On-line monitoring (OLM) of nuclear reactors (NRs) incorporatesamong other prioritiesthe concurrent verification of (i) valid operation of the NR neutron detectors (NDs) and (ii) soundness of the captured neutron noise (NN) signals (NSs) per se. In this piece of research, efficient, timely, directly reconfigurable and non-invasive OLM is implemented for providing swiftyet precisedecisions upon the (i) identities of malfunctioning NDs and(ii) locations of NR instability/unexpected operation. The use of Harmony Theory Networks (HTNs)is put forward to this end, with the results demonstrating the ability of these constraint-satisfaction artificial neural networks (ANNs) to identify(a) the smallest possible set of NDs which, configured into (b) the minimum number of 3-tuples of NDs operating on(c) the shortest NS time-window possible, instigate maximally efficient and accurate OLM. A proof-of-concept demonstration on the set of eight ex-core NDs and corresponding NSs of a simulated Pressurized Water nuclear Reactor (PWR) exhibits(i) significantly higher efficiency, at(ii) no detriment to localization accuracy, when employing only (iii) half of the original NDs and corresponding NSs, which are configured in (iv) a total of only two (out of the 56 combinatorially possible)3-tuples of NDs. Follow-up research shall investigate the scalability of the proposed methodology on the more extensive and homogeneous (i.e. "harder" in terms of ND/NS cardinality as well as of ranking/selection) dataset of the 36 in-core NSs of the same simulated NR.

This work outlines an approach for localizing anomalies in nuclear reactor cores during their steady state operation, employing deep, one-dimensional, convolutional neural networks. Anomalies are characterized by the application of perturbation diagnostic techniques, based on the analysis of the so-called “neutron-noise” signals: that is, fluctuations of the neutron flux around the mean value observed in a steady-state power level. The proposed methodology is comprised of three steps: initially, certain reactor core perturbations scenarios are simulated in software, creating the respective perturbation datasets, which are specific to a given reactor geometry; then, the said datasets are used to train deep learning models that learn to identify and locate the given perturbations within the nuclear reactor core; lastly, the models are tested on actual plant measurements. The overall methodology is validated on hexagonal, pre-Konvoi, pressurized water, and VVER-1000 type nuclear reactors. The simulated data are generated by the FEMFFUSION code, which is extended in order to deal with the hexagonal geometry in the time and frequency domains. The examined perturbations are absorbers of variable strength, and the trained models are tested on actual plant data acquired by the in-core detectors of the Temelín VVER-1000 Power Plant in the Czech Republic. The whole approach is realized in the framework of Euratom’s CORTEX project.

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Dynamic response of all PWR reactors is generated by the turbulent pressure pulsations (TPP) in the boundary layer on the surface of core barrel and acoustic pressure pulsations (APP) generated by main circulation pumps (MCP). While TPP have character of the white noise in the frequency range 〈0; 35〉 Hz, APP generate discrete set of frequencies called revolution and blade frequencies. The rotor revolutions of individual MCPs are slightly different and are changing as the operation conditions are changing. It results in the varying beat character of reactor vibrations. Results of numerical simulations and experimental measurements are presented.

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