My project is summarized here (coming soon).
Wake of wind turbines under atmospheric boundary layers
Floating offshore wind turbine have been of particular interest to address newer renewable energies and climate change challenges. A precise characterization of the far wake downstream the wind turbine is necessary for its integration in farms in order to maintain its aerodynamical performances. The infuence of the moving motion due
to the floating structure movement, and a realistic turbulent boundary layer, are the main key elements of my work at Ecole Centrale de Nantes.
Dynamics of separated and detached flows
Characterization of the separated and detached flows dynamics has been an important topic of research for me since my thesis, for separated boundary layers under control to bluff bodies wakes. Experimental determination of the dominant characteristic frequencies in particular, from Kelvin-Helmholtz instability to vortex shedding dynamics, was performed.
Control of separated turbulent boundary layers
Separation of boundary layers is a unwanted phenomenon for aerodynamic applications in transports, for airplanes or cars for example. For an airfoil, the drag increase could cause a violent stall. The study of these separated flows leads to understand the physical phenomenon and to perform more efficient control.
Innovative control for airfoils
An important challenge for flow control is the development of innovative actuators for models, in particular for industrial applications like airfoils and land transportation. It was the case for the european project SMS at ONERA/IMFT Toulouse where I worked on a morphing wing instrumented with Shape Memory Alloys (SMA) for the camber control, and piezoelectrical patches at the trailing edge. The actuators are completely included in the model, as expected for high TRL systems.
Control of wakes of bluff bodies
Bluff bodies wakes control are also of important interest for scientific and industrial applications. Instable phenomena are usually very present in bluff bodies wakes and part of the total drag of the body. Controlling these fluctuant structures is therefore the usual objective of flow control.
Feedback flow control
Open-loop control approaches, for which the actuators main parameters are fixed by the users are often not efficient enough. For practical applications, the intial flow conditions, like the freestream velocity of an airplane, could change. Therefore, the optimal control solution found is not valid anymore. Furthermore, perturbations of the flow (turbulence, brutal gust, etc.) make the control more difficult. A closed -loop approach is then preferred.
Machine learning for flow control
Non-linear behaviors of the flow make the control difficult to be achieved. Furthermore, actuators are usually driven with multiple parameters of control. To achieve flow control with zero-net mass flow jets, differents parameters have to be manipulated for each actuator: maximal velocity ratio, frequency, phase, duty cycle for PWM, etc. The parametric space is then particularly huge. New machine learning techniques are therefore particularly powerfull for the optimization of open-loop and closed-loop controllers. Genetic algorithms (for open-loop), and genetic programming (closed-loop) were mostly used for my different works, but machine learning is now a new paradigm for flow control to develop in the future projects.