Offres de Thèses et Postdocs

The School of Science and Engineering at Reykjabik University (Iceland) is looking for a PhD student to work on the project “Lagrangian Dispersion in Mixed Convective Turbulence “.

This thesis will focus on internal tidal waves (at diurnal and semi-diurnal frequencies), and may further extend to other type of ocean internal wave (e.g. near-inertial waves).

Multiple PhD positions are available at Eindhoven University of Technology in the field of turbulence research.

The aim of the present project is to develop an efficient solid wall treatment for LBM, including turbulent wall models. Several issues will be addressed, among which:• interpolation methods used within the Immersed-Boundary Method (IBM) or Cartesian meshes framework for solid boundary treatment [1-2]• use of a turbulent wall model in conjunction with immersed solid boundaries [3]• reconstruction of the density distribution functions of LBM at boundaries nodeswithin this framework (accuracy, mass conservation, ...)• reconstruction of macroscopic quantities on the solid surface for post-processing purposes (eg. calculation of aerodynamic forces applied on an airfoil)• extension to moving/deforming solid boundaries

In atmospheric clouds, in industrial power plants, and in the pandemic we have been living, a crucial element is the interaction of small particles with the turbulent flow that carries them. Will two ice crystals collide and aggregate to form a snowflake? Will an ocean wave break and splash droplets in the atmosphere? How far will a drop of saliva travel in air? Particle-laden turbulent flows are ubiquitous and extremely challenging to predict: the chaotic motion of the fluid is intertwined with the particles’ ability to exchange momentum and energy at the microscopic scale, while also altering the flow globally.

The objective of this postdoc position is to improve the CYCLONE library developed by RTECH, possibly by incorporating a different type of Immersed Boundary Method (IBM), such as the sharp interface method, which is presently under investigation at Pprime Institute.

TWe are currently offering two PhD positions: Experimental measuremenst of multiphase flows and numerical simulations of multiphase flows.The official advertisement will be opened soon, but inquiries can be sent to Mrs. Müllner at christina.muellner@tuwien.ac.at

Two years post-doctoral position on experimental/numerical fluid dynamics with applications to sub-surface oceans in icy moons. Collaboration with LPG Nantes and IPG Paris within the ANR COLOSSe project. Focus on mixing induced by topography in a stratified fluid.

A postdoctoral position is open to study experimentally the statistical properties of turbulence in a stratified medium in the Coriolis facility.

Dans le contexte de changement climatique, il est parfois difficile de distinguer les influences anthropiques, de la variabilité naturelle, tant celle-ci est complexe et pourtant si importante qu’elle peut masquer les effets des activités humaines sur le climat. C’est notamment le cas pour la variabilité de la circulation atmosphérique, que les modèles numériques actuels ne reproduisent pas correctement. L’une des raisons est l’impossibilité pour ces modèles de simuler les processus physiques sur l’ensemble des échelles spatio-temporelles où elles se produisent et notamment comment différents processus à différentes échelles interagissent. Le but de la thèse est de comprendre le lien entre les grandes échelles spatio-temporelles des mouvements cohérents de l’atmosphère et les statistiques à l'échelle régionale des différents champs turbulents (vitesse, température, humidité, précipitations), et notamment les événements rares ou extrêmes.

The objective of this postdoc is to study the importance of the fluid miscibility on the RTI dynamics using highly resolved simulations with the pseudo-spectral code STRATOSPEC . A diffuse interface method relying on the Cahn-Hilliart equations will be coupled to the Navier-Stokes solver. To this aim, we propose to assess the efficiency of various semi- or fully implicit numerical methods. Computational resources for the simulations will be provided through the TGCC supercomputers. Different theories and models will be proposed to interpret the results.

The aim of the PhD project is to understand the dynamics of jets hitting a surface, using a combination of experimental, numerical and theoretical approaches.

A goal of this research project is to propose solutions to improve the performance of heat exchang-ers by using acombination of active control strategiestomaximise heat-transfer capacityalong the interface fluid/structureandminimise the pressure loss due to friction betweenthe fluid and the wall.

The Fluid Mechanics Laboratory of Lille (CNRS-LMFL) is seeking a highly qualified candidate for a post-doctoral research fellowship to participate in ERC Advanced Grant research project NoStaHo which is aimed at an extensively transformative fundamental understanding and theory of non-stationary and/or non-homogeneous turbulence.The candidat will participate in the experimental side of the project by contributing to (i) Particle Image velocimetry and Hot Wire Anemometry measurements and (ii) various ways to analyse the data obtained for a variety of turbulent flows including various types of turbulent wakes, jets and boundary layers.

Turbulence et circulations à résolution décamétrique dans les nuages convectifsDans le cadre de cette thèse, nous souhaitons étudier les processus physiques à l’interface nuageuse à l'aide de simulations à très fine résolution (LES décamétrique) avec le modèle Méso-NH (Lac et al, 2018). L'objectif est de caractériser les circulations au sein des nuages convectifs (du cumulus au cumulonimbus), ainsi que les structures turbulentes associées.

L'objectif de ce post-doctorat est de réaliser et d'exploiter des simulations directes de turbulence compressible homogène avec forçage, de façon à mettre en évidence les propriétés inertielles de la circulicité.

The influence of rain on flows around vehicles is a new emerging theme at LMFL. A crucial aspect of the physics that determines both the mean drag and its fluctuations concerns the sharp and dynamic interface that exists in turbulent sheared flows between the turbulent and non-turbulent parts of the flow. An important aspect of the influence of rain is the interaction between droplets and this interface, and this aspect is in fact of more general importance as it is present in other situations (environmental dispersion, clouds, etc).

The present project is at the interface between Maths and Physics and aims at providing accurate, efficient and predictive modeling tools for the transport of particles and pollutants in turbulent conditions as encountered in natural atmospheric and oceanic conditions.

The proposed research aims at studying the dynamics of smoke vortices (very recently discovered in the stratosphere) by the mean of numerical simulations with an adapted version of the Weather Research and Forecast model. The postdoc will explore idealized flow configurations but also perform realistic case studies, which will be confronted to observational data from the 2020 and 2017 cases. This work will be carried out in close collaboration with other team members involved in observational and impact studies, theoretical modeling and laboratory experiments.

The design and operation of future large wind turbines require an in-depth understanding of the interaction between the rotor and the turbulent wind in the atmosphere. To this end, we will scan the entire wind field in front of, at and close behind two multi-megawatt wind turbines with a high spatial and temporal resolution, using the laser-based scanning technique lidar (light detection and ranging).Our two short-range WindScanners are continuous-wave Doppler wind lidars. They remotely measure the wind speed component along the beam direction (line-of-sight wind speed) at several hundred positions each second. A wind field can be reconstructed using these point measurements and additional meteorological information. In this project, particular focus will lie on wind field reconstructions using novel stochastic methods, which consider statistical features of small-scale turbulence.

The work of the candidate will aim for the development of the following tasks:1. Implementation of data streaming machine learning techniques within the C++ software developed by the research group, aiming to reconstruct an augmented IBM formalism. The AI learning procedure will be fed by online data generated by the MGEnKF.2. The performance of the code will be assessed via the analysis of progressively more complex scale-resolved turbulent flows. The test cases investigated include the turbulent channel flow and, in case of success, complex test cases such as a radial pump will be investigated. For every test case considered, high fidelity DNS / experimental data are already available from previous analyses of the research group.

This project is multidisciplinary and focuses on the development of new Deep Learning models for non-linear multiscale characterization with applications in fluid turbulence, remote sensing images and the combination of both to study ocean dynamics.

La convection thermique turbulente constitue un problème omniprésent dans de nombreuses situations naturelles. Elle peut être modélisée en laboratoire par la cellule de Rayleigh-Bénard. Il s’agit d’une couche de fluide confinée entre deux plaques horizontales. Le fluide est chauffé par le bas, et refroidi par le haut. Les couches limites thermiques se forment au voisinage des plaques. L’instabilité de flottabilité conduit à la germination de structures cohérentes, applées panaches thermiques. Ces panaches et leur interaction donnent naissance à un mouvement global (la circulation grande échelle). Lorsque le forçage est suffisamment intense, l’écoulement peut devenir turbulent, et le comportement global est déterminé par la compétition entre les effets purement inertiels, le forçage thermique local et l’interaction entre panaches.

At very high Rayleigh numbers, a very intense heat transfer regime appears for which the triggering mechanisms are still poorly understood. Using HPC numerical simulations and physics-guided machine learning techniques, we seek to extract from data physical information bringing to light the multi-scale interactions between different turbulent flow structures.

How do rain droplets form in clouds? How pollutants, viruses or volcano ashes disperse in the atmosphere? Where can they fall and at which speed? How plankton dynamics is affected by sea conditions? What are the consequences on the trophic chain? What are the optimal parameters for a wastewater treatment plant, for an engine, a chemical reactor? All these questions find answers in the study of inertial particles in turbulence. Such particle laden flows are ubiquitous but so complex to study due to the very large number of intertwined control parameters and the lack of theoretical predictions.