Conference paper Open Access

Towards a Deep Unified Framework for Nuclear Reactor Perturbation Analysis

Fabio De Sousa Ribeiro; Francesco Caliva; Dionysios Chionis; Abdelhamid Dokhane; Antonios Mylonakis; Christophe Demaziere; Georgios Leontidis; Stefanos Kollias


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  <identifier identifierType="DOI">10.5281/zenodo.2480265</identifier>
  <creators>
    <creator>
      <creatorName>Fabio De Sousa Ribeiro</creatorName>
      <affiliation>University of Lincoln</affiliation>
    </creator>
    <creator>
      <creatorName>Francesco Caliva</creatorName>
      <affiliation>University of Lincoln</affiliation>
    </creator>
    <creator>
      <creatorName>Dionysios Chionis</creatorName>
      <affiliation>Paul Scherrer Institute</affiliation>
    </creator>
    <creator>
      <creatorName>Abdelhamid Dokhane</creatorName>
      <affiliation>Paul Scherrer Institute</affiliation>
    </creator>
    <creator>
      <creatorName>Antonios Mylonakis</creatorName>
      <affiliation>Chalmers University of Technology</affiliation>
    </creator>
    <creator>
      <creatorName>Christophe Demaziere</creatorName>
      <affiliation>Chalmers University of Technology</affiliation>
    </creator>
    <creator>
      <creatorName>Georgios Leontidis</creatorName>
      <affiliation>University of Lincoln</affiliation>
    </creator>
    <creator>
      <creatorName>Stefanos Kollias</creatorName>
      <affiliation>University of Lincoln</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Towards a Deep Unified Framework for Nuclear Reactor Perturbation Analysis</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2018</publicationYear>
  <subjects>
    <subject>deep learning, 3D convolutional neural networks, recurrent neural networks, long short-term memory, multi label classification, regression, signal processing, nuclear reactors, unfolding, anomaly detection</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2018-11-21</date>
  </dates>
  <language>en</language>
  <resourceType resourceTypeGeneral="ConferencePaper"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/2480265</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.2480264</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;In this paper, we take the first steps towards a novel unified framework for the analysis of perturbations in both the Time and Frequency domains. The identification of type and source of such perturbations is fundamental for monitoring reactor cores and guarantee safety while running at nominal conditions. A 3D Convolutional Neural Network (3DCNN) was employed to analyse perturbations happening in the frequency domain, such as an absorber of variable strength or propagating perturbation. Recurrent neural networks (RNN), specifically Long Short-Term Memory (LSTM) networks were used to study signal sequences related to perturbations induced in the time domain, including the vibrations of fuel assemblies and the fluctuations of thermal-hydraulic parameters at the inlet of the reactor coolant loops. 512 dimensional representations were extracted from the 3D-CNN and LSTM architectures, and used as input to a fused multi-sigmoid classification layer to recognise the perturbation type.&lt;/p&gt;</description>
  </descriptions>
  <fundingReferences>
    <fundingReference>
      <funderName>European Commission</funderName>
      <funderIdentifier funderIdentifierType="Crossref Funder ID">10.13039/100010661</funderIdentifier>
      <awardNumber awardURI="info:eu-repo/grantAgreement/EC/H2020/754316/">754316</awardNumber>
      <awardTitle>Core monitoring techniques and experimental validation and demonstration</awardTitle>
    </fundingReference>
  </fundingReferences>
</resource>
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