The PhD is hosted by the KU Leuven Noise and Vibration Research Group, which currently counts 90 researchers and is headed by Prof. Wim Desmet (https://www.kuleuven.be/wieiswie/en/person/00011973) and is part of the Mechanical Engineering Department, a vibrant environment of more than 300 researchers. The research group has a long track record of combining excellent fundamental academic research with industrially relevant applications, leading to dissemination in both highly ranked academic journals as well as on industrial fora. More information on the research group can be found on the website: https://www.mech.kuleuven.be/en/research/mod/about and our Linked.In page: https://www.linkedin.com/showcase/noise-&-vibration-research-group/.
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.
If you recognize yourself in the story below, then you have the profile that fits the project and the research group.
To apply for this position, please follow the application tool and enclose:
1. full CV – mandatory
2. motivation letter – mandatory
3. full list of credits and grades of both BSc and MSc degrees (as well as their transcription to English if possible) – mandatory (when you haven’t finished your degree yet, just provide us with the partial list of already available credits and grades)
4. proof of English proficiency (TOEFL, IELTS, …) - if available
5. two reference letters - if available
6. an English version of MSc or PhD thesis, or of a recent publication or assignment- if available
Please keep in mind that these documents have to be in a pdf-format and can not be more than 4MB.
For more information about the vacancy, please contact Dr. Bert Pluymers by email – firstname.lastname@example.org. All applications should be done using the application tool.
You can apply for this job no later than January 31, 2020 via the online application tool
KU Leuven seeks to foster an environment where all talents can flourish, regardless of gender, age, cultural background, nationality or impairments. If you have any questions relating to accessibility or support, please contact us at diversiteit.HR@kuleuven.be.Continue reading
|Title||Modelling and Fault Identification of Wind Turbine Components|
|Job location||Oude Markt 13, 3000 Leuven|
|Published||November 19, 2019|
|Application deadline||January 31, 2020|
|Job types||PhD  |
|Fields||Industrial Engineering,   Analysis,   Applied Mathematics,   Mechanical Engineering,   Mechanics  |