Flow control using optical sensors

Abstract : Flow control using optical sensors is experimentally investigated. Real-time computation of flow velocity fields is implemented. This novel approach featuring a camera for acquisition and a graphic processor unit (GPU) for processing is presented and detailed. Its validity with regards to speed and precision is investigated. A comprehensive guide to software and hardware optimization is given. We demonstrate that online computation of velocity fields is not only achievable but offers advantages over traditional particle image velocimetry (PIV) setups. It shows great promise not only for flow control but for parametric studies and prototyping also.A hydrodynamic channel is used in all experiments, featuring a backward facing step for separated flow control. Jets are used to provide actuation. A comprehensive parametric study is effected to determine the effects of upstream jet injection. It is shown upstream injection can be very effective at reducing recirculation, corroborating results from the literature.Both open and closed loop control methods are investigated using this setup. Basic control is introduced to ascertain the effectiveness of this optical setup. The recirculation region created in the backward-facing step flow is computed in the vertical symmetry plane and the horizontal plane. We show that the size of this region can be successfully manipulated through set-point adaptive control and gradient based methods.A physically driven control approach is introduced. Previous works have shown successful reduction recirculation reduction can be achieved by periodic actuation at the natural Kelvin-Helmholtz frequency of the shear layer.A method based on vortex detection is introduced to determine this frequency, which is used in a closed loop to ensure the flow is always adequately actuated. Thus showing how recirculation reduction can be achieved through simple and elegant means using optical sensors. Next a feed-forward approach based on ARMAX models is implemented. It was successfully used in simulations to prevent amplification of upstream disturbances by the backward-facing step shear layer. We show how such an approach can be successful in an experimental setting.Higher Reynolds number flows exhibit non-linear behavior which can be difficult to model in a satisfactory manner thus a new approach was attempted dubbed machine learning control and based on genetic programming. A number of random control laws are implemented and rated according to a given cost function. The laws that perform best are bred, mutated or copied to yield a second generation. The process carries on iteratively until cost is minimized. This approach can give surprising insights into effective control laws.
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Nicolas Gautier. Flow control using optical sensors. Mechanics of the fluids [physics.class-ph]. Université Pierre et Marie Curie - Paris VI, 2014. English. ⟨NNT : 2014PA066640⟩. ⟨tel-01150428⟩

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