Ongoing research projects

IMMC

Ongoing research projects in iMMC (June 2022)


This a short description of research projects which are presently under progress in iMMC.
Hereunder, you may select one research direction or choose to apply another filter:

Biomedical engineering

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Civil and environmental engineering

Dynamical and electromechanical systems

Energy

Fluid mechanics

Processing and characterisation of materials

Chemical engineering

Solid mechanics


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List of projects related to: wake flows




Implementation of an incompressible hybrid Eulerian-Lagrangian external flow solver
Researcher: Philippe Billuart
Supervisor(s): Grégoire Winckelmans, Philippe Chatelain

Philippe Billuart is working on the development of a new numerical solver that will be able to solve accurately and efficiently any low Mach number external flows. His research is focusing on the hybrid Eulerian-Lagrangian solvers for the incompressible Navier-Stokes equations. Those approaches are based on the decomposition of the computational domain : an Eulerian grid-based solver is used for the computation of the near-wall region, while a Lagrangian vortex method solves the wake region. Even though the coupling of particle methods with Eulerian solvers is not new, only 3D weak coupling were developed so far. This thesis aims to develop a 3D strong coupling ; i.e. a coupling where the Schwarz iterations are not longer required to ensure consistent boundary conditions on each subdomain. As the Schwarz algorithm becomes expensive in 3D, the computational gain in the developed approach should be very significant.




Researcher: Denis-Gabriel Caprace
Supervisor(s): Grégoire Winckelmans

This research is about developing tools for wake flow analysis, and their application to rotorcraft and aircraft in formation flight.



WakeOpColl
Researcher: Marion Coquelet
Supervisor(s): Philippe Chatelain

Marion Coquelet is part of the ERC-granted project WakeOpColl, which focuses on learning and collective intelligence for optimized operations in wake flows. Her contribution is related to the control of wind turbines using artificial intelligence. One of the questions to be answered is how a wind turbine in a farm can learn and organize itself in order to maximize the global production of the farm, but also to limit the fatigue loads experienced by its blades.



She is interested in flow estimation using blades as sensors, in individual pitch control for wind turbine load alleviation and in wake mitigation strategies aiming at wind farm power maximization. She uses numerical simulations along with data assimilation and machine learning tools to tackle these challenges.