PhD Projects
The following projects are fully funded and open to students from the EU. They are available for a start in September 2019.
Understanding recrystallisation: atomistic simulations of defect-driven grain structure evolution
At the cutting-edge of technological development, we are placing ever more exacting demands on the performance of metallic components and so we need an ever increasing degree of control over the properties of our metallic materials. These properties depend in large part on the grain structure of the metal, i.e. on the pattern of grain shapes and orientations within the microstructure. A key process in obtaining an optimal grain structure is that of recrystallisation, in which new grains of defect-free material nucleate and grow within the defect-laden microstructure of deformed material.
This proposal will use atomistic simulations to gain new insight into the process of recrystallisation. Previous simulations of recrystallisation have focussed on simulating the process at larger length scales, using mesoscale techniques such as cellular automata and phase-field models. However, such models require that the dynamics of nucleation and growth of new grains be specified at the outset and the structure of the grain boundaries and deformation defects is entirely absent. By using classical molecular dynamics methods you will model aspects of the recrystallisation process from the level of atoms upwards. The structure of deformation defects and grain boundaries will be represented explicitly and processes such of nucleation and grain boundary migration will emerge from within the simulations. This will allow you to study the mechanisms and kinetics of recrystallisation directly.
you will use simulations of carefully chosen model bicrystal and tricrystal systems to determine how the presence of large amounts of damage (in the form of dislocations formed during plastic deformation) affects the properties of grain boundaries. You will examine the energy and structure of grain boundaries as a function of their geometry in the presence of defects and compare with boundaries in pristine material. You will then undertake dynamical simulations of grain boundary migration in the model systems and determine the mechanisms associated with this motion and the implications for the kinetics of grain structure evolution during recrystallisation. Finally, you will study the processes by which new grains nucleate at different microstructural sites within deformed material.
This proposal will use atomistic simulations to gain new insight into the process of recrystallisation. Previous simulations of recrystallisation have focussed on simulating the process at larger length scales, using mesoscale techniques such as cellular automata and phase-field models. However, such models require that the dynamics of nucleation and growth of new grains be specified at the outset and the structure of the grain boundaries and deformation defects is entirely absent. By using classical molecular dynamics methods you will model aspects of the recrystallisation process from the level of atoms upwards. The structure of deformation defects and grain boundaries will be represented explicitly and processes such of nucleation and grain boundary migration will emerge from within the simulations. This will allow you to study the mechanisms and kinetics of recrystallisation directly.
you will use simulations of carefully chosen model bicrystal and tricrystal systems to determine how the presence of large amounts of damage (in the form of dislocations formed during plastic deformation) affects the properties of grain boundaries. You will examine the energy and structure of grain boundaries as a function of their geometry in the presence of defects and compare with boundaries in pristine material. You will then undertake dynamical simulations of grain boundary migration in the model systems and determine the mechanisms associated with this motion and the implications for the kinetics of grain structure evolution during recrystallisation. Finally, you will study the processes by which new grains nucleate at different microstructural sites within deformed material.
Funded by: The Royal Society
For more information and to apply, see: www.findaphd.com/
For informal enquiries contact: Dr Chris Race at [email protected]
For more information and to apply, see: www.findaphd.com/
For informal enquiries contact: Dr Chris Race at [email protected]
Developing a "microstructural fingerprint" of titanium alloys - metallurgy in the information age
The properties of the metallic materials that we rely on in almost every aspect of our lives are highly dependent on their microstructures. The patterns of grain and phase boundaries within the metals, the grain shapes and the distribution of defects within the material (such as stacking faults (2-dimensional), dislocations (1-d) and point defects (0-d)) are hugely important in determining these properties. Much of the complexity in modern engineering alloys in terms of composition and processing (the ingredients and steps of the alloy recipe) is a result of the need to achieve highly optimised properties for deployment in very challenging service environments.
It is therefore curious that we have no universally agreed language for describing material microstructure. Often, for example, a pattern of grains in a polycrystal might be described by no more than an average grain size and some measure of the grain shape. Clearly this misses most of the information inherent in the detailed microstructure. Modern high-resolution, high-throughput experimental characterisation equipment can generate detailed images of microstructure at a very high rate, but most of the information is effectively thrown away immediately: the raw data files are too big to handle (and often too big even to retain) and we lack a descriptive language for capturing the essence of the microstructure in detail.
This PhD project will begin to address this deficiency. You will work to develop a methodology in which the tools of computer vision and image analysis are used alongside machine learning methods to produce a "microstructural fingerprint" of an alloy system. The project is sponsored by Rolls-Royce and will take as an example material the Ti6/4 alloy used for fan blades in jet engines. This material has a rich microstructure, requiring description on multiple length scales. Furthermore, the microstructure directly influences several key performance characteristics and Rolls-Royce has available a large database of material and properties and performance data.
Possible applications for a robust method of microstructural fingerprinting would include:
- Rapid characterisation of material along the supply and production chain permitting improved quality control;
- Development of "digital twins" at the alloy microstructure level. These are computer models evolved alongside real components to help identify possible issues or opportunities for improvement;
- Linking of composition and processing to key microstructural features or of these features to alloy properties and performance. Machine learning tools might then be used to relate composition and processing to properties via microstructure. A sound way to describe microstructure is a key step in this process.
Clearly, the methods developed as part of this project would be equally applicable to other alloy systems and to non-metallic poly-crystalline materials.
It is therefore curious that we have no universally agreed language for describing material microstructure. Often, for example, a pattern of grains in a polycrystal might be described by no more than an average grain size and some measure of the grain shape. Clearly this misses most of the information inherent in the detailed microstructure. Modern high-resolution, high-throughput experimental characterisation equipment can generate detailed images of microstructure at a very high rate, but most of the information is effectively thrown away immediately: the raw data files are too big to handle (and often too big even to retain) and we lack a descriptive language for capturing the essence of the microstructure in detail.
This PhD project will begin to address this deficiency. You will work to develop a methodology in which the tools of computer vision and image analysis are used alongside machine learning methods to produce a "microstructural fingerprint" of an alloy system. The project is sponsored by Rolls-Royce and will take as an example material the Ti6/4 alloy used for fan blades in jet engines. This material has a rich microstructure, requiring description on multiple length scales. Furthermore, the microstructure directly influences several key performance characteristics and Rolls-Royce has available a large database of material and properties and performance data.
Possible applications for a robust method of microstructural fingerprinting would include:
- Rapid characterisation of material along the supply and production chain permitting improved quality control;
- Development of "digital twins" at the alloy microstructure level. These are computer models evolved alongside real components to help identify possible issues or opportunities for improvement;
- Linking of composition and processing to key microstructural features or of these features to alloy properties and performance. Machine learning tools might then be used to relate composition and processing to properties via microstructure. A sound way to describe microstructure is a key step in this process.
Clearly, the methods developed as part of this project would be equally applicable to other alloy systems and to non-metallic poly-crystalline materials.
Funded by: EPSRC and Rolls-Royce plc
For more information and to apply, see: https://www.findaphd.com/
For informal enquiries contact: Dr Chris Race at [email protected]
For more information and to apply, see: https://www.findaphd.com/
For informal enquiries contact: Dr Chris Race at [email protected]
Understanding the role of nucleation and growth in the the oxidation of zirconium under irradiation
The pellets of uranium oxide fissile fuel in pressurised water reactors are contained within tubes of zirconium alloy - the fuel clad. Cladding materials present a particular opportunity for research with rapid impact, because they are amongst the few parts of a nuclear reactor that are replaced during its lifecycle.
Amongst other degradation processes, the zirconium alloy cladding undergoes oxidation in the high-temperature pressurised water in which it sits. A better understanding of the corrosion mechanism would allow for the design of safer, more efficient fuels. The pattern of corrosion involves repeated cycles of initially rapid then slower corrosion, before eventually a rapid breakaway phase, with linear kinetics, takes hold. Recent work at UoM suggests that oxidation proceeds very differently in the presence of irradiation to without. In particular the oxide forms much more quickly, with smaller grains with more random orientations.
This modelling project will focus on disentangling the roles of oxide grain nucleation and growth in giving rise to differences in oxide growth rate and texture with and without irradiation. The working hypothesis is that irradiation enables faster nucleation of oxide grains. This gives rise to faster oxide growth leading to a weaker texture, further accelerating oxide growth and so on.
The student will develop a mesoscale model of oxide nucleation and growth on realistic length and time scales. The proposed technique will be a cellular model, such as the Monte Carlo Potts model, which will capture a real-space picture of the evolving oxide. The model will be flexible: it will incorporate the effects of phase transformation, grain and phase boundaries, lattice orientation effects, oxide species transport etc. and allow for the exploration of the interplay of the various different processes involved in oxide growth.
The flexibility of the model will allow the student to explore a wide range of the possible parameter space and so identify the dominant factors in oxide growth, comparing, for example the effects of: accumulation of matrix damage; the dissolution of SPPs and formation of nano-clusters; the importance of grain boundary energies and texture strength.
The chosen scale of the model will allow for direct comparison with experiment. It will also be able to draw on results from atomistic modelling and be directly compared to larger scale continuum models of oxidation.
Amongst other degradation processes, the zirconium alloy cladding undergoes oxidation in the high-temperature pressurised water in which it sits. A better understanding of the corrosion mechanism would allow for the design of safer, more efficient fuels. The pattern of corrosion involves repeated cycles of initially rapid then slower corrosion, before eventually a rapid breakaway phase, with linear kinetics, takes hold. Recent work at UoM suggests that oxidation proceeds very differently in the presence of irradiation to without. In particular the oxide forms much more quickly, with smaller grains with more random orientations.
This modelling project will focus on disentangling the roles of oxide grain nucleation and growth in giving rise to differences in oxide growth rate and texture with and without irradiation. The working hypothesis is that irradiation enables faster nucleation of oxide grains. This gives rise to faster oxide growth leading to a weaker texture, further accelerating oxide growth and so on.
The student will develop a mesoscale model of oxide nucleation and growth on realistic length and time scales. The proposed technique will be a cellular model, such as the Monte Carlo Potts model, which will capture a real-space picture of the evolving oxide. The model will be flexible: it will incorporate the effects of phase transformation, grain and phase boundaries, lattice orientation effects, oxide species transport etc. and allow for the exploration of the interplay of the various different processes involved in oxide growth.
The flexibility of the model will allow the student to explore a wide range of the possible parameter space and so identify the dominant factors in oxide growth, comparing, for example the effects of: accumulation of matrix damage; the dissolution of SPPs and formation of nano-clusters; the importance of grain boundary energies and texture strength.
The chosen scale of the model will allow for direct comparison with experiment. It will also be able to draw on results from atomistic modelling and be directly compared to larger scale continuum models of oxidation.
Funded by: EPSRC and Rolls-Royce plc
For more information and to apply, see: https://www.findaphd.com/
For informal enquiries contact: Dr Chris Race at [email protected]
For more information and to apply, see: https://www.findaphd.com/
For informal enquiries contact: Dr Chris Race at [email protected]