Identification of dominant damage accumulation processes at grain boundaries during irradiation in nanocrystalline α-Fe: A statistical study
| Autor: | Rémi Dingreville, Laurent Capolungo, Aaron Dunn, Enrique Martínez |
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| Rok vydání: | 2016 |
| Předmět: |
010302 applied physics
Materials science Polymers and Plastics Binding energy Metals and Alloys 02 engineering and technology 021001 nanoscience & nanotechnology 01 natural sciences Nanocrystalline material Electronic Optical and Magnetic Materials Crystallography Chemical physics Vacancy defect 0103 physical sciences Radiation damage Frenkel defect Ceramics and Composites Grain boundary diffusion coefficient Grain boundary 0210 nano-technology Grain boundary strengthening |
| Zdroj: | Acta Materialia. 110:306-323 |
| ISSN: | 1359-6454 |
| DOI: | 10.1016/j.actamat.2016.03.026 |
| Popis: | Radiation defect accumulation in metals with high interface to volume ratios, such as nanocrystalline metals, is strongly dependent on the ability of these interfaces to act as unbiased sinks for defects. Multi-scale simulations of damage accumulation in such materials necessarily depend on parameters describing defect behaviors inside grain boundaries, such as migration and binding energies within grain boundaries as well as binding energies of defects to grain boundaries. In general, these behaviors are sensitive to the grain boundary structure, making atomic-scale quantification of such parameters challenging due to the large number of variables in the input space of such a problem. The goal of the present study is to identify which of these parameters most strongly influence defect accumulation and grain boundary sink efficiency in α-Fe during Frenkel pair implantation at room temperature and a dose rate of 10−7 dpa⋅ s−1. Defect accumulation inside grains and in grain boundaries is simulated using spatially resolved stochastic cluster dynamics (SRSCD). Using this methodology, sensitivity studies investigating vacancy accumulation in grain boundaries, sink efficiency η of grain boundaries, and vacancy cluster profiles inside grains are performed by varying model input parameters such as defect migration and binding energies inside grain boundaries. Principal component analysis of these model input parameters is then carried out to identify which defect behaviors are strongly correlated with changes in damage accumulation metrics when varying many parameters at once. In both of these analyses, single vacancy and self-interstitial diffusion inside grain boundaries, small vacancy cluster diffusion inside grain boundaries, and the binding energy of vacancies and self-interstitials to grain boundaries are shown to have the greatest impact on defect accumulation. |
| Databáze: | OpenAIRE |
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