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 longterm development without fuel reprocessing. This concept is based on a reactor that requires no plutonium reprocessing for itself, a nd 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 connec ted with insufficient power flattening, large reactivity swings during burn up cycles, and peak fuel burnup being significantly higher than recent tech nology experience, which is about 18% for U-10 wt%Zr metallic fuel to be co nsidered. Yet, the mentioned core parameters demonstrate high sensitivity t o 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 co re characteristics by fuel management optimization, which is based on advan ced optimization techniques such as Genetic Algorithms (GA). The calculatio n results obtained by a simplified reactor model can serve as estimates of achievable values for mentioned core parameters, which are necessary to mak e decisions at the preliminary optimization stage. (C) 1999 Elsevier Scienc e Ltd. An rights reserved.