Ongeveer 13 uur geleden - Technische Universiteit Delft (TUD) - Delft
We are looking for talented individuals with a strong background and track-record in chemical engineering who strive to bring innovations in the field. We …
Generic model to predict thermal oxidation of steel
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.
The current Tata Steel efforts are towards developing oxide behaviour modules (OBM) that can be incorporated on the existing control or predictive models at industrial on-line control model. The existing oxide growth predictions in steel are based on empirical calculation of a rate of growth. Hence, in the practice, only empirical results are used obtained from experiments for each steel composition and each oxidation condition.
This project is aimed at developing a generic thermal steel oxidation model. Including the effects on the internal and external oxidation behaviour of different steel phases and grain boundaries, and the effects of alloying elements. This model will govern the main features and phenomena occurring during manufacturing of strips. We will study/implement;
- the composition, sequence, and amount of oxides depending on the steel, temperature cycle, and gas phase composition.
- the oxidation-induced diffusion of elements in a multi-element and multi-phase alloy.
- the effects of alloying elements on the oxidation steels.
- the aspects of the steel microstructure, such as grain boundaries, on the oxidation behaviour.
The model will be experimentally validated for pure iron and iron–carbon alloys.
The PhD candidate should hold a Master degree in Physics, Chemistry, or Materials Science. Furthermore, the applicant should have:
• ambition to do scientific work at a high level, as well as a strong orientation on result and process.
• a strong affinity for physical-chemical modelling of solid materials and outstanding experimental competence to validate.
• experience in multiphysics programs (Comsol or other) and programming in MatLab.
• good communication skills (high degree of English proficiency) and the ability to work in a cross-organisational team, with industry stakeholders, academics and technicians.
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 A.J. (Amarante) Böttger, E: A.J.Bottger@tudelft.nl, T: +31 (15) 27 82243.
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.
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 3ME17-81 in the subject of your motivation letter.
Jolanda de Roo
Technische Universiteit Delft
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