Skin-friction reduction using periodic blowing through streamwise slits

Cheng, Xiuwen; Qiao, Z. X.; Zhang, X.; Quadrio, Maurizio; Zhou, Yu

doi:10.1017/jfm.2021.439

Cited by 25 publications

(6 citation statements)

References 59 publications

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“…This phenomenon is capable of interfering with self-sustaining near-wall streamwise vortices and coherent structures, and expected to further interfere with the structure of large-scale vortices in the outer region of the TBL, thereby regulating the influence of the structure of largescale vortices on the streamwise vortices within the inner boundary layer. However, the results of previous experiments indicate that more input energy to the plasma actuator does not correspond to a better outcome [18,30]. First, an excessively high actuation voltage can cause the insulating layer of the plasma actuator to age prematurely; after the insulating layer ages, the equivalent resistance decreases, the internal current of the plasma actuator increases, and heat accumulates quickly on the surface of the insulating layer, which causes the plasma actuator to break down, catch fire, or begin arcing.…”

Section: Repeatability Testmentioning

confidence: 89%

“…Second, when the jet velocity induced by the plasma actuator is too high, after the jet flow merges with the incoming flow, a synthetic jet is generated at a certain angle to the main flow direction and easily causes the flow to separate from the wall. Third, the application of excessively strong plasma actuation on the near-wall surface may accelerate the growth of self-sustaining streamwise vortices, thereby increasing the WSS [18]. Therefore, choosing the appropriate plasma actuation parameters according to external flow phenomena and the scale of the vortices is necessary for indepth study.…”

Section: Repeatability Testmentioning

confidence: 99%

“…To interfere with self-sustaining streamwise vortices more effectively, scholars have compared the TDR effects of streamwise blowing layouts and spanwise blowing layouts through experiments and simulations. One analysis revealed that the transverse traveling waves generated by spanwise blowing can induce additional streamwise vortices, weaken the burst frequencies of ejection and sweep events of the original streamwise vortices, and suppress self-sustaining streamwise vortices near the wall, thereby stabilizing streaky structures; therefore, blowing in the spanwise direction can achieve greater TDR than blowing in the streamwise direction [16][17][18][19][20][21]. Chen [18] conducted a TDR wind tunnel experiment with a pulsed slot jet and found that when the scale of blowing-induced vortices is similar to the thickness of the viscous sublayer, the flow control effectiveness of induced jet can act as a virtual barrier to streamwise vortices near the wall, which can isolate the interactions between the ejection and sweep events of streamwise vortices with the wall surface, reduce the turbulent WSS.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, spanwise blowing can act as a virtual barrier to near-wall streamwise vortices, as spanwise blowing isolates the interaction between the ejection and sweep events of streamwise vortices with the wall surface, and further enhances the TDR effect. Studies have shown that the current control method of slit blowing requires a gas source to generate airflow [14][15][16][17][18]21]. Although DBD can operate without a gas source, DBD cannot generate jets perpendicular to the wall [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

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Turbulent drag reduction by spanwise slot blowing pulsed plasma actuation

Zheng

Jin

et al. 2022

Plasma Sci. Technol.

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This paper studies the turbulent drag reduction (TDR) effectiveness of a flat-plate model using a series of slot blowing pulsed plasma actuator (SBP-PA) with a type of spanwise layout. Wind tunnel experiments are carried out under a Reynolds number of 1.445×104. Using a hot-wire anemometer and an electrical data acquisition system, the influences of millisecond pulsed plasma actuation with different burst frequencies and duty cycles on the microscale coherent structures near the wall of the turbulent boundary layer (TBL) have been studied. The experimental results show that the slot blowing pulsed plasma actuator can effectively reduce the TBL frictional drag. When the duty cycle exceeds 30%, the TDR rate is greater than 11%, and the optimal drag reduction rate of 13.69% is obtained at a duty cycle of 50%. Furthermore, optimizing the electrical parameters reveals that increasing the burst frequency significantly reduces the velocity distribution in the logarithmic region of the TBL. When the normalized burst frequency reaches f+=2πfpd/U∞=7.196, the optimal TDR effectiveness is 16.97%, indicating a resonance phenomenon between the pulsed plasma actuation and the microscale coherent structures near the wall. Therefore, reasonably selecting the electrical parameters of the plasma actuator is expected to significantly improve the TDR effect.

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Section: Repeatability Testmentioning

confidence: 89%

Section: Repeatability Testmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Turbulent drag reduction by spanwise slot blowing pulsed plasma actuation

Zheng

Jin

et al. 2022

Plasma Sci. Technol.

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“…Here the wall-normal velocity at the wall is set to have the opposite sign as the wall-normal velocity at some detection plane in the channel, a straightforward actuation in simulations. This method has been applied in simulations [9,10,11,12] and experiments (with some modifications) [13,14], and extensions exist to use just wall observations [15,16]. When heuristics are replaced with methods from optimal control theory, like model predictive control (MPC), the drag reduction far outperforms opposition control [17] while using the same actuation scheme.…”

Section: Introductionmentioning

confidence: 99%

Turbulence control in plane Couette flow using low-dimensional neural ODE-based models and deep reinforcement learning

Linot¹,

Zeng²,

Graham³

2023

Preprint

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The high dimensionality and complex dynamics of turbulent flows remain an obstacle to the discovery and implementation of control strategies. Deep reinforcement learning (RL) is a promising avenue for overcoming these obstacles, but requires a training phase in which the RL agent iteratively interacts with the flow environment to learn a control policy, which can be prohibitively expensive when the environment involves slow experiments or large-scale simulations. We overcome this challenge using a framework we call "DManD-RL" (datadriven manifold dynamics-RL), which generates a data-driven low-dimensional model of our system that we use for RL training. With this approach, we seek to minimize drag in a direct numerical simulation (DNS) of a turbulent minimal flow unit of plane Couette flow at Re = 400 using two slot jets on one wall. We obtain, from DNS data with O(10 5 ) degrees of freedom, a 25-dimensional DManD model of the dynamics by combining an autoencoder and neural ordinary differential equation. Using this model as the environment, we train an RL control agent, yielding a 440-fold speedup over training on the DNS, with equivalent control performance. The agent learns a policy that laminarizes 84% of unseen DNS test trajectories within 900 time units, significantly outperforming classical opposition control (58%), despite the actuation authority being much more restricted. The agent often achieves laminarization through a counterintuitive strategy that drives the formation of two low-speed streaks, with a spanwise wavelength that is too small to be self-sustaining. The agent demonstrates the same performance when we limit observations to wall shear rate.

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