On a various soft computing algorithms for reconstruction of the neutron noise source in the nuclear reactor cores

Abstract

This paper presents a comparative study of various soft computing algorithms for reconstruction of neutron noise sources in the nuclear reactor cores. To this end, the computational code for reconstruction of neutron noise source is developed based on the Adaptive Neuro-Fuzzy Inference System (ANFIS), Decision Tree (DT), Radial Basis Function (RBF) and Support Vector Machine (SVM) algorithms. Neutron noise source reconstruction process using the developed computational code consists of three stages of training, testing and validation. The information of neutron noise sources and induced neutron noise distributions are used as output and input data of training stage, respectively. As input data, both the real and imaginary parts of numerical value of the neutron noise in the detector are used. In the present study, the neutron noise source of absorber of variable strength type is only considered. The neutron noise distributions in the detectors due to 2000 randomly generated neutron noise sources are calculated using the developed computational code based on Galerkin Finite Element Method (GFEM). As output data, the strength, frequency of occurrence and location (X and Y coordinates) of the considered neutron noise sources are used. The VVER-1000 reactor core is considered as the benchmarking problem for validation of performed simulation using developed computational code. All specifications of neutron noise source including strength, frequency and location of the neutron noise source are reconstructed with high accuracy. Finally, a sensitivity analysis of results to the number of active detectors in the reactor core is performed. A comparative study of the performance of different developed algorithms represents Decision Tree as the most appropriate one for reconstruction of the neutron noise source in the nuclear reactor cores.