Enhancing fast neutron irradiation in thermal neutron spectrum reactors through python-based multi-objective optimization

This work reports on a pilot study for optimizing the design of a fast neutron irradiation experiment in a thermal neutron spectrum, specifically the Advanced Test Reactor (ATR). A fast and robust multi-objective optimization workflow that leverages Python-based open-source tools was developed and applied to the ATR to optimize experiment design and boost fast energy neutrons at a desired irradiation location. Three design options were explored to minimize thermal and epithermal neutron flux, deposited heat, and total estimated cost while maximizing the absolute fast neutron flux. This was achieved by considering several irradiation positions in the ATR with different combinations and thicknesses of filter and booster materials. The developed workflow utilizes high-fidelity Monte Carlo calculations to train a surrogate model of each objective function being optimized, thereby reducing computational efforts while searching for the optimized set of solutions. The results show that absolute fast neutron flux increased approximately 30% to 55% in regions with a harder spectrum, while the absolute fast neutron flux increased significantly by 7 to 10 times in regions with a softer spectrum outside the core but still lower than the regions with harder spectrum. Also, The predictions of the surrogate models were verified against the high-fidelity Monte Carlo calculations, and these tests showed that the surrogate models made accurate predictions.