Feed-forward control of a perturbed backward-facing step flow

Gautier, N.; Aider, Jean-Luc

doi:10.1017/jfm.2014.518

Cited by 27 publications

(17 citation statements)

References 33 publications

(40 reference statements)

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“…Based on previous works dedicated to PIV in a hydrodynamics channel (Gautier and Aider, 2014), the new homemade LabVIEW (National Instruments, USA) interface has been developed to manage the hardware specific to windtunnel experiments, together with the optical flow algorithm as sketched in Fig. 2.…”

Section: Real-time Velocity Fields Measurementsmentioning

confidence: 99%

Adaptive control of the dynamics of a fully turbulent bimodal wake using real-time PIV

et al. 2019

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In this study we focus on the control of the dynamics of 3D turbulent wake downstream a square-back Ahmed body (Re H = 3.9 × 10 5 ). The peculiar dynamics of such a wake are first characterized through the trajectories of the pressure barycenter over the rear part of the model as well as the recirculation barycenter in the wake. In particular it is shown that these dynamics allow the definition of three different states: the two so-called reflectional symmetry-breaking (RSB) modes and the transient symmetric (TS) mode. It was shown recently that the time-fluctuations of the pressure barycenter could be characterized as a weak chaotic system with a well-defined attractor (Varon et al, 2017). We show that the dynamics of the bimodal wake can then be forced into a stable asymmetric or symmetric state in open loop control, using tangential continuous or pulsed blowing in three different regions along the upper edge of the rear part of the model. Finally, a simple closed-loop opposition control, based on real-time identification of the wake barycenter in the PIV fields, is used to force the chaotic dynamics of the wake into a regular oscillatory motion at a well-controlled frequency. Depending on the actuation parameters, the wake dynamics can also be switched from bimodal to a new multimodal behavior. We show that this new mode also exhibits a peculiar dynamics with an up-down instead of left-right chaotic oscillations. Interestingly, the recirculation area (size of the recirculation bubble) is much

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Section: Real-time Velocity Fields Measurementsmentioning

confidence: 99%

Adaptive control of the dynamics of a fully turbulent bimodal wake using real-time PIV

et al. 2019

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“…Assuming a mean formation time for the circulation of t = T /2, i.e. half of the actuation cycle, Equation 15 indicates a circulation of Γ(T /2)/Γ 0 = 9. Using this new reference circulation, the ratio of measured versus expected circulation (Γ(T /2)/Γ j ) as presented in Table II is much closer to one (for the lower three Strouhal numbers).…”

Section: Vortex Circulationmentioning

confidence: 99%

“…In recent years, feedback and feed-forward control strategies have been developed to selectively control disturbances [14,15]. It has been shown numerically that the turbulent kinetic energy of the incoming flow can be reduced by more than 90% using feed-forward control [14].…”

Section: Introductionmentioning

confidence: 99%

Entrainment effects in periodic forcing of the flow over a backward-facing step

2017

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The effect of the Strouhal number on periodic forcing of the flow over a backward-facing step (height, H) is investigated experimentally. Forcing is applied by a synthetic jet at the edge of the step at Strouhal numbers ranging from 0.21 < StH < 1.98 (StH = f H/U∞) at a Reynolds number of ReH = HU∞/ν = 41000. In the literature, the effect of Strouhal number on the reattachment length is often divided into low-and high frequency actuation, referring to different frequency modes in the unforced flow. In the present paper, variations with Strouhal number are explained based on entrainment rather than frequency modes. The reattachment length is shown to decrease linearly with entrainment. Entrainment is driven by vortices generated by the forcing and locally entrainment is shown to be qualitatively similar to circulation for all cases considered. Total circulation (and therewith entrainment and the effect on the reattachment length) is shown to decrease with Strouhal number whereas this is not predicted by models based on frequency modes. An empirical model for the (decay of) circulation is derived by tracking vortices in phase-locked data. This model is used to decipher relevant scaling parameters that explain the variations in circulation, entrainment and reattachment length. Three regimes of Strouhal number are identified. A low-Strouhal-number regime is observed for which vortices are formed at a late stage relative to the recirculation region, causing a decrease in effectiveness. For high Strouhal numbers vortices are being re-ingested into the actuator or are packed so close together that they cancel each other, both decreasing the effectiveness of forcing. In the intermediate regime a vortex train is formed of which the decay of circulation increases for increasing Strouhal number. The scaling of this decay fully explains the observed variation in reattachment length. The observations on entrainment made in this study are expected to also hold for periodic forcing of other bluff-body flows. * G.Bharath@southampton.ac.uk arXiv:1702.06895v1 [physics.flu-dyn]

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“…Its offline and online accuracy has been demonstrated and detailed by Champagnat et al (2011) and Gautier & Aider (2014b). Furthermore this acquisition method was successfully used in Davoust, Jacquin & Leclaire (2012), Gautier & Aider (2013a) and Gautier & Aider (2014a). Velocity fields are computed over an area of (17.2 × 4.6) × 10 −4 m 2 which translates into a (9 × 3)h 2 area.…”

Section: Sensor: Two-dimensional Real-time Velocity Fields Computationsmentioning

confidence: 99%

Closed-loop separation control using machine learning

et al. 2015

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We present the first closed-loop separation control experiment using a novel, model-free strategy based on genetic programming, which we call 'machine learning control'. The goal is to reduce the recirculation zone of backward-facing step flow at Re h = 1350 manipulated by a slotted jet and optically sensed by online particle image velocimetry. The feedback control law is optimized with respect to a cost functional based on the recirculation area and a penalization of the actuation. This optimization is performed employing genetic programming. After 12 generations comprised of 500 individuals, the algorithm converges to a feedback law which reduces the recirculation zone by 80 %. This machine learning control is benchmarked against the best periodic forcing which excites Kelvin-Helmholtz vortices. The machine learning control yields a new actuation mechanism resonating with the low-frequency flapping mode instability. This feedback control performs similarly to periodic forcing at the design condition but outperforms periodic forcing when the Reynolds number is varied by a factor two. The current study indicates that machine learning control can effectively explore and optimize new feedback actuation mechanisms in numerous experimental applications.

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Feed-forward control of a perturbed backward-facing step flow

Cited by 27 publications

References 33 publications

Adaptive control of the dynamics of a fully turbulent bimodal wake using real-time PIV

Adaptive control of the dynamics of a fully turbulent bimodal wake using real-time PIV

Entrainment effects in periodic forcing of the flow over a backward-facing step

Closed-loop separation control using machine learning

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