Reactor pressure vessel embrittlement: Insights from neural network modelling

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

doi:10.1016/j.jnucmat.2018.02.027

Reactor pressure vessel embrittlement: Insights from neural network modelling

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

College of Engineering, Environment & Science

Research output: Contribution to journal › Article › peer-review

31 Citations (Scopus)

187 Downloads (Pure)

Abstract

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 language	English
Pages (from-to)	311-322
Number of pages	12
Journal	Journal of Nuclear Materials
Volume	502
Early online date	21 Feb 2018
DOIs	https://doi.org/10.1016/j.jnucmat.2018.02.027
Publication status	Published - 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 http://creativecommons.org/licenses/by-nc-nd/4.0/

Keywords

Irradiation damage
Neural networks
Reactor pressure vessel embrittlement

ASJC Scopus subject areas

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

Access to Document

10.1016/j.jnucmat.2018.02.027

Post-PrintAccepted author manuscript, 1.75 MBLicence: CC BY-NC-ND

Cite this

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title = "Reactor pressure vessel embrittlement: Insights from neural network modelling",

abstract = "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.",

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note = "NOTICE: this is the author{\textquoteright}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 {\textcopyright} 2018, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/",

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T2 - Insights from neural network modelling

AU - Mathew, J.

AU - Parfitt, D.

AU - Wilford, K.

AU - Riddle, N.

AU - Alamaniotis, M.

AU - Chroneos, A.

AU - Fitzpatrick, M. E.

N1 - 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 http://creativecommons.org/licenses/by-nc-nd/4.0/

PY - 2018/4/15

Y1 - 2018/4/15

N2 - 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.

AB - 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.

KW - Irradiation damage

KW - Neural networks

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ER -

Reactor pressure vessel embrittlement: Insights from neural network modelling

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

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