LONSA as a tool for loading pattern optimization for VVER-1000 using synergy of a neural network and simulated annealing

This paper presents a new method for loading pattern optimization in VVER-1000 reactor core. Because of the immensity of search space in fuel management optimization problems, finding the optimum solution requires a huge amount of calculations in the classical method, while neural network models, with massively parallel structures, accompanied by simulated annealing method are powerful enough to find the best solution in a reasonable time. Hopfield neural network operates as a local minimum searching algorithm; and for improving the obtained result from neural network, simulated annealing is used. Simulated annealing, because of its stochastic nature, can provide for the escape of the result of Hopfield neural network from a local minimum and guide it to the global minimum. In this study, minimization of radial power peaking factor inside the reactor core of Bushehr NPP is considered as the objective. The result is the optimum configuration, which is in agreement with the pattern proposed by the designer.