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


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

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.

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

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Authors

Xinyu Wei
School of Energy and Power Engineering, Xi’an Jiaotong University, Xi’an, Shaanxi 710049, China
Jiashuang Wan
School of Energy and Power Engineering, Xi’an Jiaotong University, Xi’an, Shaanxi 710049, China
Fuyu Zhao
School of Energy and Power Engineering, Xi’an Jiaotong University, Xi’an, Shaanxi 710049, China

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.

Keywords


English