Prediction Study on PCI Failure of Reactor Fuel Based on a Radial Basis Function Neural Network
Joint Authors
Wei, Xinyu
Zhao, Fuyu
Wan, Jiashuang
Source
Science and Technology of Nuclear Installations
Issue
Vol. 2016, Issue 2016 (31 Dec. 2016), pp.1-6, 6 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-04-24
Country of Publication
Egypt
No. of Pages
6
Abstract EN
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.
American Psychological Association (APA)
Wei, Xinyu& Wan, Jiashuang& Zhao, Fuyu. 2016. Prediction Study on PCI Failure of Reactor Fuel Based on a Radial Basis Function Neural Network. Science and Technology of Nuclear Installations،Vol. 2016, no. 2016, pp.1-6.
https://search.emarefa.net/detail/BIM-1118557
Modern Language Association (MLA)
Wei, Xinyu…[et al.]. Prediction Study on PCI Failure of Reactor Fuel Based on a Radial Basis Function Neural Network. Science and Technology of Nuclear Installations No. 2016 (2016), pp.1-6.
https://search.emarefa.net/detail/BIM-1118557
American Medical Association (AMA)
Wei, Xinyu& Wan, Jiashuang& Zhao, Fuyu. Prediction Study on PCI Failure of Reactor Fuel Based on a Radial Basis Function Neural Network. Science and Technology of Nuclear Installations. 2016. Vol. 2016, no. 2016, pp.1-6.
https://search.emarefa.net/detail/BIM-1118557
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1118557