Closed-loop enhancement of jet mixing with extremum-seeking and physics-based strategies

Wu, Zongmin; Zhou, Yu; Cao, Huali; Li, Wen J.

doi:10.1007/s00348-016-2194-9

Cited by 9 publications

(13 citation statements)

References 41 publications

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“…The mixing is quantified by the averaged streamwise velocity decay rate at five diameters downstream on the symmetry axis. The achieved mixing is better than in a previous study by Wu et al (2016) with ex-tremum seeking control since a better (smaller) duty cycle was found. MLC performed optimization in only 4 generations with 100 control laws in each, i.e.…”

Section: Discussioncontrasting

confidence: 66%

“…The majority of the experimental turbulence control studies rely on adaptive variation of one or few actuation parameters, like the amplitude or frequency of suction or blowing. Examples include physics-based methods, like Pastoor et al (2008), Wu et al (2016), Zhang et al (2004b), extremum and slope-seeking control method (Becker et al 2007, Brack-ston et al 2016, Maury et al 2012, Wu et al 2015 and Machine Learning Control (MLC) (Brunton and Noack 2015 allowing for a very rich set of possible control laws. All approaches have been widely applied in turbulence experiments.…”

Section: Introductionmentioning

confidence: 99%

“…For example, in the jet mixing enhancement with one pulsed minijet, extremum-seeking control (ESC) has been applied to obtain automatically and rapidly the optimal excitation frequency f e,opt of the minijet as monitored by the maximum decay rate K max of the jet centerline mean velocity. This ESC adapts a working open-loop control (Fan et al 2017, Wu et al 2016). Zhang et al (2004a) used an in-time proportionalintegral-derivative (PID) control to suppress the flow-induced vibration on a square cylinder by generating a very small cylinder surface oscillation, achieving a control performance which outperforms optimized open-loop control.…”

Section: Introductionmentioning

confidence: 99%

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Jet mixing optimization using machine learning control

Fan

Zhou

et al. 2018

Exp Fluids

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We experimentally optimize mixing of a turbulent round jet using machine learning control (MLC) following Li et al (2017). The jet is manipulated with one unsteady minijet blowing in wall-normal direction close to the nozzle exit. The flow is monitored with two hotwire sensors. The first sensor is positioned on the centerline 5 jet diameters downstream of the nozzle exit, i.e. the end of the potential core, while the second is located 3 jet diameters downstream and displaced towards the shear-layer. The mixing performance is monitored with mean velocity at the first sensor. A reduction of this velocity correlates with increased entrainment near the potential core. Machine Learning Control (MLC) is employed to optimize sensor feedback, a general open-loop broadband frequency actuation and combinations of both. MLC has identified the optimal periodic forcing with small duty cycle as the best control policy employing only 400 actuation measurements, each lasting for 5 seconds. This learning rate is comparable if not faster than typical optimization of periodic forcing with two free parameters (frequency and duty cycle). In addition, MLC re-sults indicate that neither new frequencies nor sensor feedback improves mixing further-contrary to many of other turbulence control experiments. The optimality of pure periodic actuation may be attributed to the simple jet flapping mechanism in the minijet plane. The performance of sensor feedback is shown to face a challenge for small duty cycles. The jet mixing results demonstrate the untapped potential of MLC in quickly learning optimal general control policies, even deciding between open-and closed-loop control.

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Section: Discussioncontrasting

confidence: 66%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Jet mixing optimization using machine learning control

Fan

Zhou

et al. 2018

Exp Fluids

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“…In general, the actuators are triggered/driven once the targeted event (e.g. a quasi-periodic largescale organized structure in [19,41]) exceeds a specified level in the physics-based control schemes and, therefore, the actuator transfer function is not needed. In a turbulent boundary layer, the near-wall high-speed events are highly correlated with the significant rise in skin friction drag [9,10].…”

Section: Physics-based Control Schemes (A) Controller Designmentioning

confidence: 99%

“…The performance of physics-based control schemes highly depends on the choice of both physical quantities measured and the control parameters. We have previously developed the physics-based control schemes for jet mixing [41] and for suppressing vortex shedding and vibration of a square cylinder in crossflow [19]. These schemes showed excellent performances.…”

Section: Physics-based Control Schemes (A) Controller Designmentioning

confidence: 99%

Turbulent boundary layer under the control of different schemes

Qiao

Zhou

2017

Proc. R. Soc. A.

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This work explores experimentally the control of a turbulent boundary layer over a flat plate based on wall perturbation generated by piezo-ceramic actuators. Different schemes are investigated, including the feed-forward, the feedback, and the combined feed-forward and feedback strategies, with a view to suppressing the near-wall high-speed events and hence reducing skin friction drag. While the strategies may achieve a local maximum drag reduction slightly less than their counterpart of the open-loop control, the corresponding duty cycles are substantially reduced when compared with that of the open-loop control. The results suggest a good potential to cut down the input energy under these control strategies. The fluctuating velocity, spectra, Taylor microscale and mean energy dissipation are measured across the boundary layer with and without control and, based on the measurements, the flow mechanism behind the control is proposed.

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