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

Computational science

Civil and environmental engineering

Dynamical and electromechanical systems

Energy

Fluid mechanics

Processing and characterisation of materials

Chemical engineering

Solid mechanics


Research direction:
Listed keyword:
Other keyword:
Division:
Supervisor:

List of projects related to: aerodynamics




Modeling and simulation of water electrolysis.
Researcher: Christos Georgiadis
Supervisor(s): Joris Proost

The main objective of our work is to develop models for the simulation of 2-phase flows through electrodes. After the initial validation of the model, we will perform a detailed analysis of the flow and electrochemical properties of the system, in conjunction with experimental data. The final objective will be the design of optimal electrode geometries for water electrolysis.



Modelisation and optimization of bird flight
Researcher: Victor Colognesi
Supervisor(s): Philippe Chatelain, Renaud Ronsse

This research project aims at modeling and optimizing bird flight. The goal of this modelization is to get a deep understanding of the mechanisms that govern avian flight and the best way to understand it is to re-create it. That is, the flight will be modeled starting from the given anatomy of a bird and the kinematics will be the result of an optimization process aiming at the most optimal flight.
Compared to other existing studies on the subject of bird flight, this project will follow a "bottom-up" approach, all the way from muscle activation, up to the wing aerodynamics and gait optimization. This approach is necessary to be able to evaluate key values such as metabolic rates, ...
This will allow us to answer a few questions such as :
- What are the mechanisms enabling high efficiency in bird flight ?
- How do we achieve a stable flapping flight ?
This work is purely numerical. The bio-mechanical model of the bird is developed using the multi-body solver Robotran developed at UCL. This bio-mechanical model will be coupled to an aerodynamical model based on a vortex particle-mesh code (VPM) developed at UCL as well.



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.



Flight Control and Wake Characterization of Migratory Birds
Researcher: Gianmarco Ducci
Supervisor(s): Renaud Ronsse, Philippe Chatelain

The RevealFlight project aims at shedding light on the efficiency optimization mechanisms deployed by biological flyers, with a specific focus on migratory birds. The efficiency-seeking mechanisms will be sought through the numerical reproduction of flight that includes the morphology, the neuro-muscular configuration and the gait generation. This resulting gait then exploits aerodynamics at the scale of an individual (unsteady lift generation) and at the level of the flock (formation flight). This project thus proposes to synthesize the flight mechanics of birds into a unified framework, combining bio-mechanical, sensory, aerodynamic and social interaction models, in order to reproduce the flying gaits and the interactions within a flock.
A neuro-mechanical model of the birds is currently under development, capturing bio-inspired principles both in the wing bio-mechanics (e.g. structure and compliance) and in its coordinated control (through e.g. a network of coordinated oscillators). The dynamics of this model will be solved by means a multi-body solver and in turn, coupled to a massively parallel flow solver (an implementation of the Vortex Particle-Mesh method) in order to capture the bird’s wake up to the scales of the flock. The study of self-organization phenomena and inter-bird interactions are currently beginning on simple conceptual models, and will be gradually extended to more advanced models developed during the project. It will aim at comparing the efficiency of flocks of selfish flyers with that of flocks in which collaboration takes place, whether implicitly or explicitly.
In my global project picture, the following bottom-up strategy will be adopted:
- Wake characterization: This task studies the wake in terms of the vortex dynamics at play over long distances. The candidate will perform simulations of flying agents in long computational domains in order to capture the wake behavior (topology, instabilities and decay) over longer times and larger scales. This will provide another basis of validation of the project results, given the volume of work on bird wakes;
- Flight stabilization in turbulent or wake-impacted flow: This task aims at the realization of a stabilized flight within a perturbed flow. Two perturbations are envisioned: ambient turbulence and an analytical wake composed of two counter-rotating vortices. Il will Combine previously synthesized gaits and control schemes in order to study the stability of the flyer in a turbulent flow or inside a wake;
- Maneuvers: This task realizes the first maneuvers of the virtual flyer: avoidance and trajectory tracking that will be leveraged in the simulation of multiple flyers that need to interact and swap places. In the present task, this trajectory is still prescribed, in a step towards an autonomous decision-making agent. In order to realize maneuvers, this task implements a control layer above the controllers developed in earlier tasks. Complex maneuvers will be achieved by closing the loop between trajectory errors and the inputs of the lower level controller.