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The present work investigates an appropriate algorithm based on Multilayer Perceptron Neural Network (MPNN), Apriori association rules and Particle Swarm Optimization (PSO) models for predicting two significant core safety parameters; the multiplication factor K eff and the power peaking factor P max of the benchmark 10 MW IAEA LEU research reactor. It provides a comprehensive analytic method for establishing an Artificial Neural Network (ANN) with selforganizing architecture by finding an optimal number of hidden layers and their neurons, a less number of effective features of data set and the most appropriate topology for internal connections. The performance of the proposed algorithm is evaluated using the 2-Dimensional neutronic diffusion code MUDICO-2D to obtain the data required for the training of the neural networks. Simulation results demonstrate the effectiveness and the notability of the proposed algorithm comparing with Trainlm-LM, quasi-Newton (Trainbfg-BFGS), and Resilient Propagation (trainrp-RPROP) algorithms.

In the present paper it is assumed that the recrystallization temperature of uranium dioxide decreases with burn-up. Two opposing effects of enhancement and mhibition of irradiation damage introduced by fission effect on gram growth are described. Mathematical model of fission gas release from the UO 2 fuel affected by grain growth is presented. Theoretical results are compared with the experimental data.