obtained his doctorate in aeronautics and applied mathematics from Caltech in 2005. After a research associate position at ETH Zurich, he is since 2009 professor of aeronautical mechanics at UCLouvain. His research interests cover fluid mechanics, Lagrangian numerical methods, their deployment in HPC environment, and their application to fundamental problems as well as more applied ones in bio-propulsion, aeronautics and wind energy. His work in these last two thematics led in 2013 to the launch of Wake Prediction Technologies, a spin-off company which offers services in studying and modelling aircraft and wind turbine wakes.
The problematic of wakes is now at the center of his research with the investigation and development of control schemes for devices interacting through their wakes, i.e. aircraft flying in formation or wind turbines (ERC Consolidator Grant WakeOpColl). The schemes investigated rely as much as possible on machine learning techniques: the devices learn how to sense and exploit the ambient flow.
He also collaborates with the von Karman Institute, ULg and Cenaero on aerothermal flows past Thermal Protection Systems. Other collaborations include UMons, UCLouvainA, Caltech,UIUC, DTU and ETHZ.
IMMC main research direction(s):
Research group(s): TFL
PhD and Post-doc researchers under my supervision:
|Implementation of an incompressible hybrid Eulerian-Lagrangian external flow solver|
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.
|Efficient and scalable frameworks for PDE simulations|
focuses his research on the development of efficient and scalable computational framework for the simulation of 3D PDEs on massively parallel and heterogeneous architectures.
|Modelisation and optimization of bird flight|
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.
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|
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.
|WakeOpColl: Performance optimization of wind farms under realistic operating conditions using collaborative control |
Fast-increasing demand for renewable energy has resulted in a growing interest for the development of new efficient wind farms. As a consequence, the study of wake effects has been gaining a lot of attention recently. Wake effects dictate the optimal operating point of wind farms. Indeed, placing wind turbines in close proximity to one another leads to convoluted wake-wake and wake-turbine interactions which have a detrimental effect on both the lifespan and the energy production of the turbines.
Developing flexible control strategies able to take into account the convoluted dynamic wake effects is thus currently one of the most prevailing challenges faced by the wind energy industry. Despite large amount of resources engaged on the topic, classical control and optimization theories applied to wind farms have up to this day failed to achieve high efficiency together with transparent adaptivity, robustness and flexibility.
Computational Fluid Dynamic (CFD) models based controllers have been introduced in an attempt to account for the wake effects. Even though they allowed to accurately capture the physic of wind turbine farm in given conditions, they still remained unsuitable for control under time varying atmospheric conditions due to their prohibitive computational cost
The aim of this project is thus to develop affordable wake simulation tools and then to apply them in the framework of machine learning and collaborative control in order to enhance the performances of the farms.
|Aircraft Wake Sensing and Tracking Strategies towards Operational Formation Flight|
Physics dictates that a flow device has to leave a wake or the signature of it producing sustentation forces, and can then impact negatively or favorably another device downstream (e.g. Wake turbulence between aircraft in air traffic, wake losses within wind farms). This project proposes an Artificial Intelligence and bio-inspired paradigm for the control of flow devices subjected to wake effects. To each flow device is associated an intelligent agent that pursues given goals of efficiency or turbulence alleviation. Every one of these flow agents now relies on machine-learning tools to learn how to make the right decision when confronted with wake or turbulent flow structures. At a system level, Multi-Agent System and Distributed Learning paradigms are employed. The goal is to demonstrate that the design of a system that learns how to control the flow, is simpler than the design of the control scheme and will yield a more robust scheme.
Collaborative control of multiple devices constitutes a field of development that will be transformative in many engineering areas. Collaboration is indeed proven to consistently bring increased global efficiency, adaptivity and robustness in the applications of interest. The design of robust collaborative schemes is a topic in its own, which is particularly delicate when the devices interactions are flow-mediated, due to the non-linearity of flows. More crucially, they affect the operation of the impacted devices.
|Captive Trajectory System for the handling of wake-impacted flow devices|
The main objective of the thesis is to develop a Captive Trajectory System (CTS) for the handling of wake-impacted flow devices that are free flying or swimming, such as aircrafts or bio-inspired robots. Which means that there is no other external force applied on those models, barring gravity, than the one applied by the fluid.
The envisioned facility will be unique at an international level. At the same time, its scope of applications will be quite wide, covering, but not limited to, applied and fundamental fluid mechanics (fluid-structure interaction problems), biomechanics (biolocomotion), and civil engineering (wind or flow-structure interactions). Additionally, we see this project as a first foray into the emerging field of experimental studies augmented by Artificial Intelligence or co-simulation.
Nowadays, this is not experimentally achievable by the use of Lab facilities, because they only allow, at most, horizontal and vertical displacements and do not feature any force or motion control. Hence, the goal of this thesis, of a rather experimental nature, is to design a robotic system – possibly partially immersed – whose precision, sensing and control capabilities will be able to handle free-moving devices, and to validate fluid-structure interaction models developed by various IMMC research teams, also involved in the project.
|Development of high-fidelity numerical methods for the simulation of the aerothermal ablation of space debris during atmospheric entry|
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.
|FSI for wind turbines|
Wind energy is one of the most promising renewable energy to ensure the transition towards a sustainable energy mix. At the national level, the offshore installed power will reach 3 GW in 2020 and hence become the most important source of low carbon energy. However, with wind turbines now reaching a diameter of up to 160m, there is a need to consider structural effects into the design, as the large deformation and unsteady loads can modify the aerodynamics or lead to vibration instability. Numerical simulations are an efficient and flexible tool to answer this need.
Our goal is to further advance the state-of-the-art of the simulation of both horizontal and vertical axis wind turbines by handling correctly the fluid structure interaction. The first part of project consists in the efficient coupling of the fluid and the flow solver. The wind turbine deformation will be computed using a detailed FEM solver developed at UGent, whereas the flow will be computed using a scale-resolving tool based on large-edddy simulation and HPC. The effect on the flow of the turbine will be handled with an actuator lines method. Using LES for the flow solver is a novel approach, that will capture the unsteadiness of the flow at a much higher level than the currently used URANS. This will allow to study the unsteady loads acting on the turbine, its vibration modes and the effect of deformation on the power and the wake.
The developed tool will then be used to study load alleviation methods such as working on the tip speed ratio, the orientation, and performing individual pitch control. The FSI in complex situation will also be performed, such as wind turbines interacting with the wake of preceeding ones. The evolution of the loads when the turbines are subject to gusts will also be characterized, including the study of the artificial gust generation. The aeroelasticity of WTs in a floating configuration will also be investigated.
|A pre-exascale Vortex Particle-mesh solver for complex Fluid-Structure Interaction problems.|
We present an accurate and highly scalable vortex particle method builds upon a Multi-Resolution discretization (MR), an Immersed Interface Method (IIM) and efficient elliptic solvers to simulate bio-inspired locomotion in 3D. This project is intended to bring all the mentioned approaches together to the next scale of computational intensity and concurrency. The consistency between our Lagrangian formulation, these advanced numerical frameworks and a HPC-oriented implementation should unlock the full potential of Belgium’s next generation HPC architectures and thus, enable a leap in the scale of computable problems.
|AI-based control policies towards efficient collective behaviours of flow agents and their application to fish schooling|
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.
|Three-dimensional multi-block decomposition for automatic hexahedral mesh generation and application to fluid flow simulations.|
In computational physics, the vast majority of Partial Differential Equation (PDE) solvers rely on a spatial discretization of the bulk of the domain, typically a mesh. Thus far, geometrically complex domains are discretized predominantly using unstructured meshes, on which the PDE is subsequently solved using the Finite Element Method. Methods based on unstructured meshes are however inherently penalized in their computational efficiency. On the contrary, the regularity of block structured meshes can be leveraged to build efficient algorithms. For this reason, automatic generation of block-structured meshes is the holy grail of mesh generation.
A first objective of the research project is therefore to explore new approaches for generating multiblock decompositions of general 3D domains. We will build on the recent developments in 3D frame fields and aim at improving formulations based on the constrained minimization of an energy function. A second lead that will be explored is based on the decomposition of the domain in convex sub-regions, on which existing methods are more robust.
Constructing a new class of meshes is only relevant if those meshes are endowed with a true benefit in terms of CPU/GPU time and accuracy. A second objective is therefore to extend existing Computational Fluid
Dynamics technologies for Cartesian grids to multiblock grids. In particular, we want to take advantage of the conformal map-like nature of the mesh to increase computational performance, and also show how our methodology can be applied to models with moving or deforming boundaries.
|Towards (pre-)exascale computing: GPU acceleration in a massively parallel multiresolution framework for the incompressible Navier-Stokes equations|
Very large calculations on supercomputers have increasingly occupied a central role in scientific discoveries and engineering innovations. More and more machines are relying on hardware accelerators such as GPUs to further boost performances. However, as opposed to compute-bound problems (such as AI, ML,...), memory-bound problems (such as the resolution of the incompressible Navier-Stokes equations) are suffering increasingly important communication bottlenecks because of the latency inherent to memory transfers between the CPU and the GPU as well as the network bandwidth between the computer nodes. For such problems, simple hardware upgrades do not suffice and one needs to make changes to the algorithms and memory management strategies directly.
This project in particular will focus on the simulation of incompressible flows and will build upon a currently-being-developed multi-resolution open-source framework, murphy, and bring it to exascale-level scalability and computational intensity by relying on a unique combination of algorithmic and software tools: a wavelet-based block-structured multiresolution approach to exploit most out of the GPU acceleration in the upcoming pre-exascale facilities, such as LUMI.
Recent publicationsSee complete list of publications
1. Coheur, Joffrey; Magin, Thierry E.; Chatelain, Philippe; Arnst, Maarten. BAYESIAN IDENTIFICATION OF PYROLYSIS MODEL PARAMETERS FOR THERMAL PROTECTION MATERIALS USING AN ADAPTIVE GRADIENT-INFORMED SAMPLING ALGORITHM WITH APPLICATION TO A MARS ATMOSPHERIC ENTRY. In: International Journal for Uncertainty Quantification, Vol. 13, no.2, p. 53-80 (2023). doi:10.1615/int.j.uncertaintyquantification.2022042928. http://hdl.handle.net/2078.1/271592
2. Lejeune, Maxime; Moens, Maud; Chatelain, Philippe. A meandering-capturing wake model coupled to rotor-based flow-sensing for operational wind farm flow prediction. In: Frontiers in Energy Research, (2022). doi:10.3389/fenrg.2022.884068 (Accepté/Sous presse). http://hdl.handle.net/2078.1/262173
3. Ransquin, Ignace; Duponcheel, Matthieu; Chatelain, Philippe. Dynamics of the Wake Vortices of a Two-aircraft Formation, Hazard Assessment at Large Distances and Sensitivity Analysis. In: AIAA Paper, Vol. SCITECH 2022 Forum, no., p. 2022-1198 (2022). doi:10.2514/6.2022-1198. http://hdl.handle.net/2078.1/255854
4. Coquelet, Marion; Bricteux, Laurent; Moens, Maud; Chatelain, Philippe. A reinforcement-learning approach for individual pitch control. In: Wind Energy, (2022). doi:10.1002/we.2734. http://hdl.handle.net/2078.1/261138
5. Moens, Maud; Chatelain, Philippe. Correlations Between Wake Phenomena and Fatigue Loads Within Large Wind Farms: A Large-Eddy Simulation Study. In: Frontiers in Energy Research, Vol. 10, p. 881532 (2022). doi:10.3389/fenrg.2022.881532. http://hdl.handle.net/2078.1/261929
6. Caprace, Denis-Gabriel; Ning, Andrew; Chatelain, Philippe; Winckelmans, Grégoire. Effects of rotor-airframe interaction on the aeromechanics and wake of a quadcopter in forward flight. In: Aerospace Science and Technology, Vol. 130, no.--, p. 107899 (2022). doi:10.5281/zenodo.7059240. http://hdl.handle.net/2078.1/265626
7. Ducci, Gianmarco; Vitucci, Gennaro; Chatelain, Philippe; Ronsse, Renaud. On the role of tail in stability and energetic cost of bird flapping flight. In: Scientific Reports, Vol. 12, no.1, p. 22629 (2022). doi:10.1038/s41598-022-27179-7. http://hdl.handle.net/2078.1/269376
8. Riehl, James; Hufstedler, Esteban; Chatelain, Philippe; Hendrickx, Julien. String stability of energy-saving aircraft formations. In: Journal of Guidance, Control, and Dynamics : devoted to the technology of dynamics and control, Vol. 45, no. 5, p. 935-943 (2022). http://hdl.handle.net/2078.1/255826
9. Caprace, Denis-Gabriel; Gillis, Thomas; Chatelain, Philippe. FLUPS - A Fourier-based Library of Unbounded Poisson Solvers. In: SIAM Journal on Scientific Computing, Vol. 43. doi:10.1137/19M1303848. http://hdl.handle.net/2078.1/225706
10. Ducci, Gianmarco; Colognesi, Victor; Vitucci, Gennaro; Chatelain, Philippe; Ronsse, Renaud. Stability and Sensitivity Analysis of Bird Flapping Flight. In: Journal of Nonlinear Science, Vol. 31, no.2, p. 47 (2021). doi:10.1007/s00332-021-09698-1. http://hdl.handle.net/2078.1/244939
1. Caprace, Denis-Gabriel; Chatelain, Philippe; Ransquin, Ignace. Aircraft wake sensing. http://hdl.handle.net/2078.1/269512 http://hdl.handle.net/2078.1/269512
1. Lejeune, Maxime; Moens, Maud; Chatelain, Philippe. Extension and validation of an operational dynamic wake model to yawed configurations. In: The Science of Making Torque from Wind (TORQUE 2022) 01/06/2022 - 03/06/2022 Delft, Netherlands. Vol. 2265, no.2, p. 022018 (2022). IOP Publishing Ltd: Bristol BS1 6HG, United Kingdom, 2022 xxx. doi:10.1088/1742-6596/2265/2/022018. http://hdl.handle.net/2078.1/262170
2. Chatelain, Philippe. Formation flight: wake sensing and assessment of hazard for external traffic. 2022 xxx. http://hdl.handle.net/2078.1/265611
3. Coquelet, Marion; Moens, Maud; Bricteux, Laurent; Crismer, Jean-Baptiste; Chatelain, Philippe. Performance assessment of wake mitigation strategies. In: Journal of Physics: Conference Series. Vol. 2265, p. 032078 (2022). IOP Publishing, 2022 xxx. doi:10.1088/1742-6596/2265/3/032078. http://hdl.handle.net/2078.1/261154
4. Moens, Maud; Coquelet, Marion; Trigaux, François; Chatelain, Philippe. Handling Individual Pitch Control within an Actuator Disk framework: verification against the Actuator Line method and application to wake interaction problems. In: Journal of Physics: Conference Series. Vol. 2265, no.-, p. 022053 (2022). IOP Publishing, 2022 xxx. doi:10.1088/1742-6596/2265/2/022053. http://hdl.handle.net/2078.1/261922
5. Caprace, Denis-Gabriel; Ransquin, Ignace; Colognesi, Victor; Chatelain, Philippe. Flow sensing for improved wake harvesting in formation flight. 2022 xxx. http://hdl.handle.net/2078.1/269478
6. Winckelmans, Grégoire; Caprace, Denis-Gabriel; Duponcheel, Matthieu; Bricteux, Laurent; Ivan De Visscher; Trigaux, François; Chatelain, Philippe. When turbulence matters in vortex dominated flows. 2022 xxx. http://hdl.handle.net/2078.1/269485
7. Ransquin, Ignace; Duponcheel, Matthieu; Caprace, Denis-Gabriel; Chatelain, Philippe. Analysis of a Formation Flight Wake-Tracking Strategy on the Dynamics of the Formation Wake Vortices. 2022 xxx. http://hdl.handle.net/2078.1/269484
8. Trigaux, François; Winckelmans, Grégoire; Chatelain, Philippe. A flexible actuator line method for aeroelastic simulations of wind turbines in atmospheric boundary layer. In: Journal of Physics: Conference Series. Vol. 2265, no.2, p. 022050 (2022). IOP Publishing: United Kingdom, 2022 xxx. doi:10.1088/1742-6596/2265/2/022050. http://hdl.handle.net/2078.1/267263
9. Ransquin, Ignace; Caprace, Denis-Gabriel; Duponcheel, Matthieu; Chatelain, Philippe. Vortex sensing and uncertainty propagation for aircraft formation flight. 2022 xxx. http://hdl.handle.net/2078.1/262652
10. Coquelet, Marion; Lejeune, Maxime; Moens, Maud; Riehl, James; Bricteux, Laurent; Chatelain, Philippe. Tackling control and modeling challenges in wind energy with data-driven tools. 2022 xxx. http://hdl.handle.net/2078.1/262231
1. Chatelain, Philippe; Bergdorf, Michael; Koumoutsakos, Petros. Large Scale, Multiresolution Flow Simulations Using Remeshed Particle Methods. In: Meshfree Methods for Partial Differential Equations IV , Springer: Berlin, Heidelberg, 2008, p. 35-46. 978-3-540-79994-8. xxx xxx. doi:10.1007/978-3-540-79994-8_3. http://hdl.handle.net/2078.1/140440
2. Chatelain, Philippe; Curioni, Alessandro; Bergdorf, Michael; Rossinelli, Diego; Andreoni, Wanda; Koumoutsakos, Petros. Vortex Methods for Massively Parallel Computer Architectures. In: High Performance Computing for Computational Science - VECPAR 2008 (Lecture Notes in Computer Science; xxx), Springer: Berlin/Heidelberg, 2008, p. 479-489. 978-3-540-92858-4. xxx xxx. doi:10.1007/978-3-540-92859-1_42. http://hdl.handle.net/2078.1/140442