Adaptive group of ink drop spread: a computer code to unfold neutron noise sources in reactor cores

Abstract

Abstract The present paper reports the development of a computational code based on the Adaptive Group of Ink Drop Spread (AGIDS) for reconstruction of the neutron noise sources in reactor cores. AGIDS algorithm was developed as a fuzzy inference system based on the active learning method. The main idea of the active learning method is to break a multiple input–single output system into a single input–single output system. This leads to the ability to simulate a large system with high accuracy. In the present study, vibrating absorber-type neutron noise source in an International Atomic Energy Agency-two dimensional reactor core is considered in neutron noise calculation. The neutron noise distribution in the detectors was calculated using the Galerkin finite element method. Linear approximation of the shape function in each triangle element was used in the Galerkin finite element method. Both the real and imaginary parts of the calculated neutron distribution of the detectors were considered input data in the developed computational code based on AGIDS. The output of the computational code is the strength, frequency, and position ( X and Y coordinates) of the neutron noise sources. The calculated fraction of variance unexplained error for output parameters including strength, frequency, and X and Y coordinates of the considered neutron noise sources were 0.002682 #/cm 3 s, 0.002682 Hz, and 0.004254 cm and 0.006140 cm, respectively.