PhD Position in Modelling and Fault Identification of Wind Turbine Components
Catholic University of Leuven
Belgium

Project

This PhD focuses on modelling and fault identification of wind turbine components by making use of virtual sensing strategies based on small scale lab tests. For wind turbine applications, there is a trend towards upscaling the energy capacity which puts more stringent requirements on the design. Harvesting more energy from the wind implies amongst other larger blades, gearboxes and bearings that are more susceptive to faults such as cracks and loading induced fatigue. High fidelity drivetrain models can be deployed to represent the loading conditions of such systems and predict the dynamics if a particular fault is induced. As the exact location where a possible fault (such as a crack) can occur is not a priori known, it is challenging to identify which fault model is linked to the actual (faulty) operating condition of a component, based on only a limited measurement set. The PhD has the ambition to identify faults by deploying high fidelity models and a measurement sets through state estimation and compressive sensing techniques. The use of those techniques is beneficial as state estimation allows for a minimal plant-model mismatch at every time instant and compressive sensing helps to identify possible faults among a broad space of options.  

As a PhD researcher your aim is to develop high-fidelity drivetrain models making use of multibody formulations and dedicated contact mechanics descriptions with their corresponding model reduction techniques. In addition, by deploying time domain identification techniques developed within the research group, you will be bridging the gap between experimental analysis and numerical design. You are passionate to work on engineering solutions that stimulate a greener way of energy supply and are eager to combine research with industrial cooperation.

Profile

If you recognize yourself in the story below, then you have the profile that fits the project and the research group.

• I have a master degree in engineering, physics or mathematics and performed above average in comparison to my peers.
• I am proficient in written and spoken English.
• I have some basic experience with first-principle modelling (1D/3D modelling), model identification (or numerical optimization in general) and/or I have a profound interest in these topics.
• As a PhD researcher of the KU Leuven Noise and Vibration Research Group I perform research in a structured and scientifically sound manner. I read technical papers, understand the nuances between different theories and implement and improve methodologies myself.
• Based on interactions and discussions with my supervisors and the colleagues in my team, I set up and update a plan of approach for the upcoming 1 to 3 months to work towards my research goals. I work with a sufficient degree of independence to follow my plan and achieve the goals. I indicate timely when deviations of the plan are required, if goals cannot be met or if I want to discuss intermediate results or issues.
• In frequent reporting, varying between weekly to monthly, I show the results that I have obtained and I give a well-founded interpretation of those results. I iterate on my work and my approach based on the feedback of my supervisors which steer the direction of my research.
• I value being part of a large research group which is well connected to the machine and transportation industry and Iam eager to learn how academic research can be linked to industrial innovation roadmaps.
• During my PhD I want to grow towards following up the project that I am involved in and representing the research group on project meetings or conferences. I see these events as an occasion to disseminatemy work to an audience of international experts and research colleagues, and to learn about the larger context of my research and the research project.

Offer

• A remuneration package competitive with industry standards in Belgium, a country with a high quality of life and excellent health care system.
• An opportunity to pursue a PhD in Mechanical Engineering, typically a 4 year trajectory, in a stimulating and ambitious research environment. 
• Ample occasions to develop yourself in a scientific and/or an industrial direction. Besides opportunities offered by the research group, further doctoral training for PhD candidates is provided in the framework of the KU Leuven Arenberg Doctoral School (https://set.kuleuven.be/phd), known for its strong focus on both future scientists and scientifically trained professionals who will valorise their doctoral expertise and competences in a non-academic context. More information on the training opportunities can be found on the following link: https://set.kuleuven.be/phd/dopl/whytraining.
• A stay in a vibrant environment in the hearth of Europe. The university is located in Leuven, a town of approximately 100000 inhabitants, located close to Brussels (25km), and 20 minutes by train from Brussels International Airport. This strategic positioning and the strong presence of the university, international research centers, and industry, lead to a safe town with high quality of life, welcome to non-Dutch speaking people and with ample opportunities for social and sport activities. The mixture of cultures and research fields are some of the ingredients making the university of Leuven the most innovative university in Europe (https://nieuws.kuleuven.be/en/content/2018/ku-leuven-once-again-tops-reuters-ranking-of-europes-most-innovative-universities). Further information can be found on the website of the university: https://www.kuleuven.be/english/living

Interested?

For more information please contact Mr. Matteo Kirchner, tel.: +32 16 33 03 66, mail: matteo.kirchner@kuleuven.be or Mr. Bart Blockmans, tel.: +32 16 37 28 09, mail: bart.blockmans@kuleuven.be.

If you apply for this position please say you saw it on Engineeroxy