Structural deformation and transformation of θ'-Al2Cu precipitate in Al matrix via interfacial diffusion
Journal Article
·
· Computational Materials Science
- Univ. of Tennessee, Knoxville, TN (United States)
- Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Metastable θ'-Al2Cu precipitates enhance mechanical properties of Al alloy, but gradually transform to θ phase due to diffusion related coarsening at higher temperatures (>200°C). To improve understanding of the phase instability and transformation of θ'-Al2Cu, we investigated the interfacial atomic mobility of θ'-Al2Cu precipitate in Al matrix via molecular dynamics (MD). To characterize the interfacial atomic mobility, diffusion properties (activation energy, diffusivity, and jump attempt frequency) with respect to atomic species, interfacial structure, and temperature are calculated using atomic trajectories from MD. For the enhanced accuracy of this analysis, especially at low temperatures, temporal scale of molecular dynamics is significantly extended by employing the parallel replica dynamics. This study, as the first MD investigation on atomic diffusion of θ'-Al2Cu in Al matrix, suggests and discusses (i) structural deformation activated at the semi-coherent θ'-Al2Cu/Al interfaces, (ii) transformation initiated at the edge of the coherent θ'-Al2Cu/Al interface, and (iii) directional dependence of diffusion properties. Overall, these findings are expected to be employed in a larger scale phase-field modeling of precipitate growth, and to contribute to the understanding of phase transformation process and the development of alloys with θ'-phase retained to higher temperatures.
- Research Organization:
- Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
- Sponsoring Organization:
- National Science Foundation (NSF); USDOE
- Grant/Contract Number:
- AC05-00OR22725
- OSTI ID:
- 1784208
- Alternate ID(s):
- OSTI ID: 1636610
- Journal Information:
- Computational Materials Science, Journal Name: Computational Materials Science Vol. 156; ISSN 0927-0256
- Publisher:
- ElsevierCopyright Statement
- Country of Publication:
- United States
- Language:
- English
Machine Learning for Thermal Transport Analysis of Aluminum Alloys with Precipitate Morphology
|
journal | January 2019 |
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