Noise source reconstruction using ANN and hybrid methods in VVER-1000 reactor core

Abstract

The present paper consists of two separate sections. In the first section, the neutron noise source is reconstructed using Artificial Neural Network (ANN) in a typical VVER-1000 reactor core. In the first stage of this section, the neutron noise calculations are performed based on Galerkin Finite Element Method (GFEM). To this end, two types of noise sources including absorber of variable strength and vibrating absorber are considered. As the results of noise calculations, the neutron noise is obtained in the location of detectors. In the second stage, the multilayer perception neural network is developed for reconstruction of the noise source. Complex neutron noises (real and imaginary parts) in the location of detectors are considered as the inputs of ANN. The developed artificial neural network consists of two hidden layers of type hyperbolic tangent sigmoid transfer function and a linear transfer function in the output layer. Noise source characteristics including strength, frequency and the location (X and Y coordinates) are identified with high accuracy. The developed hybrid method which comprises scanning method and multilayer perception neural network is employed for reconstruction of two coincidence noise sources. The number, type and location of noise source are exactly reconstructed using the hybrid method. The strength and frequency of noise source(s) are also identified with high accuracy using the developed method. A sensitivity analysis of the reconstructed noise source to some ANN parameters like the number of hidden layers, neurons in each hidden layer and the applied transfer functions is performed. Variation of accuracy of reconstructed noise source versus number of detectors and their arrangement in the reactor core are investigated as well.In the second section of present work, neutron flux variations (neutron noise) due to absorber of variable strength and vibrating absorber noise sources are studied in both frequency and time domains. Time dependency of neutron noise was obtained using inverse Fourier transform.