PWR power distribution flattening using Quantum Particle Swarm intelligence

Abstract

In-core fuel management optimization (ICFMO) is one of the most challenging concepts of nuclear engineering. Most of the strategies implemented for optimizing fuel loading pattern in nuclear power reactors are based on maximizing core multiplication factor in order to extract maximum energy and reducing power peaking factor from a predetermined value to maintain fuel integrity. In this investigation a new method using Quantum Particle Swarm Optimization (QPSO) algorithm has been developed in order to flatten power density distribution in WWER-1000 Bushehr Nuclear Power Plant (BNPP) and thereby provide a better safety margin. The result and convergence of this method show that QPSO performs very well and is comparable to PSO. Furthermore, an operator has been added to QPSO as a mutation operator. This algorithm, called QPSO-DM, shows a better performance on ICFMO than PSO and QPSO. MATLAB software was used to map PSO, QPSO and QPSO-DM for loading pattern optimization. Multi-group constants generated by WIMS for different fuel configurations were fed into CITATION to obtain the power density distribution.