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Science and Technology of Nuclear Installations
Volume 2016 (2016), Article ID 4720685, 6 pages
Research Article

Prediction Study on PCI Failure of Reactor Fuel Based on a Radial Basis Function Neural Network

School of Energy and Power Engineering, Xi’an Jiaotong University, Xi’an, Shaanxi 710049, China

Received 24 December 2015; Revised 11 February 2016; Accepted 10 April 2016

Academic Editor: Alejandro Clausse

Copyright © 2016 Xinyu Wei et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Pellet-clad interaction (PCI) is one of the major issues in fuel rod design and reactor core operation in water cooled reactors. The prediction of fuel rod failure by PCI is studied in this paper by the method of radial basis function neural network (RBFNN). The neural network is built through the analysis of the existing experimental data. It is concluded that it is a suitable way to reduce the calculation complexity. A self-organized RBFNN is used in our study, which can vary its structure dynamically in order to maintain the prediction accuracy. For the purpose of the appropriate network complexity and overall computational efficiency, the hidden neurons in the RBFNN can be changed online based on the neuron activity and mutual information. The presented method is tested by the experimental data from the reference, and the results demonstrate its effectiveness.