AIAC-2013-013

7. ANKARA INTERNATIONAL AEROSPACE CONFERENCE
11-13 September 2013 - METU, Ankara TURKEY

LONG-DISTANCE MicroPIV MEASUREMENTS OF A COMMERCIAL AIRCRAFT MODEL AT LOWREYNOLDS NUMBER

F. Gökhan Ergin1

Nafiz Alemdaroğlu2

Dantec Dynamics
Skovlunde, Denmark

METU
Ankara, Turkey
ABSTRACT

Velocity measurements from a sufficietly long standoff distance with micron scale resolution have significant
potential in boundary layer flow diagnostics and in resolving small turbulent structures. Long-distance microParticle Image Velocimetry (LDµPIV) technique is applied for velocity measurements around a commercial
aircraft model. Experiments are performed using a closed-return open test section wind tunnel in combination
with a single-camera LDµPIV system. Two-dimensional, two-component (2D2C) velocity measurements are
performed in a 12mm x 12mm field of view around the fuselage nose and in the wake of nose landing gear. The
velocities are calculated using proven adaptive cross correlation algorithms. The instantaneous and averaged
vector map results indicate that the LDµPIV provides sufficient spatial resolution to perform nose stagnation
flow, fuselage boundary layer and nose landing gear wake measurements.
INTRODUCTION
Boundary layer velocity measurements around streamlined bodies are often performed using point techniques
like Constant Temperature Anemometry (CTA) or Laser Doppler Anemometry (LDA) due to their small
measurement volumes and high spatial resolution. Although CTA provides a better frequency response andspatial resolution compared to LDA, it has a limitation in identifying the flow direction. LDA is preferred insituations where a flow reversal occurs, at the cost of reduced frequency response and/or spatial resolution.
Regardless of the choice, both measurements provide velocity information in a point, and the idenification ofglobal modes and transient flow structures is not trivial, if at all possible.
Alternatively, Particle Image Velocimetry (PIV) can provide velocity information in a plane or in a volume, andfinds many applications aerospace measurements; i.e. internal/external aerodynamics, seperation bubbles,wakes, etc. Availability of long distance microscopes allows velocity measurements with micron-scale
resolution from a distance, for example, in boundary layers. Since the technique is a variation of MicroPIV, it iscoveniently named as Long Distance Micro PIV (LDµPIV) [Adrian and Westerweel, 2010].
First application of LDµPIV was used in the investigation of logarithmic layer in a turbulent pipe flow
[Urushihara et al., 1993] where an in plane vector resolution of ~250µm was achieved using a magnification of
6 on photographic film. Second application of this technique was the investigation of boundary layer flow
disturbed by a small obstacle in a 1.0mmx1.3mm field of view (FOV) [Dieterle and Weichert, 1996] where theplane was illuminated from both directions to supress potential particle motion due to radiation pressure. It
was estimated that unidirectional laser radiation would accelerate stationary 1µm particles 0,5 m/s under theexperimental conditions.

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Microfluidics Product Manager, Email: gokhan.ergin@dantecdynamics.com
Prof. in Aerospace Engineering Department, Email: nafiz@metu.edu.tr

AIAC-2013-013

Ergin & AlemdaroğluLindken et al. [2002] used a reflecting type long-range microscope in a turbulent pipe flow experiment. The380-nm-diameter silica fluorescent particles were imaged from a distance of 550 mm. A vector resolution of42µm was achieved using 64x64 pixel interrogation area (IA), with number of spurious vectors less than 20% of
the total number.
Kähler et al. [2005, 2006] applied LDµPIV technique to measure the wall shear stress in a canonical boundary
layer over a flat plate in order to verify the applicability of the “universal law-of-the-wall” suggested by Von
Kármán. The authors systematically analysed the experimental limitations associated with seeding,illumination, out-of-focus particles, optical aberrations and correlation based vector calculations. The authors
selected a refracting long-range microscope, compansated for image distortions and oriented the laser sheet
illumination parallel to the surface to avoid unwanted reflections. Kähler et al. [2006] were successful insupressing the unwanted wall reflections to an extent that they could image particle reflections from the flat
surface. This information was used for the accurate estimation of the wall location. Kähler et al. [2005]
concluded that systematic measurement error due to radiation pressure was small during the experiment, andcan be neglected. Later, Kähler and Scholz [2006] applied the technique to investigate a laminar separation
bubble on a 2D airfoil and a laminar round jet with 1mm orifice, the latter campaign using intensified highspeed cameras.
Recently, Fiscaletti et al. [2013] investigate a turbulent air jet flow using LDµPIV and compare basic statistics
with CTA measurements with good agreement between the results. They also use a reflecting type long-range
microscope with 2.5x magnification at 580mm standoff distance, obtaining a vector resolution of 80µm with an
IA of 64x64 pixels. By masking the spurious vectors at the edges of the image, they were able to achive 94%
reliable vectors in the measurement.
EXPERIMENTS
The experiments are performed in an open-test-section, closed-return compact wind tunnel with manual speed
3control up to 20 m/s (Figure 1a). The open test section dimensions are 120x120x205 mm , and the 123-mmlong airplane model’s frontal area covers only ~3% of the test section’s cross sectional area. A smooth inlet
fairing downstream of the test section ensures a smooth reentry of the air back in the wind tunnel. The metal
airplane model is a 1:320-scale Boeing 737-800 manufactured by Shantou Wanchengfeng Craft Factory, and ismainly used as a gift to airline passangers. Additionally, the measurements are performed with the roll axis of
the airplane aligned with the flow direction, far from the landing/take off trim of an actual aircraft. Therefore
the results presented here do not aim to be indicative of the performance of the actual aircraft or actual flight
conditions. Instead, the aim is to apply the LDµPIV technique as a proof of concept in a small-scale experiment,
where the experience can be applied to future work planned on boundary layer measurements. Since themetal airplane model is not transparent and had a shiny surface it was painted to black color to reducereflections from the surface. The entire underbody of the fuselage was illuminated thru an opening using a
DualPower 65-15 PIV laser head situated below the test section.

Aircraftmodel
Tunnelspeedcontrol

Olive oilseedingLongdistanceµscopeLight
sheet
opticsSeeder(a)(b)

Figure 1: (a) Long-distance µPIV experiment (b) Image of the cross ruler microPIV calibration target
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The PIV laser system is a flashlamp-pumped, externally triggered, frequency-doubled, dual-cavity laser withNd:YAG crystals emitting light at 532nm wavelength. Pulse energy on each cavity is separately adjustable using
a motorized optical attenuator. Each cavity was operated at maximum pulse energy of 65mJ during theexperiments. A vertical light sheet with 37-degree full angle was formed using a combination of commercial
sheet formation optics; a 45-degree mirror, variable focus module and three cylindrical lenses. The light sheet
was focused right below the fuselage surface, which is at a distance of 500mm, producing a thickness of2320µm, a confocal length of 20mm and an energy density of 60mJ/cm . The confocal length of the light sheet is2larger than the 12.18x12.18mm FOV, therefore the illuminated volume thickness can be assumed fairly
constant in the entire FOV. The FOV is determined by performing a linear image calibration using a linear crossruler target with 100µm resolution (Figure 1b).
Olive oil droplets are used as light scatterers. 1µm-mean-diameter droplets are generated using Dantec
Dynamics 55L18 seeding generator and introduced in the wind tunnel downstream of the aircraft model forenhanced mixing and reduced flow disturbance (Figure 1a). Particle images are recorded in two regions, one infront of the fuselage nose (Uo=11m/s) and one in the aft of the nose landing gear (Uo=18m/s), from a nominal
distance of 500mm using the refractive long-distance microscope at 1.25x total magnification. In thisconfiguration the numerical aperture (NA) is 0.037 and the depth of filed (DoF) is 390µm. Single-exposure
image pairs are recorded with 12-bit depth using a FlowSense EO 4Mpix PIV camera, featuring a 55%-quantum
efficiency CCD detector with 2048 square pixels in each direction. A typical raw particle image is included in2Figure 3a. An intorrogation window of 32x32 pix sets the remaining dimensions of the measurement volume,
3i.e. 190x190x320µm , and the 16 pix grid spacing corresponds to 95µm vector resolution.
The particle response time assuming Stokes flow is calculated as 2.8µs ( τ p = d p ρ p / 18µ ), which is on the2same order of magnitude for the Kolmogorov time scale 2µs ( τ η = η / uη ). This means that the seeding
particles used in this experiment are not small enough to follow structures involved in turbulent dissipation( η = 6 µm), but are able to follow vortices that is limited by the vector resolution of the optical setup(~5x95µm).
The system synchronization is controlled with the system controller running DynamicStudio v3.41 software
platform, which is also used for hardware control, database management, image processing and PIV analysis.
Trigger signals are distributed thru an advanced synchronization unit with 12.5ns time resolution.
The adaptive cross-correlation (ACC) algorithm used in data processing is implemented in Dynamic Studiov3.41, the commercial PIV analysis package of Dantec Dynamics. Briefly, the implementation is an adaptive anditerative procedure: First, the displacement is calculated on an initial IA, which is larger in size compared to thefinal IA. Then the initial IA divided into smaller IA’s, which are shifted by the displacement calculated in theprevious step and deformed according to the velocity gradients in the image. Several passes can be madewithin each refinement step to further shift & deform the windows to minimize the in-plane particle dropout.
The window deformation is performed by adapting the IA shape to velocity gradients. For each IA size, thisprocedure is repeated until a convergence limit in pixels or a maximum number of iterations is reached. Then a
9-point Gaussian fit is performed on the correlation peak to obtain the displacement field with subpixelaccuracy in each pass. A number of FFT window (Hanning, Hamming etc.) and filter functions can be applied
during the analysis. Finally, spurious vectors are identified and replaced with a number of validation schemes
including peak height, peak height ratio, SNR & Universal Outlier Detection (UOD) [Westerweel and Scarano,2005].
RESULTSThe vector results in Figure 2 are obtained averaging 61 vector maps at a nominal freestream velocity of
11m/s. Instantenous vector maps are computed after an image background subtraction based on the minimum
pixel value in the image ensamble. An ACC algorithm is used with a 50% overlapped, 32-pixel-wide square IA
with no window refinement steps. A phase-only Gaussian filter is used in order to make the correlation more
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tolerant to variations in the background. Spurious vectors are identified and replaced using a peak height ratio
validation scheme (>1.2) in combination with a UOD [Westerweel and Scarano, 2005] in a 3x3 neighborhoodwith a detection threshold of 2.0 and a normalization level of 0.1 pixels. The convergence limit for deforming
windows was 0.01 pixels or a maximum 10 iterations. No further FFT windowing or local smoothing is applied
during the analysis. Every third vector is displayed in the horizontal plane and every second vector is displayedin the vertical plane. The number of spurious vectors was not more than 2% of the total number in the
instantaneous vector maps. The average vector map is displayed with U and V components separately (Figure2a, 2b) where stagnation flow at the nose tip is observed. An average boundary layer profile U(y) along theblue line in Figure 2a is also displayed in Figure 2c without further smoothing. The vertical distance ismeasured in mm, but the origin is placed at an arbitrary location during image calibration. Since the profile is
not obtained perpendicular to the surface, it cannot be compared to the Blasius profile. However, the flow inthis region is quite laminar with no indication of turbulence in the instaneous vector maps.

(a)

(b)

(c)

Figure 2: Average velocity results around the fuselage nose, with colors indicating (a) U component,(b) V component. (c) Boundary layer profile obtained along blue line in (a).

(a)

(b)

(c)

Figure 3: (a) Typical raw particle image, (b) average vector map with colors indicating vector length(c) velocity profile in the landing gear wake obtained along the red line in (a)The vector results in Figure 3 are also obtained averaging 61 vector maps, but this time at a nominal
freestream velocity of 18m/s. Instantenous vector maps are computed in a similar fashion as explained above,
except the IA size, which is chosen as 64x64 pixels. The number of spurious vectors was approximately 5% of
the total number in the instantaneous vector maps. Once again the vector spacing is 16 pixels, so Figure 3b
has the same vector density as in Figure 2a and 2b. The scalar field (colors) represents the vector length. One4Ankara International Aerospace Conference

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immediate observation is the complex wake structure downstream of the wheel including a large rotational
flow field. There is flow reversal close to the fuselage up to the landing gear strut, where the flow turns
downwards and is entrained into the wake flow behind the wheel. The region with very small velocity ismarked with purple color, which extends up to the right end of the FOV. A locally accelerated flow region isvisible emerging from the lower part of the wheel. A boundary layer profile U(y) along the red line in Figure 3ais also displayed in Figure 3c without further smoothing. The vertical distance is measured in mm, but once
again, the origin is placed at an arbitrary location during image calibration. In this figure, the flow reversal in
the wake is much more visible and the zero-velocity region is situated approximately 3.5 mm from the fuselage.
The locally accelerated flow from the wheel tip is visible just below the origin.
CONCLUSIONS & FUTURE WORK
Boundary layer and wake velocity measurements were performed with 95µm resolution around a small aircraftmodel using LDµPIV. The vector resolution is sufficient to provide boundary layer profiles, seperation bubbles
and large-scale flow dynamics, but not yet sufficient to resolve smaller turbulence scales. In order to increase
the spatial resolution, a better magnification is required. Similarly, in order to achieve a measurement volume
thinner than 380µm, PIV lasers with better beam characteristics must be used. For shorter particle response
times, the mean particle size should be decreased, and for particle detectability the laser pulse energy density
should be increased. Fluorescent imaging can be used to supress undesired wall reflections, and particletracking techniques can enhance resolution and accuracy near the wall. An ensamble average of 61 vector
maps is not sufficient to describe turbulence statistics; therefore a larger number of images should be acquired.
For increased temporal resolution, high-speed CMOS detectors can be used and high repetition rate Nd:YLF
lasers with sufficient pulse energy must be available. These conclusions indicate that LDµPIV experiments
require advanced hardware componets for spatially and temporally resolved turbulence measurements. Future
experiments will include improved spatial resolution with a larger image ensamble for better turbulence
statistics.
ACKNOWLEDGMENTS
The authors extend their thanks to Anadolu Jet Airlines for providing the aircraft model, which is the inspiration
for this experiment.

References
Adrian RJ, Westerweel J (2010) Particle image velocimetry, Cambridge University Press, Cambridge
Dieterle L, Weichert R (1996) Particle image velocimetry applied to microstructures in turbulent flows,Engineering Turbulence Modelling & Experiments Vol 3. pp. 391-400
Fiscaletti D, Elsinga GE, Westerweel J (2013) Long-range µPIV in the turbulent region of a jet, at high Reynolds
numbers, Proc. 10th Int. Symp. on Particle Image Velocimetry, Delft, The Nederlands
Kähler CJ, McKenna R, and Scholz U (2005) Wall-shear-stress measurements at moderate Re-numbers withthsingle pixel resolution using long distance µPIV – an accuracy assessment, 6 International Symposium onParticle Image Velocimetry, Pasadena, CA, Sept. 2005
Kähler CJ and Scholz U (2006) Application of long distance MicroPIV at Large Reynolds number, 13th Int. Symp
on Appl. Laser Techniques to Fluid Mechanics, Lisbon, Portugal, Jun 2006
Kähler CJ, Scholz U and Ortmanns J (2006) Wall-shear-stress and near-wall turbulence measurements up tosingle pixel resolution by means of long-distance micro-PIV, Experiments in Fluids, Vol. 41, pp:327-341
Lindken R, Di Silvestro F, Westerweel J and Nieuwstadt FTM (2002) Turbulence measurements with µ-PIV inlarge scale pipe flow, Proc. 11th Int. Symp. on Applications of Laser Techniques to Fluid Mechanics, Lisbon,
Portugal
Urushihara T, Meinhart CD, and Adrian RJ (1993) Investigation of the logarithmic layer in pipe flow using
particle image Velocimetry, Near Wall Turbulence Flows, Elsevier, Amsterdam, The Netherlands, pp:433-446
Westerweel J and Scarano F (2005) Universal outlier detection for PIV data, Exp. In Fluids 39, pp.1096-1100

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