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.
Highlights
The reactor core of Light Water Reactors (LWRs) holds fuel assemblies of fuel rods, which consist of zirconium alloy tubes containing uranium dioxide pellets
Pellet-Cladding Interaction (PCI)-induced clad tube failure is caused by a combination of stresses in the Zr-alloy clad due to the pelletclad contact pressure and chemical reaction of corrosive fission products, such as iodine released during operation, with Zr-alloy under a power ramp
Taking into account that the performance of an radial basis function neural network (RBFNN) is heavily dependent on its architecture, many researchers have focused on self-organizing methods that can be used to design the architecture of three-layered RBFNN
Summary
The reactor core of Light Water Reactors (LWRs) holds fuel assemblies of fuel rods, which consist of zirconium alloy tubes containing uranium dioxide pellets. PCI-induced clad tube failure is caused by a combination of stresses in the Zr-alloy clad due to the pelletclad contact pressure and chemical reaction of corrosive fission products, such as iodine released during operation, with Zr-alloy under a power ramp. We present a neural network method to predict PCI failure. The reduction of the calculation complexity of the present method may contribute to the online calculation and prediction of the PCI failure in operating reactors. The actual result and its inputs will be used to replace one of the initial experimental data; a new RBFNN will be trained by the new data. Manner, the PCI failure probability can be predicted online by the simple and fast neural network method, which allow for straightforward implementation within a transient analysis methodology and core monitoring systems, and only the input parameters of the transient are considered in this method.
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