Prediction Study on PCI Failure of Reactor Fuel Based on a Radial Basis Function Neural Network. (24th April 2016)

• Record Type: Journal Article
• Authors: Wei, Xinyu; Wan, Jiashuang; Zhao, Fuyu
• Other Names: Clausse Alejandro Academic Editor.
• Is Part Of: Science and technology of nuclear installations. Volume 2016(2016)
• Publication Date: 2016-04-24
• Subjects: Nuclear engineering -- Periodicals; Nuclear facilities -- Periodicals; Nuclear engineering; Nuclear facilities; Electronic journals; Periodicals; 621.48
• DOI: 10.1155/2016/4720685
• Languages: English
• ISSNs: 1687-6075

Abstract

Abstract : 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.