In steel precipitates preferentially nucleate on interfaces and dislocations. This dramatically affects the mobility of the interfaces and dislocations. Physical models for microstructure evolution, which are critical for optimal control of high quality steel production, therefore, nowadays are integrated models of precipitation, recovery and recrystallization. In these models both the size and fraction of the precipitates play an important role. Precipitate sizes and fractions evolve with time usually are described using LSW theory or cluster dynamics, or on empirical- and ab initio based kinetic Monte Carlo (KMC) simulations. Recently, progress has been made to integrate precipitate size distribution/evolution models with models for recovery and recrystallization. Combining all the various processes involving precipitation, dislocation motion & deformation, and phase transformations is a great challenge, even more so because the aim is to achieve this modelling in approximate real-time so that it can be used to steer and fine-tune the steel production process.
The Department of Materials Science and Engineering (MSE) of the Delft University of Technology, undertakes coherent and innovative research dedicated to developing, producing, characterising and manipulating materials, with a focus on metals. The aim is to develop fundamental understanding forming the basis for better performance, a longer life cycle with preservation of functionality, while at the same time optimising sustainable use of resources and minimising environmental footprint.
The Virtual Materials and Mechanics programme is focused on applying advanced computational methods to help solving engineering problems of scientific interest and societal importance, by studying materials behaviour under specialized, controlled conditions and by designing new materials and processing techniques. For more information: https://www.tudelft.nl/en/3me/departments/materials-science-and-engineering/research/computational/
This PhD project forms part of the Digitally Enhanced New Steel Product Development (DENS) program, in which Tata Steel Europe, Materials innovation institute (M2i) and several academic partners collaborate to enable the development of new generations of advanced materials for e.g. the automotive industry.
Digitally Enhanced New Steel Product Development (DENS) program
Significant progress has been made in the past decades in the development of advanced models that describe the behavior of steel during processing and subsequent applications. However, the quantitative application of through process models in new steel product development still lacks predictivity. In modern steel grades, parameters of the steel production process have a significant influence on the final material properties. Furthermore, the trend towards complex multi-phase microstructures requires very sophisticated models to describe their mechanical properties in a predictive manner. The main scientific challenge addressed in this program is to integrate state-of-the-art models in a single through process model framework that can be applied in practice for new steel product development.
Physical models for microstructure evolution, which are critical for optimal control of high-quality steel production are currently integrated models of precipitation, recovery, and recrystallisation. Precipitate sizes and fractions evolve with time and are usually described using LSW theory or cluster dynamics, or with empirical- and ab initio-based kinetic Monte Carlo (KMC) simulations. Recently, progress has been made to integrate precipitate size distribution/evolution models with models for recovery and recrystallisation. Combining all the various processes involving precipitation, dislocation motion and deformation, and phase transformations is a great challenge.
Our aim is modelling in approximate real time so that it can be used to steer and fine-tune the steel production process. Therefore, it is necessary to extract properties such as:
- bulk thermodynamic stabilities of precipitating phases and solid solutions,
- diffusivities, in bulk, at grain boundaries, and at dislocations,
- interfacial energies in the presence of segregation and precipitate growth and dissolution,
- homogeneous and localized precipitate nucleation probabilities.
The ultimate benefit is a realistic predictive description of microstructure development during thermos-mechanical treatment of steel.
The PhD student must have a MSc in Materials Science or Physics or equivalent with a sound understanding of thermodynamics and kinetics of phase transformations and computer modelling. Furthermore, an applicant should:
- Have ability to work in a team and have good communication skills (high English proficiency)
- Knowledge of modern software design, specifically related to python is a plus
- Be self-motivated, be scientifically curious, and be able to work with little direct supervision
The TU Delft offers a customisable compensation package, a discount for health insurance and sport memberships, and a monthly work costs contribution. Flexible work schedules can be arranged. An International Children’s Centre offers childcare and an international primary school. Dual Career Services offers support to accompanying partners. Salary and benefits are in accordance with the Collective Labour Agreement for Dutch Universities.
The PhD. candidate will be enrolled in the TU Delft Graduate School. The TU Delft Graduate School provides an inspiring research environment; an excellent team of supervisors, academic staff and a mentor; and a Doctoral Education Programme aimed at developing your transferable, discipline-related and research skills. Please visit www.phd.tudelft.nl for more information.
Minds for Innovation is the recruitment partner for this project. For more information regarding recruitment please contact Jolanda de Roo. E: email@example.com, T: + 316 572 913 50
For more information on the project specifics, please contact Dr. Marcel Sluiter. E: M.H.F.Sluiter@tudelft.nl
Application documents (in PDF format) must contain:
- letter of motivation containing why you are interested in this specific project and why you would be a good candidate (refer to vacancy number 3ME17-79 in the subject)
- detailed curriculum vitae and contact information of two potential referees
- transcripts of BSc and MSc grades
- transcript of English test
Apply before the deadline of July 31st 2018
As one of the first selection steps, you will be invited to participate on our specialised online assessment. If you would like to know more about why we use this and how we have set it up, then check out our website.