Effectiveness of active flow control for turbulent skin friction drag reduction

Chung, Yongmann M.; Talha, Tariq

doi:10.1063/1.3553278

Cited by 69 publications

(71 citation statements)

References 30 publications

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“…However, control performance is strongly dependent on the location of the detection plane. Maximum drag reduction is achieved with the detector at y + d ≈ 15, and drag increases substantially for y + d > 25 (Chung & Talha 2011). studied the effectiveness of opposition control in turbulent pipe flow at R + = 180 (where R is the pipe radius, and superscript + denotes normalization with respect to the inner units, u τ and ν) and obtained similar results.…”

Section: Previous Results For Opposition Controlsupporting

confidence: 58%

“…In addition, the magnitude of v k exhibits a sharp dip at y + ≈ 10, halfway between the detection plane and the pipe wall (figure 3a). Similar to previous DNS observations (Hammond et al 1998;Chung & Talha 2011), this suggests that a virtual wall is established, which hinders the transfer of momentum ( figure 4b,c). Further, opposition control displaces the location of peak u k for this mode away from the critical layer to y + ≈ 22 (figures 3a and 4a).…”

Section: A Single Modesupporting

confidence: 73%

“…The model predicts that maximum drag reduction is achieved with the sensor at y + d ≈ 10, and that drag increases for y + d > 25. The maximum drag reduction predicted by the model (6 %) is much lower than that obtained in channel flow DNS (25 %) for similar Reynolds number, Re τ = 180 (Choi et al 1994;Chung & Talha 2011). Further, the increase in drag past y + d > 25 predicted by the model is less dramatic compared to the DNS results (figure 11a).…”

Section: Drag Reductionmentioning

confidence: 55%

“…DNS studies (Choi et al 1994;Chung & Talha 2011) show that opposition control yields skin friction reductions as large as 25 % at low Reynolds number in channel flows (Re τ = u τ h/ν = 180, based on channel half-height h, friction velocity u τ and kinematic viscosity ν). However, control performance is strongly dependent on the location of the detection plane.…”

Section: Previous Results For Opposition Controlmentioning

confidence: 99%

“…Figure 12 shows model predictions for the drag reduction possible under opposition control executed with varying amplitude |A d | blowing and suction (2.9). In a recent DNS study, Chung & Talha (2011) showed that the deterioration in opposition control performance with increasing sensor elevation (figure 11a) can be offset by decreasing the amplitude of the blowing and suction generated at the wall. More specifically, Chung & Talha (2011) showed that the optimal amplitude decreases from A d = 1 for…”

Section: Drag Reductionmentioning

confidence: 99%

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Opposition control within the resolvent analysis framework

2014

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as an example. Under this formulation, the velocity field for turbulent pipe flow is decomposed into a series of highly amplified (rank-1) response modes, identified from a gain analysis of the Fourier-transformed NavierStokes equations. These rank-1 velocity responses represent propagating structures of given streamwise/spanwise wavelength and temporal frequency, whose wall-normal footprint depends on the phase speed of the mode. Opposition control, introduced via the boundary condition on wall-normal velocity, affects the amplification characteristics (and wall-normal structure) of these response modes; a decrease in gain indicates mode suppression, which leads to a decrease in the drag contribution from that mode. With basic assumptions, this rank-1 model reproduces trends observed in previous direct numerical simulation and large eddy simulation, without requiring high-performance computing facilities. Further, a wavenumber-frequency breakdown of control explains the deterioration of opposition control performance with increasing sensor elevation and Reynolds number. It is shown that slower-moving modes localized near the wall (i.e. attached modes) are suppressed by opposition control. Faster-moving detached modes, which are more energetic at higher Reynolds number and more likely to be detected by sensors far from the wall, are further amplified. These faster-moving modes require a phase lag between sensor and actuator velocity for suppression. Thus, the effectiveness of opposition control is determined by a trade-off between the modes detected by the sensor. However, it may be possible to develop control strategies optimized for individual modes. A brief exploration of such mode-optimized control suggests the potential for significant performance improvement.

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Section: Previous Results For Opposition Controlsupporting

confidence: 58%

Section: A Single Modesupporting

confidence: 73%

Section: Drag Reductionmentioning

confidence: 55%

Section: Previous Results For Opposition Controlmentioning

confidence: 99%

Section: Drag Reductionmentioning

confidence: 99%

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Opposition control within the resolvent analysis framework

2014

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Turbulent Drag Reduction by Travelling Waves of Spanwise Lorentz Force

Yang

Chung

2023

Flow Turbulence Combust

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Travelling waves induced by spanwise Lorentz force for skin-friction drag reduction are studied in the present work using direct numerical simulations, with particular focus on the streamwise and spanwise travelling waves. The overall picture of drag reduction in the frequency-wavenumber parameter space, $$\omega -\kappa _x-\kappa _z$$ ω - κ x - κ z is uncovered. It is found that both drag reduction maps for the streamwise and spanwise travelling waves are featured with a drag reduction ($$\mathcal{D}\mathcal{R}$$ D R ) region and a drag increase ($$\mathcal{D}\mathcal{I}$$ D I ) region. For the streamwise travelling wave of spanwise Lorentz force, the $$\mathcal{D}\mathcal{R}$$ D R and $$\mathcal{D}\mathcal{I}$$ D I regions are located in the same parameter regime compared to that of the streamwise travelling wave of spanwise wall velocity, while for the spanwise travelling wave, the $$\mathcal{D}\mathcal{R}$$ D R variation at any fixed $$\kappa _z$$ κ z is similar to that of spanwise oscillating Lorentz force. An exploration of the oblique travelling wave with an angle to the mean flow shows that the optimal drag reduction appears when the wave travels backward relative to the flow direction, and the "ribbon" structure is a general phenomenon appearing in all oblique travelling wave cases. The ensemble averaged positive and negative quasi-streamwise vortices become asymmetric when the travelling wave is imposed, and the near-wall high- and low-speed streaks are significantly tilted in the spanwise direction. Spanwise oscillation, streamwise and spanwise travelling waves share strong similarity in the statistics, energy spectra as well as turbulent structure modulation.

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Deep Reinforcement Learning for the Management of the Wall Regeneration Cycle in Wall-Bounded Turbulent Flows

Cavallazzi,

Guastoni,

Vinuesa

et al. 2024

Flow Turbulence Combust

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The wall cycle in wall-bounded turbulent flows is a complex turbulence regeneration mechanism that remains not fully understood. This study explores the potential of deep reinforcement learning (DRL) for managing the wall regeneration cycle to achieve desired flow dynamics. To create a robust framework for DRL-based flow control, we have integrated the StableBaselines3 DRL libraries with the open-source direct numerical simulation (DNS) solver CaNS. The DRL agent interacts with the DNS environment, learning policies that modify wall boundary conditions to optimise objectives such as the reduction of the skin-friction coefficient or the enhancement of certain coherent structures’ features. The implementation makes use of the message-passing-interface (MPI) wrappers for efficient communication between the Python-based DRL agent and the DNS solver, ensuring scalability on high-performance computing architectures. Initial experiments demonstrate the capability of DRL to achieve drag reduction rates comparable with those achieved via traditional methods, although limited to short time intervals. We also propose a strategy to enhance the coherence of velocity streaks, assuming that maintaining straight streaks can inhibit instability and further reduce skin-friction. Our results highlight the promise of DRL in flow-control applications and underscore the need for more advanced control laws and objective functions. Future work will focus on optimising actuation intervals and exploring new computational architectures to extend the applicability and the efficiency of DRL in turbulent flow management.

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Effectiveness of active flow control for turbulent skin friction drag reduction

Cited by 69 publications

References 30 publications

Opposition control within the resolvent analysis framework

Opposition control within the resolvent analysis framework

Turbulent Drag Reduction by Travelling Waves of Spanwise Lorentz Force

Deep Reinforcement Learning for the Management of the Wall Regeneration Cycle in Wall-Bounded Turbulent Flows

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