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Multi-scale numerical modelling of phase transition phenomena in metallic alloys

Multi-scale numerical modelling of phase transition phenomena in metallic alloys

Chirazi, Ali (2000) Multi-scale numerical modelling of phase transition phenomena in metallic alloys. PhD thesis, University of Greenwich.

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A multiscale numerical model of the solidification process involving the metallic alloys is proposed. The purpose of this model is to increase our basic knowledge of the physics of the solidification by incorporating atomic aspects of the phase change and, therefore, to predict the rnicrostructural features of the phase transformation based on the alloy parameters, such as the solute concentration and the process parameters, such as the cooling rate and surface roughness. The proposed multiscale strategy, is based on the parametric study of the solidification process at atomistic (nano), microscopic (rneso) and macroscopic levels. Once the major parameters, influencing the process at each scale, are identified, the three different levels are linked via the creation of relevant databases and the passage of information from one scale to another is implemented by using these databases. The multiscale model utilises the Molecular Dynamics (MD) methods at the atomic level, the Cellular Automaton (CA) method at the microscale and the Finite Volume (FV) models at the macroscale. The combination of these methods allowed us to study the phase transition beginning from the atomic clusterisation, progressing to the microstructure formation and culminating in the bulk formation at the macro level.

Item Type: Thesis (PhD)
Additional Information:
Uncontrolled Keywords: numerical modelling, molecular dynamics (MD),
Subjects: Q Science > QA Mathematics
Pre-2014 Departments: School of Computing & Mathematical Sciences
School of Computing & Mathematical Sciences > Department of Smart Systems Technologies
Last Modified: 25 Sep 2018 11:13
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
Selected for GREAT 2019: None
Selected for REF2021: None

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