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Neural networks for displacement analysis in an advanced gas cooled reactor core model

Neural networks for displacement analysis in an advanced gas cooled reactor core model

Dihoru, Luiza, Dietz, Matt, Horseman, Tony, Kloukinas, Panos ORCID: 0000-0002-5158-3109, Oddbjornsson, Olafur, Voyagaki, Elia, Crewe, Adam J. and Taylor, Colin A. (2018) Neural networks for displacement analysis in an advanced gas cooled reactor core model. Nuclear Engineering and Design, 332. pp. 252-266. ISSN 0029-5493 (doi:https://doi.org/10.1016/j.nucengdes.2018.03.039)

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Abstract

This paper presents a Neural Network (NN) approach for displacement analysis with applications in modelling the seismic response of the UK's Advanced Gas Cooled Reactors (AGRs). A quarter sized physical model of a reactor core was developed at the University of Bristol to provide experimental validation to the existing numerical models that support the seismic resilience assessments of the AGRs. The physical model outputs include displacement and acceleration datasets of considerable size and complexity, collected for a range of seismic inputs and postulated component damage scenarios. Rich sets of displacement data were employed in training two NN models that can predict displacement at user-defined locations in the core physical model and can map the correlation between the component relative displacements. Understanding component displacements is particularly important, as such displacements may affect the channel shapes and can cause local and general distortion of the core. This paper presents the development, testing and performance of the NN models. The NNs yield predictions that compare well with the experimentally obtained parameters. As more experimental test data become available, the NN's prediction capability will benefit from accumulated training. In the future, the NNs will be incorporated into a multi-layered framework for dynamic response prediction and analysis.

Item Type: Article
Uncontrolled Keywords: advanced gas cooled reactor, displacement prediction, seismic testing, neural network
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
Faculty / Department / Research Group: Faculty of Engineering & Science
Faculty of Engineering & Science > Department of Engineering Science
Last Modified: 16 Aug 2019 09:01
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
Selected for GREAT 2019: None
URI: http://gala.gre.ac.uk/id/eprint/24925

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