Neutron flux flattening in PWRs using neural networks in fuel management

The Hopfield network is studied from the standpoint of taking it as a computational model in optimizing the fuel management of pressurized water reactors (PWRs). In this paper, the flattening of the neutron flux is considered as the objective function. By this consideration, the power peaking inside the reactor core is also minimized. Regarding the local minimum problem of Hopfield network, the simulated annealing method is applied to improve the Hopfield solution. The method is applied to Bushehr Nuclear Power Plant (PWR design) and the result is compared with the core configuration purposed by the designer.