Problem Decomposition and Information Minimization for the Global, Concurrent, On-line Validation of Neutron Noise Signals and Neutron Detector Operation

Abstract

This piece of research introduces a purely data-driven, directly reconfigurable, divide-and-conquer on-line monitoring (OLM) methodology for automatically selecting the minimum number of neutron detectors (NDs) – and corresponding neutron noise signals (NSs) – which are currently necessary, as well as sufficient, for inspecting the entire nuclear reactor (NR) in-core area. The proposed implementation builds upon the 3-tuple configuration, according to which three sufficiently pairwise-correlated NSs are capable of on-line (I) verifying each NS of the 3-tuple and (II) endorsing correct functioning of each corresponding ND, implemented herein via straightforward pairwise comparisons of fixed-length sliding time-windows (STWs) between the three NSs of the 3-tuple. A pressurized water NR (PWR) model – developed for H2020 CORTEX – is used for deriving the optimal ND/NS configuration, where (i) the evident partitioning of the 36 NDs/NSs into six clusters of six NDs/NSs each, and (ii) the high cross-correlations (CCs) within every 3-tuple of NSs, endorse the use of a constant pair comprising the two most highly CC-ed NSs per cluster as the first two members of the 3-tuple, with the third member being each remaining NS of the cluster, in turn, thereby computationally streamlining OLM without compromising the identification of either deviating NSs or malfunctioning NDs. Tests on the in-core dataset of the PWR model demonstrate the potential of the proposed methodology in terms of suitability for, efficiency at, as well as robustness in ND/NS selection, further establishing the “directly reconfigurable” property of the proposed approach at every point in time while using one-third only of the original NDs/NSs.