Version 1
: Received: 26 June 2019 / Approved: 27 June 2019 / Online: 27 June 2019 (05:56:10 CEST)
How to cite:
Yousefi, M. Large-Scale Multiscale Modeling of Phase Transformation in Nanocrystalline Materials: Atomistic and Phase-Field Methods. Preprints2019, 2019060279. https://doi.org/10.20944/preprints201906.0279.v1
Yousefi, M. Large-Scale Multiscale Modeling of Phase Transformation in Nanocrystalline Materials: Atomistic and Phase-Field Methods. Preprints 2019, 2019060279. https://doi.org/10.20944/preprints201906.0279.v1
Yousefi, M. Large-Scale Multiscale Modeling of Phase Transformation in Nanocrystalline Materials: Atomistic and Phase-Field Methods. Preprints2019, 2019060279. https://doi.org/10.20944/preprints201906.0279.v1
APA Style
Yousefi, M. (2019). Large-Scale Multiscale Modeling of Phase Transformation in Nanocrystalline Materials: Atomistic and Phase-Field Methods. Preprints. https://doi.org/10.20944/preprints201906.0279.v1
Chicago/Turabian Style
Yousefi, M. 2019 "Large-Scale Multiscale Modeling of Phase Transformation in Nanocrystalline Materials: Atomistic and Phase-Field Methods" Preprints. https://doi.org/10.20944/preprints201906.0279.v1
Abstract
In this research, atomistic molecular dynamics simulations are combined with mesoscopic phase-field computational methods in order to investigate phase-transformation in polycrystalline Aluminum microstructure. In fact, microstructural computational modeling of engineering materials could help to optimize their mechanical properties for industrial applications (e.g. directional solidification for turbine blades). As a result, a multiscale modeling approach is developed to find a relation between manufacturing variables (e.g. temperature) and microstructural properties of crystalline materials (e.g. grain size), which could be used to develop an advanced manufacturing process for sensitive applications. The results show that atomistic modeling of grain growth could be used as a first-principle approach in order to study phase transformation's kinetics, which could capture morphology of polycrystalline materials more accurately. On the other hand, phase-field mesoscopic approach needs less computational efforts, but still it relies on semi-empirical data to capture accurate phase transformation regimes, which makes this approach suitable for rapid examining of new manufacturing conditions as well as its effects on microstructural properties of polycrystalline materials.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
