Active Flow Control of a High-Lift Supercritical Airfoil with Microjet Actuators

Aley, Kade; Guha, Tufan K.; Kumar, Rajan

doi:10.2514/1.j058939

Cited by 14 publications

(7 citation statements)

References 15 publications

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“…[47,48]. The effectiveness of microjets in drag reduction was experimentally studied by Aley et al [49] in a simplified 2D wing; a significant wake velocity deficit reduction and, thus, drag, was observed when using the microjets actuation.…”

Section: Active Control Of Separationmentioning

confidence: 99%

Flow Control in Wings and Discovery of Novel Approaches via Deep Reinforcement Learning

Vinuesa

Lehmkuhl

Lozano-Durán

et al. 2022

Fluids

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In this review, we summarize existing trends of flow control used to improve the aerodynamic efficiency of wings. We first discuss active methods to control turbulence, starting with flat-plate geometries and building towards the more complicated flow around wings. Then, we discuss active approaches to control separation, a crucial aspect towards achieving a high aerodynamic efficiency. Furthermore, we highlight methods relying on turbulence simulation, and discuss various levels of modeling. Finally, we thoroughly revise data-driven methods and their application to flow control, and focus on deep reinforcement learning (DRL). We conclude that this methodology has the potential to discover novel control strategies in complex turbulent flows of aerodynamic relevance.

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Section: Active Control Of Separationmentioning

confidence: 99%

Flow Control in Wings and Discovery of Novel Approaches via Deep Reinforcement Learning

Vinuesa

Lehmkuhl

Lozano-Durán

et al. 2022

Fluids

View full text Add to dashboard Cite

show abstract

“…[47,48]. The effectiveness of microjets at drag reduction was experimentally studied by Aley et al [49] in a simplified 2D wing; a significant wake velocity deficit reduction and thus, drag was observed when using the microjets actuation.…”

Section: Active Control Of Separationmentioning

confidence: 99%

Flow Control in Wings and Discovery of Novel Approaches via Deep Reinforcement Learning

Vinuesa¹,

Lehmkuhl²,

Lozano-Durán³

et al. 2022

Preprint

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In this review we summarize existing trends of flow control used to improve the aerodynamic efficiency of wings. We first discuss active methods to control turbulence, starting with flat-plate geometries and building towards the more complicated flow around wings. Then, we discuss active approaches to control separation, a crucial aspect towards achieving high aerodynamic efficiency. Furthermore, we highlight methods relying on turbulence simulation, and discuss various levels of modelling. Finally, we thoroughly revise data-driven methods, their application to flow control, and focus on deep reinforcement learning (DRL). We conclude that this methodology has the potential to discover novel control strategies in complex turbulent flows of aerodynamic relevance.

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“…(1) The massive flow separation on the flap at large deflections limits the lift performance (Aley et al, 2020;Chu et al, 2012), and the Fowler flap has no effective technical measures to eliminate separation; (2) Positions of the flap side edge and external fairing, as well as flow phenomena such as separation vortexes and boundary layer mixing of the main element and flap, are sources of airframe noise (Ma et al, 2020;Zhang, 2010); (3) Retraction/extension mechanisms of the Fowler flap increase both the operating empty weight and cruise excrescence drag. Studies have shown that removing external fairings can reduce the overall cruise drag by up to 3.3 counts (1 count = 0.0001), resulting in fuel savings of 2.25% (Hartwich et al, 2014(Hartwich et al, , 2017.…”

Section: Introductionmentioning

confidence: 99%

“…Passive and active flow control technologies are potential strategies to solve the above problems, meeting the requirements of a high-efficiency, simple, and low-noise design for new-generation civil aircraft high-lift devices (Jimenez et al, 2011). Moreover, high-lift devices (Aley et al, 2020;Chu et al, 2012;Hartwich et al, 2014;Steinfurth & Haucke, 2018) are also one of the most promising applications for flow control technology.…”

Section: Introductionmentioning

confidence: 99%

Numerical study on technical and conceptual improvements to a civil aircraft trailing-edge flap using passive/active flow control

Wang

Zhang

2021

Engineering Applications of Computational Fluid Mechanics

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Future civil aircrafts require simple, low-noise, and highly efficient trailing-edge flaps. In this study, a detailed numerical study for flap improvements using steady and unsteady Reynolds-averaged Navier-Stokes computations was conducted. A movable trailing edge (MTE) was applied to the main element as a passive flow control method to improve the traditional Fowler flap. A new concept of a backward seamless flap (BSLF) based on the MTE configuration is proposed, which can eliminate external fairings, simplify the mechanisms, and reduce the cruise drag. Then, a wall jet active flow control method was used to improve the BSLF. The improvement effect, flow mechanism, and parameter influences were investigated in detail. The results indicated that the MTE can increase the lift coefficient in the linear segment by 1.2-1.4 for an aerofoil and 0.79 for a wing by controlling the slot flow, energy distribution, and circulation. Moreover, the BSLF provided an improvement in performance higher than 27% over the plain flap, and the wall jet controlled BSLF configuration could reach or even exceed the MTE configuration in terms of performance. The zero-mass jet had a higher control effect and efficiency than the continuous jet. This study highlighted the significance and engineering value for technical improvement and concept exploration of new-generation high-lift devices.

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Active Flow Control of a High-Lift Supercritical Airfoil with Microjet Actuators

Cited by 14 publications

References 15 publications

Flow Control in Wings and Discovery of Novel Approaches via Deep Reinforcement Learning

Flow Control in Wings and Discovery of Novel Approaches via Deep Reinforcement Learning

Flow Control in Wings and Discovery of Novel Approaches via Deep Reinforcement Learning

Numerical study on technical and conceptual improvements to a civil aircraft trailing-edge flap using passive/active flow control

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