Effect of wind tunnel wall porosity on the flow around an oscillating airfoil at transonic speeds

Golestani, Ali; Soltani, Mohammad Reza; Masdari, Mehran

doi:10.24200/sci.2017.4089

Cited by 1 publication

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References 12 publications

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“…In this case, Golestani et al [20] captured a shock-wave location using schlieren imaging and presented a new method for detecting the bu et onset using pressure distribution data on the current supercritical airfoil. Additionally, they investigated the e ects of wall porosity on the experimental measurements over the same supercritical airfoil [21]. In addition to applying common methods to detect the bu et onset, the speci c aim of the current paper is to assess bu et frequency by applying the cross-correlation method to both pressure distribution and wake data behind the model.…”

Section: Introductionmentioning

confidence: 99%

Experimental Investigation of Shock Wave Oscillation on a Thin Airfoil

et al. 2018

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Experimental results of surface pressure distribution over a thin supercritical airfoil and its wake are presented. All tests were conducted at free stream Mach numbers ranging from 0.27 to 0.85 and at di erent angles of attacks in a transonic wind tunnel. The model was equipped with static pressure ori ces connected to high-frequency pressure transducers. The present paper evaluates variations of shock wave location with both Mach number and angle of attack variation, as well as its interaction with the boundary layer, leading to the bu et phenomenon. Note that, for this thin supercritical airfoil, there exist only a few experimental results regarding surface pressure distributions, corresponding forces and moments, and the shock wave oscillations and its behavior with various ow conditions. The frequency of the shock wave oscillation and unsteady wake behavior at a freestream Mach no. of M 1 = 0:66 and at di erent angles of attacks are measured by the cross-correlation technique by means of pressure sensors located on the suction side of the model and via the rake total pressure data that was traversed vertically behind the model, respectively. From the analysis of surface pressure distribution and wake data, drag divergence occurred at a certain angle of attack and at a frequency equal to the shock wave oscillation frequency.

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Section: Introductionmentioning

confidence: 99%