Afterbody Drag Reduction Using Active Flow Control

Jackson, Richard; Wang, Zhijin; Gursul, Ismet

doi:10.2514/6.2017-0954

Cited by 13 publications

(5 citation statements)

References 21 publications

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“…4b). Based on the finding of Jackson et al (2019), to maximise the control effect the jet devices should be placed before x/c =0.15. Considering the typical pressure distribution on the slanted endplate and the practical geometric limitation, in the present experiments the sweeping jet was placed at x/c=0.12 (Fig.…”

Section: The Sweeping Jetmentioning

confidence: 99%

Control of afterbody vortices from a slanted-base cylinder using sweeping jets

et al. 2022

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In this paper, a sweeping jet is applied to control the afterbody vortices behind a slanted-base cylinder for the first time at Reynolds numbers from 87,000 to 200,000. The control effects are examined using stereo Particle Image Velocimetry (PIV) and surface pressure measurements with the jet momentum coefficient (Cμ) varying from 0.056 to 0.893. It is found that the sweeping jet results in increasingly diffused and larger afterbody vortices as Cμ increases. While an increase in Cμ up to 0.167 leads to a reduction in the circulation of the afterbody vortices and their earlier detachment from the slanted base, a further increase in Cμ introduces additional vorticity into the afterbody vortices leading to higher vortex strength, which could be detrimental to the control purpose. The interaction mechanism of sweeping jets lies in that turbulence is injected into the afterbody vortices as the sweeping jet intersects with these vortices and this subsequently causes diffusion of velocity gradient in the vortices which weakens their strength. As the sweeping jet spreads itself sideways while it propagates downstream along the endplate, it pushes the afterbody vortices upwards and to the side. The impact of the sweeping jet has resulted in the dominant vortex wandering frequency of the afterbody vortex being locked to that of the sweeping jet. This also causes the afterbody vortices detach themselves from the endplate earlier resulting in a shorter low-pressure footprint on the surface.

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Section: The Sweeping Jetmentioning

confidence: 99%

Control of afterbody vortices from a slanted-base cylinder using sweeping jets

et al. 2022

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“…The flow in the symmetry plane shown in Figure 20 reveals that the spoiler causes separation, and ingestion of turbulence is likely to be the reason for the diffused vortex. However, this case has the highest drag due to the negative Active flow control using continuous blowing has also been investigated for this geometry [63]. It was thought that blowing upstream in the vortex core would be the most effective way to disintegrate the afterbody vortices.…”

Section: E Drag Reduction Of Afterbody Vorticesmentioning

confidence: 99%

Flow Control of Tip/Edge Vortices

Gursul

Wang

2018

AIAA Journal

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Location, strength and structure of tip and edge vortices shed from wings and bodies can be manipulated by using flow control techniques. Flow physics of these approaches involve flow separation from the edge, roll-up into the vortex, wing flow regime, vortex instabilities, vortex-vortex interactions, and vortex-turbulence interactions. Actuators include continuous and unsteady blowing and suction, bleed, and control surfaces, which add momentum, vorticity and turbulence into the vortices. It is noted that actuation may have effects on more than one aspect of the flow phenomena. A comparative review of the control of delta wing vortices, tip vortices, and afterbody vortices is presented. The delay of vortex breakdown and the promotion of flow reattachment require different considerations for slender and nonslender delta wings, and may not be possible at all. Tip vortices can be controlled to increase the effective span, to generate rolling moment, to attenuate wing-rock, and to attenuate vortex-wing interactions. While there are different approaches for each application, opportunities for future research on turbulence ingestion, bleed, and excitation of vortex instabilities exist. Recent research also indicates that active and passive flow control can be used to manipulate the afterbody vortices in order to reduce the drag.

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“…There has been an interest in solving these equations for reliable active flow control strategies 7,10,19 . Active flow control strategies reduce noise emission and aerodynamic drag, lowering the energy consumption of large marine vehicles and aircraft 6,17 . To solve these partial differential equations, various numerical discretization techniques such as finite-volume or finite-element are frequently used 16,18,23 .…”

Section: Introductionmentioning

confidence: 99%

Combined space-time reduced-order model with 3D deep convolution for extrapolating fluid dynamics

Deo¹,

Gao²,

Jaiman³

2022

Preprint

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There is a critical need for efficient and reliable active flow control strategies to reduce drag and noise in aerospace and marine engineering applications. While traditional full-order models based on the Navier-Stokes equations are not feasible, advanced model reduction techniques can be inefficient for active control tasks especially with strong non-linearity and convection-dominated phenomena. Using convolutional recurrent autoencoder network architectures, deep learning-based reduced-order models have been recently shown to be effective while performing several orders of magnitude faster than full-order simulations. However, these models encounter significant challenges outside the training data, limiting their effectiveness for active control and optimization tasks. In this study, we aim to improve the extrapolation capability by modifying the network architecture and integrating coupled space-time physics as an implicit bias. Reduced-order models via deep learning generally employ decoupling in spatial and temporal dimensions, which can introduce modeling and approximation errors. To alleviate these errors, we propose a novel technique for learning coupled spatial-temporal correlation using a 3D convolution network. We assess the proposed technique against a standard encoder-propagator-decoder model and demonstrate a superior extrapolation performance. To demonstrate the effectiveness of the 3D convolution network, we consider a benchmark problem of the flow past a circular cylinder at laminar flow conditions and use the spatio-temporal snapshots from the full-order simulations. Our proposed 3D convolution architecture accurately captures the velocity and pressure fields for varying Reynolds numbers. Compared to the standard encoder-propagator-decoder network, the spatio-temporal-based 3D convolution network improves the prediction range of Reynolds numbers outside of the training data.

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Afterbody Drag Reduction Using Active Flow Control

Cited by 13 publications

References 21 publications

Control of afterbody vortices from a slanted-base cylinder using sweeping jets

Control of afterbody vortices from a slanted-base cylinder using sweeping jets

Flow Control of Tip/Edge Vortices

Combined space-time reduced-order model with 3D deep convolution for extrapolating fluid dynamics

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