Researcher in Mechanical Engineering

Project

BACKGROUND
The surface–atmosphere exchanges of momentum, energy, water and trace gases are central components of the Earth system. Their quantification, the understanding of their variability and their modeling can unlock key scientific questions like the impact of ecosystems on climate change and air pollution, and their capacity to cope with the ongoing climatic and atmospheric composition modifications. Meeting these challenges increasingly relies on observations at the ecosystem plot level obtained at experimental sites equipped with flux towers, which are organized in continental networks (ICOS for Europe), and the global FLUXNET database. The cornerstone of these infrastructures is the single-point micrometeorological eddy-covariance (EC) equipment, allowing the estimation of surface fluxes at a spatial scale of a few hectares and with a time resolution below the hour. The eddy covariance method relies on the idea that in high turbulence conditions, and for sufficiently homogeneous terrain and flow conditions, the transport of CO2 (or likewise heat) is dominated by the vertical turbulent transport. The method thus fails when turbulence is low and advective fluxes are not negligible; this leads to the systematic underestimations of (i) the trace gas flux (e.g. CO2), in particular in atmospheric stable conditions, typical of cloud-free nights and of (ii) the available energy, in particular in atmospheric free convective conditions, typical of sunny windless days. These so-called night-flux problem and un-closure of the surface energy balance (SEB) have remained unsolved for the past 25 years and are currently handled through debated data filtering and gap-filling techniques weakening the method as a quantitative tool for long term (annual) quantification of the net GHG exchanges from ecosystems. The Agroflux project aims at a significant breakthrough in EC flux tower source reconstruction by leveraging and combining recent scientific and technological progresses in (I) measurements, (II) simulations and (III) inference and assimilation techniques.

Research: The research focuses on the development of physics based reduced order models for scalar and heat-flux closures in the atmospheric boundary layer. Model parameter calibration is based on the one hand on data from extensive experimental campaigns (provided by other researchers in the project), and on the other hand on detailed large-eddy simulations of non-homogeneous scenarios that are representative for situations when scalar and heat fluxes are not closed by the vertical turbulent flux only. To this end, the KU Leuven LES code SP-Wind is used. This is a simulation code that is routinely run on the high-performance infrastructure of the Flemish Supercomputer Center, and is efficiently parallelized up to 10000 cores. Finally, model inversion methods are developed, using the reduced order models, that leverage flux tower measurements in combination with new measurement techniques such as lidar measurements and drone measurements.

Profile

Candidates have a master degree in one of the following or related fields: fluid mechanics, aerospace or mathematical engineering, numerical mathematics, or computational physics. They should have a good background or interest in fluid mechanics, optimization, simulation, and programming (Fortran, C/C++, MATLAB, 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). 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) Please 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) two recommendation letters
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 September 1st 2023. Later start can be negotiated.