Development of a supporting tool for optimal fuel management in research reactors using artificial neural networks

The present work investigates an appropriate way to solve the problem of optimizing fuel management in research reactors. A computer program has been developed, to automate this procedure. The program suggests an optimal core configuration, which satisfies to established safety constraints. The suggested solution was based upon the use of coupled methods in which a stochastic technique, simulated annealing, and an adaptive back-propagation neural network are connected to each other.An objective function was developed based on two performance parameters: cycle length, which can be determined through the evaluation of the effective multiplication factor Keff and power peaking factor Pmax. The system uses optimization of these two parameters to finds configurations in which Keff is maximized, whether a penalty function is applied to limit the value of Pmax which should be lower than the one given in the initial configuration.A series of tests have been performed using a modified core configuration of the benchmark 10 MW IAEA LEU (low enriched uranium) research reactor and the result achieved is the optimum configuration of the studied core.