Ongoing research projects

IMMC

Ongoing research projects in iMMC (December 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


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List of ongoing projects in the division: TFL




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.



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.



BEST
Researcher: Véronique Dias
Supervisor(s): Hervé Jeanmart

obtained her PhD at UCLouvain in 2003, then worked as Postdoctoral Researcher at the Laboratoire de Physico-Chimie de la Combustion (Faculty of Science). In 2009, she moved to the Institute of Mechanics, Materials and Civil Engineering, and since 2012, she has a position of Research Associate. In 2015, she obtained her HDR (Habilitation à Diriger la Recherche) at the Université of Orléans (France).
Her research interests cover the combustion and kinetics of alternative fuels by the elaboration of kinetic models for hydrocarbons and oxygenated species. These projects in combustion include both experimental and numerical parts. They are contributions to the IEA (International Energy Agency) Implementing Agreement for Energy Conservation and Emission Reduction in Combustion.
In 2016-2018, Véronique Dias also worked on a project on energy storage, and more specifically, in chemical form. In the BEST project (2020-2024), she holds the management and coordination that support all the activities to be developed during the project by providing the necessary tools, methods and governing structure.
Since 2018, she has been the IMMC Research Coordinator for European projects on energy transition.



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 UCLouvain. This bio-mechanical model will be coupled to an aerodynamical model based on a vortex particle-mesh code (VPM) developed at UCLouvain as well.



Generating energy transition pathways- application to Belgium
Researcher: Gauthier Limpens
Supervisor(s): Hervé Jeanmart

The transition towards more sustainable, fossil-free energy systems is interlinked with a high penetration of stochastic renewables, such as wind and solar.
Integrating these new energy resources and technologies will lead to profound structural changes in energy systems, such as an increasing need for storage and a radical electrifcation of the heating and mobility sectors.
To capture the increasing complexity of such future energy systems, new
flexible and open-source optimization modelling tools are needed.In collaboration with EPFL (Ecole Polytechnique Fédérale de Lausanne), we develop EnergyScope, a new open-source energy model for strategic energy planning of urban and national energy systems.
We applied our methodolgy to Switzerland and Belgium. During the end of the thesis, we are developping a transition pathway model representing the transition from 2015 until a long term target (such as 2050) with intermediary steps. The technologies merit order and the total cost of the transition will be key results.
In addition, other studies are under investigation (by master thesis or myself) about more countries, a multi-cells versions, an urban version, model coupling (EnergyScope-DispaSET), create an educational interface for citizens and policy makers or apply the model for uncertainty characterisation.



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.



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 UCLouvain, 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.



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 UCLouvain, 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.



Robust optimisation of the pathway towards a sustainable whole-energy system: role of synthetic fuels
Researcher: Xavier Rixhon
Supervisor(s): Francesco Contino, Hervé Jeanmart

Securing energy supply while mitigating the anthropogenic greenhouse gas emissions embodies one of the biggest challenges of today’s -and tomorrow’s- society. In this perspective, renewable energies, mainly wind and solar, will be extensively installed. However, these resources per se present a time and space disparity which generally leads to a mismatch between supply and demand. Therefore, to harvest their maximum potentials, the energy system shall become more flexible, especially through the storage of this renewable electricity. The integration of electro-fuels seems to be a promising solution. They could play the role of long-term storage of electricity and energy carriers to supply other sectors (e.g. heat or mobility). To address the question of the role of these fuels in the energy transition, a multi-energy and multi-sector model, Energy Scope TD (ESTD), will be further developed. It optimizes the design of an energy system to minimize its costs and emissions. Defining an energy transition strategy for a large-scale system, such as a country, implies decisions with long-term impacts (20 to 50 years) and, hence, many uncertainties. To perform the uncertainty quantification (UQ), ESTD will be complemented with a surrogate-assisted UQ framework. The perspective of this project is then to provide the designers and the decision-makers with optimized energy system designs, including the knowledge we have on the uncertainties, in order to pave a robust pathway towards sustainability.



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.



Robust integration of carbon capture in renewable methanation
Researcher: Dierderik Coppitters
Supervisor(s): Francesco Contino

Robust and antifragile design optimization of energy systems, considering computationally-efficient uncertainty quantification methods.

Improvement of computational efficiency of surrogate models for uncertainty quantification, using active learning methods.

Process simulation and optimization of direct air capture systems in power-to-gas systems.



Developing a low-NOx ammonia burner
Researcher: Charles Lhuillier
Supervisor(s): Francesco Contino

In collaboration with a startup company, the goal of this project is to develop, characterise and optimise an innovative burner adapted ammonia combustion with low nitrogeneous pollutant emissions.



Carnot batteries as effective sector-coupling systems for heat and power: techno-economic analysis and robust optimisation
Researcher: Antoine Laterre
Supervisor(s): Francesco Contino

The first concepts of Carnot batteries appeared in the early 2010s. These systems propose to use excess energy from the grid to produce heat and store it in thermal form. This energy can then be returned in the form of electricity through thermal cycles. By their very nature, these “batteries” allow for efficient coupling between electrical and thermal systems, which is an asset regarding the challenges prescribed by the energy transition. For example, they can take advantage of waste heat (< 100°C) to increase their power output to power input ratio to values above 100%. The heat they generate can also be used for other purposes (e.g. industrial).


Theoretical studies to date have shown that this technology has great potential for development. However, they also reveal that the performance can deteriorate severely when certain parameters deviate slightly from the optimal design conditions (i.e. variation of waste heat temperature, of isentropic efficiencies, etc.). In order to evaluate their real potential, this project proposes to integrate, by simulation means, the uncertainty dimension on these parameters to quantify more efficiently the sensitivity of Carnot batteries to them.


To identify the designs that are robust to uncertainty and to evaluate the actual techno-economic performance of these systems, Uncertainty Quantification and Robust Optimisation (optimisation under uncertainty) techniques will be applied. Using metrics such as LCOS, we will assess with more certainty the potential of this technology compared to other storage systems, such as batteries.





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.



Towards more sustainable mobility practices in Wallonia? A mixed-method research on the boosts and brakes for the development of such practices, and on the COVID-19 pandemic consequences.
Researcher: Line Vanparys
Supervisor(s): Francesco Contino

In the transportation sector of Wallonia, despite the existing policy goals and scenarios aiming to reduce energy consumption, and therefore car use, car is still the most used mode of transport. Moreover, in Belgium, the number of wage cars almost doubled between 2008 and 2020. And, if fewer people travelled in 2020 because of the COVID-19 pandemic, a more intensive use of the car is conceivable: people intend to use public transport less and use the car more because of health security reasons. There is thus an emerging need to pursue research on the development of more sustainable mobility practices. Social practice theories are the theoretical framework. By rejecting the distinction between macro- and microscopic levels of analysis,


they consider the practices as the unit of analysis explained by norms and values, infrastructures, socioeconomic, characteristics, other practices, … Even if there are surveys on how people explain their use of different means of transport, nearly no social practice theories’ studies have been carried out in Wallonia to understand the adoption and defection of sustainable mobility practices. A mixed-methods approach will be used, with quantitative data from existing surveys (notably BELDAM, Monitor, BeMob, and Mo’-vid19), and with qualitative data collected through three sets of in-depth interviews with a) individuals who changed their mobility practices to more sustainable ones, b) individuals wanting to do so but failed, and c) individuals having access to a company car. The research will focus on several aspects of mobility practices: means of transportation, distance, frequency, time allowed for each trip, type of energy (fuel, electric, …), and type of car (SUV, family car, more or less fuel/energy-efficient, weight…). The last elements have not yet been studied with a sociological approach.



The Impact of Energy Policies on the Energy System
Researcher: Panagiotis Varelas
Supervisor(s): Francesco Contino

There are many difficulties in the upcoming years toward a carbon-neutral planet. The main issue that renewable energy sources are facing in the energy transformation is the security of supply. Alternative fuels like hydrogen and ammonia might be a solution to this challenge as possible energy carriers. The main barrier they are currently facing is the increased cost compared to other fuels. This project is aiming to promote alternative fuels and green applications in mobility and other sectors and to highlight the importance of alternative fuels as a solution to tackle climate change and reach our goals.









In this direction, academia should play a vital role by providing the necessary facts and guidelines to the policymakers. Currently, researchers and policymakers are acting independently, and coordinating actions are very often lacking. The main goal of this research is to provide evidence and facts that can help policymakers take decisions. This implies measuring the impact of the energy policies on energy systems. Thus, every time an energy policy will be implemented or modified our model will be able to predict the potential impact both for investors and consumers.









Existing models are not considering the effect of the energy policies on the energy systems. Traditionally these models are scenario-oriented. According to their input parameters such as demand, consumption, and available technologies, they can suggest a couple of different scenarios to the policymakers.









Modern societies are rapidly changing by the decisions of political institutions. Recent major events in Europe and United States proved that energy transition is a dynamic condition affected by a plethora of different parameters that should be considered. Designing energy models needs to be a vice versa task proposing the optimal scenarios to the policy makers, considering at the same time the impact of energy policies.









The second novelty of this research proposal is that it will take into consideration both the financial and the societal impact of energy policies.