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Durrant, A. 
ORCID: https://orcid.org/0000-0002-8375-4523, Leontidis, G., Kollias, S., Torres, L. A., Montalvo, C., Mylonakis, A., Demazière, C. and Vinai, P.
(2021)
DETECTION AND LOCALISATION OF MULTIPLE IN-CORE PERTURBATIONS WITH NEUTRON NOISE-BASED SELF-SUPERVISED DOMAIN ADAPTATION.
In:
Proceedings of the International Conference on Mathematics and Computational Methods Applied to Nuclear Science and Engineering, M and C 2021.
Proceedings of the International Conference on Mathematics and Computational Methods Applied to Nuclear Science and Engineering, M and C 2021
.
American Nuclear Society, Virtual, Online, pp. 2038-2047.
ISBN 9781713886310
Abstract
The use of non-intrusive techniques for monitoring nuclear reactors is becoming more vital as western fleets age. As a consequence, the necessity to detect more frequently occurring operational anomalies is of upmost interest. Here, noise diagnostics - the analysis of small stationary deviations of local neutron flux around its time-averaged value - is employed aiming to unfold from detector readings the nature and location of driving perturbations. Given that in-core instrumentation of western-type light-water reactors are scarce in number of detectors, rendering formal inversion of the reactor transfer function impossible, we propose to utilise advancements in Machine Learning and Deep Learning for the task of unfolding. This work presents an approach to such a task doing so in the presence of multiple and simultaneously occurring perturbations or anomalies. A voxel-wise semantic segmentation network is proposed to determine the nature and source location of multiple and simultaneously occurring perturbations in the frequency domain. A diffusion-based core simulation tool has been employed to provide simulated training data for two reactors. Additionally, we work towards the application of the aforementioned approach to real measurements, introducing a self-supervised domain adaptation procedure to align the representation distributions of simulated and real plant measurements.
| Item Type: | Book Section |
|---|---|
| Additional Information: | Publisher Copyright: Copyright © 2021 AMERICAN NUCLEAR SOCIETY, INCORPORATED, LA GRANGE PARK, ILLINOIS 60526.All rights reserved. |
| Uncontrolled Keywords: | core diagnostics,core monitoring,machine learning,neutron noise,nuclear energy and engineering,applied mathematics ,/dk/atira/pure/subjectarea/asjc/2100/2104 |
| Faculty \ School: | Faculty of Science > School of Computing Sciences |
| UEA Research Groups: | Faculty of Science > Research Groups > Data Science and AI |
| Related URLs: | |
| Depositing User: | LivePure Connector |
| Date Deposited: | 07 Jul 2026 16:08 |
| Last Modified: | 08 Jul 2026 12:19 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/103779 |
| DOI: |
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