Cellular Automaton to Model Rapid Crystal Growth and Recrystallisation PhD 36 months PHD Programme By Loughborough University |TopUniversities
Subject Ranking

# 100QS Subject Rankings

Programme Duration

36 monthsProgramme duration

Tuitionfee

27,500 Tuition Fee/year

Application Deadline

01 Apr, 2025Application Deadline

Programme overview

Main Subject

Engineering - Mechanical

Degree

PhD

Study Level

PHD

Study Mode

On Campus

Texture in materials plays a crucial role in metallic products. A thorough study of the underlying morphology and its evolution is relevant for producing cast parts of innovative technological products. The formation of grain boundaries during directional solidification of grains with different crystallographic orientations affect its deformation and mechanical response. The cellular automaton (CA) method aims at modelling complex phenomena taking place at the scale of interest through the use of simple laws applied at a smaller scale. The goal is to predict the grain structure formed in large simulation domains. This scale-bridging is necessary as the development of the structure depends on phenomena taking place at large spatial distances all being influenced by time-dependent boundary conditions. Coupling of the CA method with a relevant crystal plasticity finite element (CPFE) method will allow for a physics-based computational framework with a unique capability to model crystal growth and recrystallization in a bio-compatible magnesium alloy. The approach will be used to predict the mechanical response of meso-scaled components made of magnesium alloy including next-generation bio-compatible stents.

Programme overview

Main Subject

Engineering - Mechanical

Degree

PhD

Study Level

PHD

Study Mode

On Campus

Texture in materials plays a crucial role in metallic products. A thorough study of the underlying morphology and its evolution is relevant for producing cast parts of innovative technological products. The formation of grain boundaries during directional solidification of grains with different crystallographic orientations affect its deformation and mechanical response. The cellular automaton (CA) method aims at modelling complex phenomena taking place at the scale of interest through the use of simple laws applied at a smaller scale. The goal is to predict the grain structure formed in large simulation domains. This scale-bridging is necessary as the development of the structure depends on phenomena taking place at large spatial distances all being influenced by time-dependent boundary conditions. Coupling of the CA method with a relevant crystal plasticity finite element (CPFE) method will allow for a physics-based computational framework with a unique capability to model crystal growth and recrystallization in a bio-compatible magnesium alloy. The approach will be used to predict the mechanical response of meso-scaled components made of magnesium alloy including next-generation bio-compatible stents.

Admission Requirements

3.2+
6.5+
92+

01 Apr 2025
3 Years
Apr
Jul

Domestic
4,786
International
27,500

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