Members

IMMC

Marion Coquelet
PhD student
Ir. at UMONS in 2018
Contact

Main project: WakeOpColl
Funding: ERC
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.

IMMC main research direction(s):
Energy
Fluid mechanics

Keywords:
aerodynamics
wake flows
wind turbine

Research group(s): TFL
Collaborations: Collaboration with UMONS - Prof. Bricteux

  

Recent publications

See complete list of publications

Journal Articles


1. Bricteux, Laurent; Chatelain, Philippe; Coquelet, Marion; Moens, Maud. A reinforcement-learning approach for individual pitch control. In: Wind Energy, (2022). doi:10.1002/we.2734. http://hdl.handle.net/2078.1/261138


Conference Papers


1. Chatelain, Philippe; Coquelet, Marion; Bricteux, Laurent; Riehl, James; Lejeune, Maxime; Moens, Maud. Tackling control and modeling challenges in wind energy with data-driven tools. 2022 xxx. http://hdl.handle.net/2078.1/262231

2. Bricteux, Laurent; Crismer, Jean-Baptiste; Coquelet, Marion; Moens, Maud; 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

3. Trigaux, François; Chatelain, Philippe; Coquelet, Marion; Moens, Maud. 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

4. Chatelain, Philippe; Lejeune, Maxime; Coudou, Nicolas; Coquelet, Marion; Moens, Maud. Data assimilation for the prediction of wake trajectories within wind farms. In: Journal of Physics: Conference Series. Vol. 1618, no.6, p. 062055 (2020). IOP Publishing Ltd, 2020 xxx. doi:10.1088/1742-6596/1618/6/062055. http://hdl.handle.net/2078.1/238249

5. Chatelain, Philippe; Coquelet, Marion; Caprace, Denis-Gabriel; Balty, Pierre; Waucquez, Juan. Multiphysics simulations of the dynamic and wakes of a floating Vertical Axis Wind Turbine. In: Journal of Physics: Conference Series. Vol. 1618, no.1, p. 062053 (2020). IOP Publishing, 2020 xxx. doi:10.1088/1742-6596/1618/6/062053. http://hdl.handle.net/2078.1/237219

6. Bricteux, Laurent; Chatelain, Philippe; Coquelet, Marion; Lejeune, Maxime. Biomimetic individual pitch control for load alleviation. In: Journal of Physics: Conference Series. Vol. 1618, no. 1, p. 022052 (2020). IOP Publishing, 2020 xxx. doi:10.1088/1742-6596/1618/2/022052. http://hdl.handle.net/2078.1/239854

7. Chatelain, Philippe; Lejeune, Maxime; Coudou, Nicolas; Coquelet, Marion; Moens, Maud. Development of an online wind turbine wake model. 2020 xxx. http://hdl.handle.net/2078.1/228689

8. Bricteux, Laurent; Chatelain, Philippe; Coquelet, Marion; Lejeune, Maxime. Biomimetic individual pitch control for load alleviation. 2020 xxx. http://hdl.handle.net/2078.1/244422

9. Chatelain, Philippe; Lejeune, Maxime; Coquelet, Marion; Moens, Maud. Characterisation and Online Update of a Vorticity-Based Wind Skeleton Wake Model. 2019 xxx. http://hdl.handle.net/2078.1/225804

10. Chatelain, Philippe; Lejeune, Maxime; Coudou, Nicolas; Coquelet, Marion; Moens, Maud. Data assimilation for the prediction of wake trajectories within wind farms. 2019 xxx. http://hdl.handle.net/2078.1/225821