Abstract
Microstructure evolution of irradiated materials is a complex phenomenon that involves time and length scales that can expand several orders of magnitude. Defects produced in the irradiation can interact with the existing microstructure, sometimes inducing changes in the mechanical, electrical or even magnetic properties. The selection of the most adequate material for nuclear applications requires an understanding at a fundamental level of the evolution of these defects during the lifetime of the reactors. Therefore, very efficient simulation tools, with physical and accurate parameters must be used. In this review, one of the computational methods that is commonly employed to study defect evolution, kinetic Monte Carlo, is described. The differences and similarities between three algorithms are explained: atomistic (or lattice), object and event kinetic Monte Carlo. In order to reveal the applicability of these methods in the nuclear field, examples are given for the case of one of the candidates for first wall materials for both fusion and IV generation fission reactors: FeCr alloys. Finally, the limitations of the models to describe these systems and the current efforts to improve the predictive capabilities of this approach, as well as other developments in the field are discussed.
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Acknowledgments
This work has been carried out within the framework of the EUROfusion Consortium and has received funding from the Euratom research and training program 2014–2018 under grant agreement No 633053. The views and opinions expressed herein do not necessarily reflect those of the European Commission. The research leading to these results is partly funded by the European Atomic Energy Community’s (Euratom) H2020 WP 2016–2017 call NFRP13 (M4F) and in the framework of the EERA (European Energy Research Alliance) Joint Programme on Nuclear Materials.
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Balbuena, J.P., Caturla, M.J., Martinez, E. (2018). Kinetic Monte Carlo Algorithms for Nuclear Materials Applications. In: Andreoni, W., Yip, S. (eds) Handbook of Materials Modeling. Springer, Cham. https://doi.org/10.1007/978-3-319-50257-1_120-1
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