Localizing Perturbations in Pressurized Water Reactors Using One-Dimensional Deep Convolutional Neural Networks

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-...

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Autori principali:	Laurent Pantera, Petr Stulík, Antoni Vidal-Ferràndiz, Amanda Carreño, Damián Ginestar, George Ioannou, Thanos Tasakos, Georgios Alexandridis, Andreas Stafylopatis
Natura:	Artigo
Lingua:	Inglês
Pubblicazione:	MDPI AG 2021-12-01
Serie:	Sensors
Soggetti:	neutron noise neutron diffusion deep learning convolutional neural networks pressurized water reactor perturbation localization
Accesso online:	https://www.mdpi.com/1424-8220/22/1/113
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