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.
- During my courses or prior professional activities, I have gathered some basic experience with numerical modelling and numerical integration and/or I have a profound interest in these topics.
- As a PhD researcher of the KU Leuven LMSD and NUMA division, 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 feel comfortable to work as a team member and I am eager to share my results to inspire and being inspired by my colleagues.
- I value being part of a large research group which is well connected to the machine and transportation industry and I am 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 disseminate my work to an audience of international experts and research colleagues, and to learn about the larger context of my research and the research project.
This PhD is part of a WEAVE project which aims to extend the Wave Based Method, which is a Trefftz approach, providing an efficient alternative to the Finite Element Method to the time domain. Vibro-acoustic analysis is most commonly performed in the frequency domain, implying the assumption of steady-state conditions and time-harmonic excitation. Many problems, however, involve transient
rather than time-harmonic signals. Many dedicated applications, like non-destructive evaluation, health monitoring, auralization and virtual sensing call for time domain approaches. Transient vibro-acoustic
analysis poses specific challenges: a wide range of frequencies can be present and the temporal coordinate needs to be estimated very accurately. Current time-domain methods quickly become time and memory consuming. Within this project, the Wave Based Method (WBM), a Trefftz-based approach, will be extended for time-domain analysis. As compared to standard element-based approaches, the
WBM applies exact solutions of the governing problem to approximate the solution field and thus embeds physical information of the problem. Consequently, the resulting system of equations is
typically small and a high convergence rate is obtained. Switching to the time domain, however, comes with major challenges. First, different time discretization schemes that provide stability, accuracy
and efficiency should be developed and evaluated. Second, particular solutions of the governing equations with arbitrary righthand sides need to be determined. Finally, these methods are
combined within the WBM with a proper selection for the wave function set for the considered physical problems at hand.
As a researcher you develop particular solutions of inhomogeneous (bi-)Helmholtz equations, for which approximate numerical techniques will have to be developed. These solutions will be combined with efficient time-stepping or transformation methods to obtain efficient solutions of acoustic, structural and fully coupled vibro-acoustic models. A research stay at TU Graz will also be included.
The research is hosted by the Mecha(tro)nic System Dynamics division (LMSD), which currently counts >100 researchers and is part of the department of mechanical engineering of KU Leuven. KU Leuven features consistently in Europe's top-15 universities within the Times Higher Education ranking. Worldwide our university ranks #45 in the 2024 edition of the same ranking. The research group itself 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.
The KU Leuven Mecha(tro)nic System Dynamics division (LMSD) of the Department of Mechanical Engineering and the NUMA division of the Department of Computer Science are jointly searching for a research engineer to join their teams to work within the exciting WEAVE project “Wave Based Method for time domain analysis of acoustic, structural and vibro-acoustic problems” in collaboration with TU Graz.
- 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.