Near-field aeroacoustic characteristics of a stalled
NACA 0012 airfoil
Yannick D. Mayer∗, B. Zang†, Mahdi Azarpeyvand‡
Faculty of Engineering, University of Bristol, United Kingdom

ABSTRACT
Stall and separation noise can be major contributors to the noise emission of rotating blades, such as
wind turbines, propellers and fans among others. Hence, this paper experimentally investigates theeffects a separated boundary layer has on near-field flow structures, on turbulent surface pressure fluctuation statistics, and therefore on noise radiation. Static pressure distributions and boundary layer
profiles are presented to characterize the present flow fields pre- and post-stall. Large surface pressure fluctuation spectra increases have been measured, and surface pressure fluctuation autocorrelation
width increases by one order of magnitude during stall have been established. Additionally, highly coherent spanwise structures have been shown to influence the hydrodynamic field and changes in theconvection velocity have been assessed for separated boundary layers.
Keywords: Aeroacoustics, Separation and Stall Noise, Surface Pressure Fluctuations, Convection Velocity, Flow Separation.

1

INTRODUCTION

Brooks et al. reported five different airfoil self-noise mechanisms (1). For the majority of today’s industrial applications, the most relevant self-noise mechanisms are turbulent boundary layer trailing edge noise,laminar boundary layer vortex shedding noise and separation/stall noise. Trailing-edge noise and laminar
vortex shedding noise have been the subject of many studies over the years (1–7), but stall/separation noise
investigations are comparably limited. This is despite stall noise being assumed to be a significant contributor to wind turbine noise emissions due to sudden changes in environmental conditions, such as gust or
topographical effects for instance (8, 9). Since computational stall noise investigations require a substantial
spanwise domain to achieve sufficient de-correlation of the present flow structures (10), the majority of stall
noise studies have been of experimental nature, for instance by Moreau et al. (11), Mayer et al. (12), Bertagnolio et al. (13) and Laratro et al. (14) among others. The experimental results clearly point toward a shift tolow frequency noise emissions and considerable low frequency surface pressure spectra increases.
This paper aims to further experimentally examine and characterize the hydrodynamic near-field duringstall. Section 2 will introduce the experimental setup as well as the utilized symmetric airfoil, before unsteadysurface pressure fluctuation and hot wire results are presented in Section 3. Lastly, Section 4 will provide
conclusions and an outlook on future work.
∗ Ph.D. Researcher Aerospace Engineering, yannick.mayer@bristol.ac.uk.
† Research Associate Aerospace Engineering, nick.zang@bristol.ac.uk.
‡ Corresponding Author, Reader in Aeroacoustics, m.azarpeyvand@bristol.ac.uk.

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2

EXPERIMENTAL SETUP

2.1 Overview
Experiments were conducted in the aeroacoustic wind tunnel facility of the University of Bristol (15). An
overview of the experimental setup and a rendering of the anechoic chamber are shown in Fig. 1. This study
used a nozzle with the exit dimensions of 500 mm width and 775 mm height at a free stream velocity of
U∞ = 20 m s−1 , corresponding to a chord-based Reynolds number of Rec = 4.0·105 for the utilized NACA 0012
airfoil. A 1500 mm long test section with tensioned Kevlar cloth at the top and bottom is employed in order
to limit excessive flow deflection, while permitting acoustic sound waves to pass through with little attenuation. Mayer et al. characterized this Kevlar-walled test section acoustically as well as aerodynamically andshowed that geometric angles of attack can be corrected to the equivalent free-stream angle of attack (16).
Each of the two vertical sides has three perspex windows allowing test objects and measurement equipment
to be installed or inserted. Neoprene strips and insulation tape have been used throughout to avoid any
extraneous noise sources such as air leaks, gaps or cavities. The angle of attack of the NACA 0012 airfoil
with a chord length of 300 mm is controlled by a high torque servo motor.

(a)

(b)

Figure 1. (a) Anechoic chamber and (b) Isometric rendering of the experimental setup: (A) servo motor, (B)coupling and bearing, (C) NACA 0012 airfoil and (D) tensioned Kevlar cloth.

2.2 NACA 0012 airfoil
The NACA 0012 airfoil has a chord length of c = 300 mm and it is tripped by a 6 mm wide and 0.5 mm
thick zig-zag turbulator trip tape from Glasfaser-Flugzeug-Service GmbH (17), located at approximately
13 % of the chord on both airfoil sides. The airfoil is equipped with 87 static pressure taps together with
91 embedded microphones placed under 0.4 mm diameter pinholes, of which 21 are operated in a remotesensing configuration. Both the static pressure taps and unsteady pressure transducers are aligned along
the chord as well as along the span at several chordwise locations. Knowles FG-23629-P16 microphones areused as the in-situ direct sensing microphone and Panasonic WM-61A microphones are used for the remotesensing configurations. All microphones were calibrated in magnitude and phase referenced to a G.R.A.S.
40PL microphone.

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2.3 Data acquisition and postprocessing
All microphone and hot wire measurements were sampled simultaneously at a frequency of 215 Hz for 32 s
using five 16-channel NI PXIe-4499 modules mounted in a NI PXIe-1062Q chassis. All microphone signals
are zero-phase high-pass filtered at 20 Hz, to account for the low-frequency limit of both microphones. A
Dantec 55P15 boundary layer hot wire probe was traversed using two Thorlabs LTS300 stages and operated
via a CTA91C10 module. The probe was calibrated daily using a Dantec 54H10 calibrator. The power spectral density of the surface pressure fluctuations, φ pp , is estimated via Welch’s method using a window size of
211 samples and a Hamming window with 50 % overlap, resulting in a frequency bin size of 16 Hz. All surface pressure spectra are normalized with a reference pressure of p0 = 20 µPa. Lastly, 64 static pressure taps
were simultaneously connected to two MicroDaq-32 pressure acquisition systems, manufactured by Chell,and data are recorded at 1000 Hz for a duration of 32 s.

3

RESULTS AND DISCUSSION

This section will present test results including static surface pressure results, unsteady surface pressure results and hot wire anemometry results. The geometric angle of attack, α, was altered from −5◦ to 23◦ inincrements of α = 0.5◦ . However, this conference paper will mainly focus on one pre-stall angle of attack,α = 12◦ , and one post-stall angle of attack, α = 15◦ .

3.1 Pressure distribution
Figure 2 displays the mean static pressure coefficient for five angles of attack up to α = 15◦ . As the angle ofattack is increased, the suction side pressure peak increases and shifts toward the leading edge, as expected,
and for α = 10◦ and α = 12◦ a laminar separation bubble is evidently present near the leading edge due to astrong adverse pressure gradient. At α = 15◦ , the boundary layer has separated near the leading edge andthe pressure distribution is effectively constant for the suction side, confirming a sudden leading-edge typestall behavior. Additionally, the effect of the trip strip is clearly visible except for α = 15◦ .

(a)Figure 2. Static pressure coefficient, C p , distribution at Rec(b)= 4.0 · 105 : (a) 0◦ to 10◦ and (b) 10◦ to 15◦ .

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3.2 Boundary layer measurements
This section presents single-wire hot wire measurements for varying airfoil wall normal distances (zw ),namely mean velocity and velocity fluctuation profiles normalized with the boundary layer edge velocity, Ue , in Fig. 3 up to α = 15◦ . It is illustrated that the boundary layer thickness increases for increasing
angle of attack. Once, the boundary layer has separated at α = 15◦ , the velocity profile suggests that up to
zw /c = 0.22, a reverse flow is present, because of the plateau in the velocity distribution up to zw /c = 0.22
(18). Additionally, the peak of the surface pressure fluctuations has moved away from the airfoil surface,
increased in magnitude and now occurs at zw /c = 0.32, while the extent of the separated boundary layer
approaches half the airfoil chord length.

(a)

(b)

Figure 3. Boundary layer measurement at x/c = 0.98: (a) α = 0◦ to 10◦ and (b) α = 10◦ to 15◦ .

3.3 Surface pressure fluctuation PSD
The remaining sections examine the unsteady surface pressure fluctuations as measured by the embedded
microphones. This particular section focuses on power spectral density (PSD) of the unsteady surface pressure fluctuations to examine the energy distribution in the frequency domain.
Figure 4 presents the surface pressure spectra near the leading edge at x/c = 0.3 and near the trailingedge at x/c = 0.99. For both pressure transducer locations the peak frequency shift to lower frequencies forincreasing angles of attack is clear, as the peak spectrum value also increases from 65 dB to 95 dB and from
70 dB to 101 dB for x/c = 0.3 and x/c = 0.99, respectively. The frequency shifts and spectra increases occur asa result of a growing boundary layer and subsequent flow separation due to the adverse pressure gradienton the suction side of the airfoil. A thicker boundary layer results in larger length scales in the flow, which inturn leads to lower peak frequencies. It is noticeable that the most significant change occurs between α = 12◦
and α = 15◦ , highlighting the profound effect stall has on the surface pressure spectra. This rapid and abruptchange can also be observed in Fig. 5, showing contour plots of the surface pressure fluctuation spectra up
to α = 15◦ for transducers covering the space between x/c = 0.3 and x/c = 0.99.

3.4 Surface pressure autocorrelation
The autocorrelation coefficient width, R pp (τ) (where τ is the time delay), is indicative of the size of the dominant flow structures causing the surface pressure fluctuations. Figure 6 displays a contour map of the variation of the autocorrelation coefficient along the suction side of the airfoil for α = 12◦ and α = 15◦ , with the

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(a)

(b)

Figure 4. Wall pressure fluctuation PSD for various angles of attack: (a) x/c = 0.3 and (b) x/c = 0.99.

(a)

(b)

Figure 5. Wall pressure fluctuation PSD contour plot: (a) x/c = 0.3 and (b) x/c = 0.99.

(a)

(b)

Figure 6. Wall pressure fluctuation autocorrelation, R pp , contour maps: (a) α = 12◦ and (b) α = 15◦ . First zerocrossing is displayed as a white line.

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white line displaying the first zero-crossing of the autocorrelation coefficient. At α = 12◦ , the autocorrelation
width increases strongly toward the trailing edge due to the strong adverse pressure gradient resulting ina thicker boundary layer, see Figs. 2 and 3. This trend is in stark contrast to the contour map at α = 15◦ ,because the autocorrelation width is approximately one order of magnitude larger in comparison, but shows
less variation along the chord. This therefore suggests that the dominant flow structures have undoubtedly
increased in size, and occur over a large extent of the airfoil without significantly evolving or changing insize.

3.5 Surface pressure spanwise coherence
Figure 7 displays the spanwise coherence spectra, γ 2 , for seven pressure transducers located at x/c = 0.70. On
the one hand, it can be observed that at α = 12◦ the coherence spectra reach considerable values for the sensor
separation distances of up to ξ2 /c = 0.047 and the spectra reach local maxima at approximately 200 Hz. On
the other hand, at α = 15◦ , substantial coherence values are observed even for the largest sensor separation
distance of ξ2 /c = 0.243, and for separation distances of up to ξ2 /c = 0.047, the coherence for frequencies
below 100 Hz almost reaches unity. This coherence increase can be interpreted as the occurrence of spanwise
highly coherent flow structures which are supported by the large vertical extent of the separation region.

(a)

(b)

Figure 7. Wall pressure fluctuation coherence, γ 2 , for multiple spanwise distances, ξ2 , at x/c = 0.70: (a) α = 12◦
and (b) α = 15◦ .

3.6 Surface pressure cross-correlation
This section concerns the cross-correlation coefficient between the surface pressure fluctuations at varying
chordwise distances, ξ1 , allowing the determination of the convection velocity, Uc , of the most prominent
flow structures, as
Uc
ξ1 /c
=
,
U∞
τmaxU∞ /c(1)

where τmax is the time delay with the maximum cross-correlation coefficient. Figure 8 displays the chordwise
cross-correlation coefficient referenced to the pressure transducer located at x/c = 0.93, while Table 1 lists thecorresponding convection velocities, calculated via Eq. 1. It is apparent that for α = 0◦ and 10◦ , the convection velocity increases for greater sensor separation distances, because the larger flow structures which
travel further away from the airfoil surface have longer live spans and are convected at larger surface normal

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Table 1. Serration parameters.
Sensor separation
distance, ξ1 /c [−]Uc /U∞ for
α = 0◦ [−]Uc /U∞ for
α = 12◦ [−]Uc /U∞ for
α = 15◦ [−]

0.033
0.050
0.067
0.083
0.100

0.63
0.64
0.70
0.72
0.72

0.53
0.56
0.56
0.59
0.60

- 0.78
2.73
0.94
0.72
0.62

distances. The highest convection velocity at α = 0◦ is Uc /U∞ = 0.72 and Uc /U∞ = 0.60 for α = 12◦ , suggestinga decrease with increasing angles of attack as long as the boundary layer stays attached. However, in stalled
conditions, i.e. α = 15◦ , the convection velocity is calculated to be negative for the smallest sensor spacing of
ξ1 /c = 0.033 and finally reaches Uc /U∞ = 0.62 for the greatest separation distance investigated here.

(a)

(b)

Figure 8. Wall pressure fluctuation cross-correlation, R pp , for various sensor separation distances, with thereference sensor located at x/c = 0.93: (a) α = 12◦ and (b) α = 15◦ .

4

FINAL REMARKSThis paper investigated the effect of flow separation on near-field aeroacoustic properties, such as surfacepressure spectra and convection velocity. Substantial and sudden increases were found in both surface pressure spectra and autocorrelation widths once the boundary layer had separated. Additionally, locally negative convection velocities and a decrease in the convection velocity were established in the presence of flow
separation. The results provided in this paper are a small snapshot of the total data set available from thehighly instrumented airfoil.
Future investigations will include further flow field characterizations using dual sensor hot wire probes,
higher order analysis of the surface pressure fluctuations, as well as far-field noise studies. In addition, dynamic stall noise and stall noise mitigation approaches will be examined.

ACKNOWLEDGEMENTS
The first author gratefully acknowledges the financial support of the ICA-ASA-DEGA Young Scientists Conference Grant and all authors acknowledge the support of the Engineering and Physical Sciences ResearchCouncil (EPSRC Grant No. EP/R010846/1).

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