Multiobjective fuel management optimization for self-fuel-providing LMFBR using genetic algorithms

One of the conceptual options under consideration for the future of nuclear power is the long-term development without fuel reprocessing. This concept is based on a reactor that requires no plutonium reprocessing for itself, and provides high efficiency of natural uranium utilization, so called Self-Fuel-Providing LMFBR (SFPR). Several design considerations were previously given to this reactor type which, however, suffer from some problems connected with insufficient power flattening, large reactivity swings during burnup cycles, and peak fuel burnup being significantly higher than recent technology experience, which is about 18% for U-10 wt%Zr metallic fuel to be considered. Yet, the mentioned core parameters demonstrate high sensitivity to the fuel management strategy selected for the reactor. Therefore, the aim of this study is to develop a practical tool for the improvement of the core characteristics by fuel management optimization, which is based on advanced optimization techniques such as Genetic Algorithms (GA). The calculation results obtained by a simplified reactor model can serve as estimates of achievable values for mentioned core parameters, which are necessary to make decisions at the preliminary optimization stage.