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 time domain identification methods for modelling mechatronic systems. Standard methods focus on optimizing parameters by either minimizing the discrepancy between the time history of the measurement signal and the model output, or by optimizing towards a certain global criteria, i.e.,shifting time, transmission error in an ad hoc fashion. This often results in a fit based on the training data, and does not necessarily offer good predicting capabilities for operating regimes outside the regimes of the training set. Consequently,there is a clear need for novel identification strategies that not only optimize the parameters but also iterate on the equation structure and finally indicate which equations are responsible for the remaining model mismatch. An additional challenge is linked to incorporating different measurement sets and different operational conditions in a single identification step. The focus is on powertrains where the distributed flexibility is dominant and several non-linear aspects are present, i.e.,velocity-dependent stiffening effects, Coriolis forces due to bending loads,load-dependent bearing stiffness’s, etc. The result will be a pre-test,correlation and validation approach for non-linear systems.
As a PhD researcher your aim is to develop high-fidelity models with dedicated modular equation structure and to link them with novel identification strategies. As the space of unknown parameters is typically very large and the operating regimes include very non linear phenomena, a strong emphasis will be dedicated on computationally efficient strategies. You will deal with the trade-off between the accurate and rigorous modelling formulations, keeping in mind the final application goal of the model.
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 must 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 –email@example.com. 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
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|Title||Time Domain-based Model Correlation and Validation Techniques for Mechatronic Applications|
|Job location||Oude Markt 13, 3000 Leuven|
|Published||November 19, 2019|
|Application deadline||January 31, 2020|
|Job types||PhD  |
|Fields||Mechatronics,   Industrial Engineering,   Applied Mathematics,   Mechanical Engineering,   Mechanics  |