Tatiana Tambouratzis^a, Laurent Pantera^b and Petr Stulik^c, ^aUniversity of Piraeus, Greece, ^bLaboratoire des Programmes Expérimentaux et des Essais en Sûreté, France, ^cÚJV Řeža.s., Czech Republic

On-line monitoring (OLM) of nuclear reactors (NRs) incorporates – among other priorities – the 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 swift – yet precise – decisions 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.

Nuclear Reactor (NR), On-Line Monitoring (OLM), Neutron Noise (NN), Neutron Noise Signal (NS), Neutron Detector (ND), Computational Intelligence (CI), Artificial Neural Network (ANN), Harmony Theory Network (HTN), 3-tuple of NDs/NSs