Reactor pressure vessel embrittlement: Insights from neural network modelling

J. Mathew, D. Parfitt, K. Wilford, N. Riddle, M. Alamaniotis, A. Chroneos, M. E. Fitzpatrick

Research output: Contribution to journalArticlepeer-review

27 Citations (Scopus)
165 Downloads (Pure)


Irradiation embrittlement of steel pressure vessels is an important consideration for the operation of current and future light water nuclear reactors. In this study we employ an ensemble of artificial neural networks in order to provide predictions of the embrittlement using two literature datasets, one based on US surveillance data and the second from the IVAR experiment. We use these networks to examine trends with input variables and to assess various literature models including compositional effects and the role of flux and temperature. Overall, the networks agree with the existing literature models and we comment on their more general use in predicting irradiation embrittlement.

Original languageEnglish
Pages (from-to)311-322
Number of pages12
JournalJournal of Nuclear Materials
Early online date21 Feb 2018
Publication statusPublished - 15 Apr 2018

Bibliographical note

NOTICE: this is the author’s version of a work that was accepted for publication in Journal of Nuclear Materials. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Journal of Nuclear Materials, [502, (2018)] DOI: 10.1016/j.jnucmat.2018.02.027

© 2018, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International


  • Irradiation damage
  • Neural networks
  • Reactor pressure vessel embrittlement

ASJC Scopus subject areas

  • Nuclear and High Energy Physics
  • Materials Science(all)
  • Nuclear Energy and Engineering


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