Fuel loading pattern optimization of a pressurized water reactor by varying internal weights-based particle swarm optimization

Abstract

Fuel reload pattern optimization is essential for attaining maximum fuel burnup for minimization of generation cost while minimizing power peaking factor (PPF). The aim of this work is to carry out detailed assessment of particle swarm optimization (PSO) in the context of fuel reload pattern search. With astronomically large number of possible loading patterns, the main constraints are limiting local power peaking factor, fixed number of assemblies, fixed fuel enrichment, and burnable poison rods. In this work, initial loading pattern of fixed batches of fuel assemblies is optimized by using particle swarm optimization technique employing novel feature of varying inertial weights with the objective function to obtain both flat power profile and cycle keff > 1. For neutronics calculation, PSU-LEOPARD-generated assembly depletion-dependent group-constant-based ADD files are used. The assembly data description file generated by PSU-LEOPARD is used as input cross-section library to MCRAC code, which computes normalized power profile of all fuel assemblies of PWR nuclear reactor core. The standard PSO with varying inertial weights is then employed to avoid trapping in local minima. A series of experiments have been conducted to obtain near-optimal converged fuel-loading pattern of 300 MWe PWR Chashma reactor. The optimized loading pattern is found in good agreement with results found in literature. Hybrid scheme of PSO with simulated annealing has also been implemented and resulted in faster convergence.