3 dagen geleden - Universiteit van Amsterdam (UvA) - Amsterdam
The Amsterdam School for Cultural Analysis, one of six research schools of the Faculty of Humanities, has a vacant PhD position as part of the ERC …
Metallurgical process, computational fluid dynamics
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 an MSc in Materials Science or Physics or the equivalent, and 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 good communication skills (high degree of English proficiency).
• Knowledge of modern software design, specifically related to Python, is a plus.
• Be self-motivated, scientifically curious, and 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.
As a PhD candidate you 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 graduateschool.tudelft.nl/ for more information.
For more information about this position, please contact dr M. (Marcel) H.F. Sluiter, e-mail: email@example.com.
Minds for Innovation is the recruitment partner for this project. For more information regarding recruitment please contact Jolanda de Roo, e-mail: firstname.lastname@example.org, tel.: + 316 572 913 50.
To apply, please visit the website www.m4i.nl/vacancies/ and use M4i's online application tool by 31 July 2018. When applying for this position, please refer to vacancy number 3ME17-79 in the subject of your motivation letter.
Jolanda de Roo
Technische Universiteit Delft
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