Feasibility study of noise analysis methods on virtual thermal reactor subcriticality monitoring

Abstract

This paper presents the analysis results of Rossi-alpha, cross-correlation, Feynman-alpha, and Feynman difference methods applied to the subcriticality monitoring of nuclear reactors. A thermal spectrum Godiva model has been designed for the analysis of the four methods. This Godiva geometry consists of a spherical core containing the isotopes of H-l, U-235 and U-238, and the H 2O reflector outside the core. A Monte Carlo code, McCARD, is used in real time mode to generate virtual detector signals to analyze the feasibility of the four methods. The analysis results indicate that the four methods can be used with high accuracy for the continuous monitoring of subcriticality. In addition to that, in order to analyze the impact of the random noise contamination on the accuracy of the noise analysis, the McCARD-generated signals are contaminated with arbitrary noise. It is noticed that, even when the detector signals are contaminated, the four methods can predict the subcriticality with reasonable accuracy. Nonetheless, in order to reduce the adverse impact of the random noise, eight detector signals, rather than a single signal, are generated from the core, one signal from each equally divided eighth part of the core. The preliminary analysis with multiple virtual detector signals indicates that the approach of using many detectors is promising to improve the accuracy of criticality prediction and further study will be performed in this regard.