Molecular dynamics simulations of neutron induced collision cascades in Zr — Statistical modelling of irradiation damage and potential applications

Bartosz Barzdajn, Christopher P. Race

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Abstract

Understanding irradiation damage involves a multi-scale and multi-physics approach, integrating data from experiments, simulations and phenomenological models. This paper focuses on its early stages, specifically neutron-induced collision cascades in zirconium, as nuclear-grade zirconium alloys are widely used in fuel assemblies. We have gathered and analysed a significant sample of results from high-fidelity, large-scale molecular dynamics (MD) simulations, employing existing interatomic potentials and the two-temperature model to account for electron–phonon coupling. Our data can be directly applied to higher-scale methods.

Furthermore, we carried out a comprehensive statistical analysis of the features associated with the defect production, including the number of defects, their distribution and size of the affected area. As a result, we developed a generative model of collision cascades that is parametric, hierarchical and stochastic, i.e. it takes into account a statistical nature of the phenomenon, is interpretable and shareable. This model has been developed with three primary objectives: to provide a sufficient descriptor of a cascade, to interpolate data obtained from high-fidelity simulations, and to demonstrate that the statistical model can produce representative distributions of primary irradiation defects. The results can be used to generate synthetic inputs for models at longer length and time scales, to provide fast approximations that take into account the morphology of introduced defects, and in general to serve as a powerful analytical tool.
Original languageEnglish
Article number113315
Number of pages21
JournalComputational Materials Science
Volume245
Early online date29 Aug 2024
DOIs
Publication statusPublished - 1 Oct 2024

Keywords

  • Irradiation damage
  • collision cascades
  • Point defects
  • Vacancies
  • Self-interstitials
  • Statistical modelling
  • Generative Models

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    Burke, G. (PI), Engelberg, D. (PI), Preuss, M. (PI), Frankel, P. (PI), Jimenez-Melero, E. (PI), Pickering, E. (PI), Scenini, F. (PI), Stevens, N. (PI), Race, C. (PI), Connolly, B. (PI), Abdallah, M. (PGR student), Armson, S. (PGR student), Attar, H. (PGR student), Barron, P. (PGR student), Bird, J. (PGR student), Brown, A. (PGR student), Bruce, K. (PGR student), Buxton, O. (PGR student), Cassineri, S. (PGR student), Cattivelli, A. (PGR student), Chen, W. (PGR student), Collins, J. (PGR student), Debenham, L. (PGR student), Eaton-Mckay, J. (PGR student), Eguchi, K. (PGR student), Foster, D. (PGR student), Fox, C. (PGR student), Garrett, A. (PGR student), Gredis, A. (PGR student), Grime, T. (PGR student), Hou, J. (PGR student), Howe, B. (PGR student), Hughes, J. (PGR student), Hunt, C. (PGR student), Islam, U. (PGR student), Jalil, R. (PGR student), Jones, C. (PGR student), Kablan, A. (PGR student), Kapousidou, M. (PGR student), Koc, O. (PGR student), Kroll, R. (PGR student), Lang, R. (PGR student), Lennard, J. (PGR student), Liubercev, S. (PGR student), Mahmood, S. (PGR student), Maric, M. (PGR student), Martin, C. (PGR student), Mazzei, G. (PGR student), Rigby-Bell, M. (PGR student), Rogers, M. (PGR student), Sanchez, C. (PGR student), Shah, Z. (PGR student), Stavroulakis, E. (PGR student), Stuglik, P. (PGR student), Suleman, T. (PGR student), Supornpaibul, N. (PGR student), Thornley, S. (PGR student), Volpe, L. (PGR student), Wang, H. (PGR student), Wilson, M. (PGR student), Wilson, A. (PGR student), Woodward, T. (PGR student), Wylie, A. (PGR student), Xu, X. D. (PGR student), Yankova, M. (PGR student), Yildirim, E. (PGR student), Zhou, Y. (PGR student), Zhu, S. (PGR student), Barzdajn, B. (CoI), Carruthers, A. (CoI), Cassineri, S. (CoI), Chang, L. (CoI), Obasi, G. (CoI), Palko, S. (CoI), Reccagni, P. (CoI), Tang, R. (CoI), Thomas, R. (CoI), Unnikrishnan, R. (CoI), Zhang, Z. (CoI), Zhou, Y. (CoI) & Aspinall, A. (Support team)

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