The present work investigates an appropriate way to solve the problem of optimizing fuel management in a VVER/1000 reactor. To automate this procedure, a computer program has been developed. This program suggests an optimal core configuration which is determined according to established safety constraints. The suggested solution is based on the use of coupled programs, one of which is the nuclear code, for making a database and modeling the core, and another one is the Hopfield neural network. In addition to we applied axial variations of enrichment in fuel rods to flat the flux core as novel role. This computational procedure consists of three main steps. The first one consists of creating the cross section database and calculating neutronic parameters by using WIMSD4 and CITATION codes. The second one consists of finding the best axial variations distributions of enrichment to create a fuel rod pattern by using Hopfield neural network artificial (HNNA) and the cross section database. The third one consists of loading of the fuel rods by the suggested fuel rod patterns and finding the optimum core configuration by HNNA that based on minimizing power peaking factor (PPF, PPF=maximum power/average power) and maximizing the effective multiplication factor (k eff, the ratio of the number of neutrons in two successive fission generations). The procedure uses the optimized parameters in order to find configurations in which k eff is maximized. The penalty function is applied to limit the value of local PPF in the neighborhood fuel assemblies. Therefore, in this paper, we proposed a new approach for the use of Hopfield neural network to guide the heuristic search, and applied axial variations distributions of enrichment as novel method to flat the neutron flux and for evaluating the obtained results pertaining to the first core. The results show that applying the HNNA led us to the appropriate PPF and k eff. Also, applying HNNA and axial variation of enrichment is promising to reach the flattening neutronic flux and guaranteeing safety condition in the reactor core. Therefore, we achieved to a set of two basic parameters PPF and k eff as effective factors on satisfying the safety constraints of VVER/1000 reactor core. 
Using Hopfield neural network to optimize fuel rod loading patterns in VVER/1000 reactor by applying axial variation of enrichment distribution