFRED); the utilisation of different sets of constraints (that can include the correlations or the residual uncertainty), and the variation of weights for the minimisation function. Furthermore, the tool includes the capacity of integrating multiple, simultaneous constraints into a single TAR analysis. All these features have been tested throughout the thesis and its results verify the tool’s effectiveness and flexibility in reducing nuclear data uncertainties. Additionally, the project includes the development of a homogenised core for MYRRHA at the Beginning Of Cycle (BOC) using the Serpent 2 Monte Carlo code. In conclusion, this work introduces a versatile tool that significantly contributes to the reduction of nuclear data uncertainties in a user-friendly and efficiently manner and that has been tested with positive results.