3 dagen geleden - Radboud Universiteit (RU) - Nijmegen
PhD Position Environmental Cost Indicator of Rolling Resistance(ECIRR): Monitoring & Modeling
Challenge: To improve environmental cost of rolling resistanceChange: Data analysisImpact: Better estimation of RR's impact on environment
- Mekelweg, Delft, Zuid-Holland
- Tijdelijk contract / Tijdelijke opdracht
- Uren per week:
- 38 - 40 uur
- € 2395 - € 3061 per maand
The PhD topic aims to address the issue of adequately quantifying cost of rolling resistance to the environment particularly under the Netherlands condition with the ultimate aim to reduce the carbon-footprint. Rolling resistance is directly linked to fuel consumption and hence carbon-footprint. The goal of the PhD topic in a way supports achieving the targets as defined in the Climate Treaty of Paris for creating a sustainable society. The PhD topic also focuses on monitoring of dedicated field sections with currently available facilities in parallel with developing a tool that aims to use the model-framework capable of explaining and giving deeper insight of rolling resistance's impact on environment. Moreover, classical data-analyses in combination with machine learning tools will be utilized as an interface to transfer the existing knowledge(database) to improve the environmental cost estimation of rolling resistance.
The knowledge of improved ECI could significantly impact the design and maintenance of asphalt pavements, life-cycle cost analysis and the development of low rolling resistant asphalt top layers. However, coming up with an accessible and robust ECI is a complex process since the rolling resistance is significantly affected by various factors related to traffic, road design, environment, vehicle class, tyre type and pavement condition. Although past research studies and projects developed statistics-based relationships, they are not directly applicable under the Netherlands road conditions. Therefore measurements were carried out on some national roads out in the recent past. These data can be a solid and sound base for deepening the knowledge on rolling resistance and fuel consumption. The candidate in his initial years is supposed to use the available data and data analysis techniques to identify key factors affecting Rolling resistance. In the later years, the candidate is supposed to be involved in conducting field tests and utilize gained knowledge to develop a robust numerical tool which could help in getting state-of-the-art ECI for the Netherlands.
One of the research output is to develop an accessible and understandable tool to quantify the contribution of Rolling Resistance in the Environmental Cost Indicator (ECIRR) for different asphalt top layers. This tool will consider factors that can be directly affected by tyre properties, driving speed, wind, spinneys, baffle boards, and also by pavement related factors such as International Roughness Index (IRI), mean profile depth, road design, bearing capacity, etc. This information should enable deeper understanding of the relations between rolling resistance and surface characteristics by monitoring dedicated field sections with currently available facilities in parallel with developing a tool that aims to use the model-framework capable of explaining and giving deeper insight of rolling resistance's impact on environment. Moreover, classical data-analyses in combination with machine learning tools will be utilized as an interface to transfer the existing knowledge (database) to improve the environmental cost estimation of rolling resistance. Furthermore, when deviations are seen in field performance, the gained knowledge will be used to identify the "cause and use" to define where more detailed knowledge is required.
Your PhD promotor will be Prof. Sandra Erkens. During your PhD period you are expected to interact with other (inter)national researchers and experts from Rijkswaterstaat and TNO.
- An MSc degree in the areas of Civil Engineering, Computational Materials Science, Applied Mathematics, Mechanical Engineering, Computer Science, or other related areas.
- Knowledge of basic material modelling, data analysis, continuum and discrete mathematical formulations, numerical skills and programming skills (Fortran / C++ / Python).
- Independent, self-motivated, eager to learn and open to communicate and collaborate with peers.
- Experience with advanced pavement material modeling, monitoring & field testing, data analysis, and machine learning/artificial intelligence is a plus.
- Good command of English language, both verbally and in writing. If your mother tongue is not English and you do not hold a degree from an institution in which English is the official language of instruction, you must submit proof of English proficiency from either TOEFL (minimum total score of 100) or IELTS (minimum total score of 7.0). Proof of English language proficiency certificates older than two years are not accepted.
TU Delft offers PhD-candidates a 4-year contract, with an official go/no go progress assessment after one year. Salary and benefits are in accordance with the Collective Labour Agreement for Dutch Universities, increasing from € 2395 per month in the first year to € 3061 in the fourth year. As a PhD candidate you will be enrolled in the TU Delft Graduate School. The TU Delft Graduate School provides an inspiring research environment with an excellent team of supervisors, academic staff and a mentor. The Doctoral Education Programme is aimed at developing your transferable, discipline-related and research skills.
The TU Delft offers a customisable compensation package, discounts on health insurance and sport memberships, and a monthly work costs contribution. Flexible work schedules can be arranged. For international applicants we offer the Coming to Delft Service and Partner Career Advice to assist you with your relocation.
For information about this vacancy, you can contact Dr. K. (Kumar) Anupam, Assistant Professor, email: K.Anupam@tudelft.nl, tel: 0031 (0)15 27 82394.
For information about the selection procedure, please contact Claudia Baltussen, Group Secretary, email: C.Y.Baltussen@tudelft.nl.