A stability assessment of optimum Fuel Reload Patterns for a BWR

In this paper, a dynamic assessment of optimal BWR Fuel Reload Patterns (FRP) is presented. Using two meta-heuristics techniques: Tabu Search (TS) and Artificial Neural Networks (ANN), the FRPs were designed. In order to perform the stability analysis, a point in the operation map, which is under the instability region defined in technical specifications, was used. The CASMO-4/SIMULATE-3/SIMULATE-3K system was used. The obtained results show that the inclusion of Decay Ratio (DR) into the objective function of TS and ANN optimization techniques, allows finding FRPs that fulfill not only with thermal limits, shutdown margin, energy demand but also with stability requirements. It was possible to achieve FRPs with DR 6% lesser than the corresponding comparison value. The integration of the DR parameter into FRP optimization implied that the needed CPU time to fulfill every requirement was increased by 6.6 times, with respect to the classic steady-state optimization.