Data assimilation in fluid-structure interaction problems

This PhD position is supervised by Prof. Johan Meyers (Department of Mechanical Engineering), and co-supervised by Prof. Geert Lombaert (Department of Civil Engineering). The position is based in the Turbulent Flow Simulation and Optimization (TFSO) research in the department of Mechanical Engineering at the University of Leuven (KU Leuven). The research is part of the project “Computational methods for infinite-dimensional Bayesian inversion of physics-based models in engineering applications” led by Prof. Giovanni Samaey, Prof. Geert Lombaert, Prof. Dirk Nuyens and Prof. Johan Meyers.

Project

BACKGROUND
Large structures in the built environment such as skyscrapers, bridges, antenna towers, wind turbines, … are immersed in the turbulent atmospheric boundary layer. The interaction between this turbulent flow and the structure itself can lead to unsteady dynamic loading of the structure that can be detrimental for its life time, and needs to be considered in design. Nowadays, structures are often equipped with strain gauges and accelerometers that allow to register the structural response. At the same time, remote sensing techniques such as LIDAR exist that allow to measure some of the flow features. Integrating these types of measurements in a data-assimilation approach that includes both the flow and structure can provide valuable information on the full three-dimensional flow field and structural response. To date, such coupled fluid structure data-assimilation methods for applications in the atmospheric boundary layer are still largely unexplored.

PHD PROJECT DESCRIPTION
Research aims at the development of data-assimilation of structural response and LIDAR measurements in the atmospheric boundary layer into a coupled fluid–structure problem. We first focus on reconstructing turbulent flow features from structural responses in large towers, as well as in wind turbines, using them as a ‘flow sensor’. To this end,  a coupled inversion problem is formulated that builds on work related to 4DVar in the ABL, which has been performed earlier with the inhouse flow code SP-Wind (Bauweraerts & Meyers, J. Fluid Mech, 2021; Alreweny et al, J. Fluid Mech, 2024, in press), and couples this with the finite element code STABIL also developed at KU Leuven. Later additional flow measurements, such as LIDAR, will be integrated in the 4DVar approach.

Profile

Candidates have a master degree in one of the following or related fields: fluid mechanics, aerospace or mathematical engineering, numerical mathematics, mechanical engineering, civil engineering, or computational physics. They should have a good background or interest in fluid mechanics, optimization, simulation, and programming (Fortran, C/C++, Python, …). Proficiency in English is a requirement. The position adheres to the European policy of balanced ethnicity, age and gender. Persons of all origins and gender are encouraged to apply.

Offer

The PhD position lasts for the duration of four years, and is carried out at the University of Leuven. The candidate also takes up a limited amount (approx. 10% of the time) of teaching activities. The remuneration is generous and is in line with the standard KU Leuven rates. It consists of a net monthly salary of about 2400 Euro (in case of dependent children or spouse, the amount can be somewhat higher); social security is also included. Following Belgian law, the salary is automatically adjusted for inflation based on the smoothed health index.

Interested?

To apply, use the KU Leuven online application platform (applications by email are not considered). Applications should ideally include:
a) an academic CV and a PDF of your diplomas and transcript of course work and grades
b) a statement of research interests and career goals, indicating why you are interested in this position
c) a sample of technical writing in English, e.g. a paper with you as main author, or your bachelor or master thesis
d) at least one recommendation letter
d) a list of at least two additional references (different from recommendation letters): names, phone numbers, and email addresses
e) some proof of proficiency in English (e.g. language test results from TOEFL, IELTS, CAE, or CPE)

Please send your application as soon as possible.
Decision: as soon as a suitable candidate applies.
Starting Date: immediate start possible, preferably before October 1st 2024. Later start can be negotiated.

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