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: Fluid mechanics




Potential of wind and solar resources and macroeconomic implications of the energy transition
Researcher: Elise Dupont
Supervisor(s): Hervé Jeanmart

I am working on the link between energy availability and accessibility and economic growth. To do so, I study the concept of Energy Return on Investment (EROI), which is the ratio of the energy that is produced by an energy conversion device throughout its lifetime to all the energy inputs that were invested from the extraction of raw materials to the end-of-life treatment of the facility. It is the best indicator to assess the quality and sustainability of an energy project, without any economic distorsion. Easy access to high EROI resources allowed our modern societies to develop their economic activities. However, even taking into account the technological progress, the amount of high EROI resources is decreasing because : (i) EROI of fossil fuels is declining over time, (ii) renewable alternatives have lower EROIs than traditional fossil fuels and (iii) EROI of renewable alternatives is declining with their spatial expansion.

I am developing a methodology to estimate the dynamic function for the evolution of the EROI of different renewable energy sources (wind, solar and biomass) with the cumulated annual production, in order to be able to accurately estimate the evolution of the EROI of the future energy system.



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.



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.



Efficient and scalable frameworks for PDE simulations
Researcher: Thomas Gillis
Supervisor(s): Philippe Chatelain

focuses his research on the development of efficient and scalable computational framework for the simulation of 3D PDEs on massively parallel and heterogeneous architectures.



Techno-economic viability of variable-speed pumped-storage hydropower based on centrifugal pumps used as turbines
Researcher: Thomas Mercier
Supervisor(s): Emmanuel De Jaeger

This research takes place in the frame of SmartWater, a 3.5-year research project funded by the Walloon region, Belgium, and whose goal is to investigate the conversion of former mines and quarries into pumped-storage hydropower (PSH) sites, taking advantage of existing cavities. The project involves several academic and industrial partners, among which Laborelec, Electrabel and Cofely, as well as sponsors, including Ores, Elia, Charmeuse and Ensival-Moret. The SmartWater project is divided in several work packages, ranging from the geological study of potential mines and quarries, to the economical and electromechanical aspects of pumped-storage hydropower.



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.



Modèle hybride multi- échelle pour l’ étude rh éologique des solutions de macromolécules
Researcher: Nathan Coppin
Supervisor(s): Vincent Legat

graduated in physical engineering at Université Catholique de Louvain in 2018 and is currently pursuing a PhD under the supervision of Prof. Vincent Legat. The goal of his thesis is to study the performance of the MigFlow Software using applications that require the management of frictional contacts.



Numerical Modeling and Simulation of Sediment Mobilisation and Transport due to Turbulent Currents
Researcher: Anouk Riffard
Supervisor(s): Miltiadis Papalexandris

The proposed doctoral research evolves around two principal axes. The first one is the development of mathematical models and algorithms for flows of fluid-solid particles mixtures, i.e. granular suspensions. The second one is the use of these algorithms for the study of sediment mobilisation and transport due to turbulent currents.



Development of high-fidelity numerical methods for the simulation of the aerothermal ablation of space debris during atmospheric entry
Researcher: David Henneaux
Supervisor(s): Philippe Chatelain

This project, lead in collabaration with the von Karman Institute (VKI) and Cenaero, aims at developing high-fidelity numerical methods for the simulation of the aerothermal ablation of space debris during an atmospheric entry.

The number of space debris orbiting the Earth is becoming increasingly problematic for the integrity of operational satellites and the future access to space. The many space debris mitigation projects currently under study require an accurate prediction of the degradation of these objects when they re-enter the atmosphere in order to comply with the severe re-entry safety requirements.

Dedicated engineering softwares are used to assess the survivability of these debris. However, the correlation-based models implemented in these software lack accuracy and they do not allow to gain insight into the complex flow phenomena taking place near the surface of the body, yet essential for the conception of new satellites designed for demise. That is why CFD methods are needed to study this complex situation. But the methods currently available rely on simplifying assumptions that compromise the reliability of the results.

The objective of this project is to develop new high-fidelity numerical methods able to deal with the presence of the three phases in the same domain and their complex interactions. They will be grouped into the ARGO code under development at CENAERO, VKI, and UCL, which relies on the discontinuous Galerkin method. To do so, a highly-accurate multiphase method coupled with evaporation and surface tension models and based on a sharp interface approach will be employed for the treatment of the gas-liquid interface, while a state of the art melting method accounting for the diffuse character of the liquid-solid interface will be considered. Both methods will be built to work with multicomponent compressible equations. The code will then be validated with experimental data from the VKI Plasmatron facility.



Morphological impact of dam-flushing
Researcher: Robin Meurice
Supervisor(s): Sandra Soares Frazao

An important number of dams worldwide face sedimentation issues, leading to a decrease in their reservoir capacity and hence, many difficulties to properly satisfy to their different functions (e.g. water distribution, flood management, hydroelectricity production). To overcome these problems, we can proceed to dam flushing operations, transporting huge amounts of sediment downstream of the dam. Nevertheless, these operations can be harmful to the environment, the living organisms and the human infrastructures if not properly handled. For that reason, this thesis aims at developing a numerical model capable of accurately predicting the sediment deposition downstream of a dam after flushing operations. In order to do so, several mathematical models shall be implemented, among which a two-phase two-layer model, and laboratory experiments shall be run. The numerical model will then be confronted with the data collected from the experiments. Finally, the model will be tested with real-case data collected in situ near Lyon, France.



Improvement of gas quality in small-scale biomass gasification facilities through steam injection
Researcher: Arnaud Rouanet
Supervisor(s): Hervé Jeanmart

Biomass, as a renewable fuel, can be converted in a gasifier to produce a synthetic gas that is easier to transport and has a wider range of applications than solid biomass, including bio-fuels, chemicals or energy production.
In order to improve the quality of the produced gases, we will investigate how steam can be used instead of air as the oxidizing agent, to limit the syngas dilution with inert nitrogen and increase its heating value. The project will focus on improving an existing small-scale two-stage gasification unit owned by UCL, on which ad-hoc modifications will be brought and experimental campaigns will be performed.
Theoretical calculations and literature reviews will be performed to confirm and precise the potential for improvement of syngas composition. The design and ideal location of steam injection points will be studied, and experiments will be conducted on the modified gasifier to complement the theoretical calculations. Advanced tools and methods will be used for the characterisation of the syngas composition, to increase the accuracy of the experimental results. Finally, a numerical model of the gasification process will possibly come as complement for a more accurate prediction and confirmation of the experimental results.
This research project will take place in the frame of the project ENERBIO, in collaboration with ULB, UMons and CRA-W.



AI-based control policies towards efficient collective behaviours of flow agents and their application to fish schooling
Researcher: Denis Dumoulin
Supervisor(s): Philippe Chatelain

The principal objective is to shed light on mechanisms allowing anguiliform swimmers to swim very efficiently either on their own or in group.
Simulations rely on an unsteady panel method with vortex shedding and on reinforcement learning.



Reconciling engineering models of ejectors with experiments and CFD using physics-informed machine learning
Researcher: Jan Van den Berghe
Supervisor(s): Yann Bartosiewicz

Although Computational Fluid Dynamics (CFD) have proven sufficiently accurate to analyse the complex flow fields in ejectors, their computational cost remains too high to drive the design and optimization phases at system scale operation.


In that case, Lumped Parametric Models (LPM) are vastly preferable because of their lower computational cost. LPM models are based on integral balances and usually 0D formulations from isentropic gas dynamics. However, the lumping of complex physical phenomena such as turbulent mixing, oblique shock patterns and shock-boundary layer interaction into LPMs requires several closure parameters such as isentropic efficiencies. Moreover, LPMs are designed to predict global quantities such as the entrainment ratio or efficiency but are unaware of the stream-wise evolution of local parameters such as velocity, pressure, or Mach number, which only CFD can access. Finally, none of the LPMs presented in the literature consider the problem of modelling ejectors in transient conditions, which can be of primary importance at system scale operation.


This thesis aims to bridge the gap between classic LPMs and CFD approaches by introducing a new family of self-calibrating 1D models that use physics informed machine learning. In particular, the proposed models will combine 1D and unsteady gas dynamics with closure parameters that depend on local variables and properties. The functional relation linking closure models and flow parameters will be encoded in the form of Artificial Neural Networks (ANNs), and their calibration (i.e., the training of the ANN) will be automatic and online, i.e., while data is progressively collected. The model and the automated calibration procedure will be tested on experimental and numerical data. Hence the machine learning techniques will be used here to help the physical model to be closed, i.e., where our knowledge of the governing equations reaches its limit to derive universal relations for exchange of mass, momentum and energy in complex situations.