Quantitative Flow Visualization for Large Scale
Wind Tunnels
R. Bömmels, M. Machacek, A. Landolt, and T. Rösgen
Institute of Fluid Dynamics, ETH Zurich, Switzerland
Introduction
Despite the increased use of numerical simulations in the development and
performance optimization of aerodynamic vehicles wind tunnel tests still are of
fundamental importance in the related engineering design process. In order to
keep the cost down and to increase the data return from the expensive measurement campaigns, there is an interest in improving and expanding the diagnostic tools available.
Recent developments in modern measurement technology, especially in the
areas of digital imaging and photonics, have led to the introduction of a number of computer-based, quantitative flow visualization tools. While these techniques have rapidly gained acceptance in laboratory research, their introduction into the domain of commercial testing has been slower. This may be not
only due to the cost involved but also for a number of technical reasons.
The reliability, precision and flexibility of the new methods still have to be
improved to convert them into turn-key tools. Furthermore, there are some
problems associated with the scale-up from bench-top hardware to systems operating in considerably larger and less controlled environments.
The paper will address some of those scaling issues. The specific requirements for large scale testing will be analyzed and a number of recent flow visualization and measurement techniques are described which should be well
suited for such applications.
Generic Issues in Large Scale Flow Visualization
Image based flow measurements and visualization techniques provide a number of desirable features such as non-intrusiveness, remote access, and the simultaneous processing of multiple measurement stations. This last feature enables also the computation of spatial gradients in unsteady flows, which is
useful in the detection and analysis of spatial flow structures. These properties
have led to the widespread introduction of such techniques in the laboratory,
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where for example particle image velocimetry (PIV) has developed almost into
standard method within the last ten years (Kompenhans 2000).
In large scale facilities such as wind tunnels, there are a number of generic
problems which have delayed a similar progress. Although these problems are
at times simple and of a purely technical nature, they still can affect the functionality and performance limits of various imaging techniques at a very fundamental level.
Most camera-based measurement methods require some form of active scene
illumination. Dependent on the capabilities of the cameras (high speed or slow
scan, direct integration or intensified, large or small dynamic range etc.) this
lighting may have to be pulsed or continuous, wide band (white) or spectrally
narrow-band and often of a considerable intensity. Incandescent lamps, lasers,
high-powered flash lamps or other, more specialized sources can usually be
found to meet these requirements. However, when arbitrary aerodynamic
models are to be inserted into the field of view, spurious surface reflections
may arise which cannot easily be controlled or eliminated. Since it is often the
flow close to the model surface which is of particular interest, the cameras’ pixels can become saturated and the information is lost where it counts most.
Finding a locally optimized arrangement of illumination and observation angles may temporarily solve the problem but it will arise immediately again if
the configuration is changed.
Another fairly common requirement in air flow diagnostics is the generation
of flow seeding. Usually micron-sized particles or droplets have to be chosen so
that the tracers can follow the flow with a sufficient fidelity. Since the backscatter signatures tend to be small, fairly high tracer densities have to be generated which may affect the overall visibility of the model and the optical access
in general, defeating in part the original purpose. More importantly, in many
imaging techniques the individual tracers have to be spatially resolved by the
recording device in order to extract the desired information. This puts constraints on the cameras’ resolution and optical magnification which may in
turn severely limit the size of the field of view. In addition, there are issues regarding the chosen tracers’ lifetime (too short, too long) and the environmental and health impact of the sometimes corrosive or poisonous substances
involved.
For imaging techniques that require either long time integration periods or
comparisons with reference images, another problem may arise from a possible
lack of configurational stability. Model movements on the supports, facility vibration and other uncontrolled effects may affect the precision with which the
position of the measurement points on the model surface can be determined.
In techniques such as pressure sensitive paint monitoring, where a pixel-bypixel referencing is necessary, even very small displacements can create significant changes in the ratio images.
Finally, there are simple operational and financial constraints that have to be
accommodated. While the actual data taking with an imaging setup may be
done in a few seconds, the calibration and adaptation of that setup can be an
hour-long activity. This may lead to the situation where a specific measurement technique is ruled out purely because of its operational complexity.
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Taking into account these technical constraints, there remain a few basic
choices which have to be made in the design and selection of a large scale,
quantitative flow visualization system.
In the laboratory, a certain preference has developed for imaging configurations which exploit the Eulerian view of the flow such as in PIV. This may be
due to technical reasons (e.g. the need for strong illumination, available only in
light sheets) but is also related to the interest in spatial flow structures and gradient properties. As a consequence, the flows under investigation are often
“tailored” to accommodate an essentially planar image acquisition philosophy.
Furthermore, the amount of raw data produced and the associated transfer and
storage requirements for a full 3-dimensional measurement grid would become
prohibitively large.
In flows around complex model surfaces, three-dimensionality is almost always present and a volumetric view of the measurement domain is necessary.
In order to keep the data complexity at a reasonable level, it may be more advantageous to rely on Lagrangian techniques, which track localized flow features
in time rather than providing spatially resolved snapshots.
A similar duality is present when one looks at the data processing philosophies. The established approach is based on a time-global approach where all
data acquired during a measurement run are stored and utilized in a postprocessing step to extract the desired information. The need for fast, near realtime data visualization and efficient data storage, however, does favor incremental strategies where the incoming data are processed “on the fly” with only
a limited knowledge of the temporal flow evolution. Especially for Lagrangian
techniques (e.g. particle tracking methods), the choice of such an incremental
approach does have a significant impact on the overall processing scheme.
Finally, a balance has to be found between the requirements for fast data
analysis / presentation and the achievable measurement accuracy. For visualization-oriented schemes the temporal coherence of the data may be more important than the ultimate precision of each individual data point reading, creating a requirement for efficient error rejection, but not necessarily for error
correction. In other situations, the priorities are reversed in that accuracy is
more important than speed and an increased temporal effort (including averaging multiple measurements or recursive processing) is considered acceptable.
Looking at these considerations, one may state that large scale diagnostic
applications create their own set of requirements which may be different from
those applicable to laboratory-style research. The measurement techniques
which are to be applied have to be adapted especially regarding their operational characteristics. At times, even the development of new methods designed specifically for large scale environments may become necessary.
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Candidate Technologies and Techniques
At the Institute of Fluid Dynamics of ETH, a medium sized wind tunnel (2m
x 3m test section, see Fig. 1) is being operated for educational and research
purposes, which was built in the 1930’s by J. Ackeret.
Fig. 1: Medium scale wind tunnel at IFD / ETHZ
This facility is also used as a test bed for a number of advanced quantitative
imaging techniques which are being developed with true large scale applications in mind.
Pressure sensitive paints are being studied in cooperation with RUAG Aerospace (CH). Being a technique of obvious potential for wind tunnel applications, the research concentrates specifically on ways to eliminate the inherent
temperature sensitivity of the fluorescent paints used and the wind-on / windoff calibration procedure (see also Engler 2000).
A pulsed infrared thermography system has been installed to provide a tool
for the rapid visualization of laminar-turbulent transition and separation lines
on model surfaces. It utilizes a high power stroboscope (7500 W, approx. 7 J /
flash) in conjunction with a midwave (3-5mm) infrared camera (20 mK
NETD) to detect the subtle changes in surface heat transfer associated with
those flow phenomena. The technical challenge lies in the development of imaging strategies which are independent of the model’s surface structure and
composition which can strongly affect the infrared signature (Le Sant 2002).
In the area of flow velocimetry, a Doppler global velocimeter is being developed based on a custom narrow band pulsed laser with two independent oscillators. This permits the simultaneous use as a PIV system for comparison
and the measurement of 3-dimensional velocity vectors with a single camera /
single view point arrangement.
At the same time a particle tracking velocimeter is being used to provide a
Lagrangian view of the flows under investigation. A pair of high speed cameras
tracks the motion of helium-filled soap bubbles which are used as low-inertia
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tracers. The resulting three-dimensional path lines can be used to determine
velocities and topological information.
Finally, activities are under way to develop a simple real-time visualization
tool to enhance the information obtained from operating standard smoke
probes. Here the emphasis is strictly on the fast processing of the visual information, not on the extraction of detailed quantitative information.
In the following, three of the above mentioned examples will be described
in more detail as they highlight the points made about large scale diagnostics.
Doppler Global Velocimetry
Doppler global velocimetry is a planar imaging technique designed to measure
three-dimensional velocity components. Instead of analyzing the displacement
of individual tracers (as in particle tracking velocimetry – PTV) or of small
tracer clusters (as in particle image velocimetry – PIV) one looks at the optical
Doppler shift of the moving tracers (Mosedale 2000, Samimy 2000). The
great advantage in doing this is that the individual particles do not have to be
resolved anymore by the recording camera. Any feature that moves with the
flow and crosses the illuminating light sheet will create the desired signature.
The camera magnification can be set so as to image the whole test section and
one of the major drawbacks of PTV / PIV - the limited recording area - is
avoided.
Fig. 2: Components of a DGV setup – seeded long pulse laser (left),
dual camera / iodine filter cell (right)
The price one has to pay for this improvement is twofold. First, the lasers
required to generate the light sheet have to be very stable and must emit a very
narrow-band radiation with an optical line width of, say, below 20 MHz. Such
lasers are available as CW systems, but the illumination in large facilities demands higher intensities only available from pulsed systems (e.g. Nd:YAG lasers). Here a so-called injection seeder has to be used. A low power, highly stable ring laser injects seed radiation into the cavity of a Q-switched oscillator
which leads to a longitudinal mode selection and stabilization. Since the opti-
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cal bandwidth of the emitted radiation is inversely proportional to the pulse
length / roundtrip time, the laser cavities can become fairly large (several meters, see Fig 2) to support the desired bandwidth reduction.
The frequency shifts caused be the moving tracer clouds are on the order of
the ratio (flow velocity / speed of light), a very small number indeed. The
Doppler frequencies in the MHz regime cannot be detected directly since one
is using an integrating device (CCD camera) - imaging heterodyne detectors
are not (yet) available for those frequencies. A molecular filter cell filled with
iodine is used to convert frequency changes into intensity variations. Tuning
the laser frequency onto the edge of one of the absorption lines of iodine, any
Doppler shift in the imaged scenery can then be observed as a change in
transmission (Fig. 3). A camera pair monitors the filtered and unfiltered images and the ratio of both images encodes the velocity observed in the Doppler
sensitive direction (Fig. 2). Since each pixel can be processed separately and
independently, a very high spatial resolution can be achieved.
light sheet with n0
molecular filtering in an iodine cell
Dn
T
1
T0
V(z1)
n = n0 + Dn(z1 = 0)
DT
T(n)
V(z2)
n
n0
0
V(z3) = 0
x
V(z4)
V(z5)
n = n0 – Dn(z5 = zmax)
z
result: data image
Fig. 3: Operating principle of a molecular filter cell in DGV
The system operated at IFD is more advanced in that it operates two seeded
lasers in parallel to provide a double pulse capability as well. This facilitates the
use of the lasers in a PIV mode for comparison. As a matter of fact, if the scaleup / resolution issue is not critical, a simultaneous DGV / PIV measurement is
possible. In such an arrangement, the out-of-plane (DGV) and in-plane (PIV)
velocity components in the light sheet can be measured with the same camera
system, creating a true 3-component velocimeter. (Normally, different velocity
components are measured with separate camera systems in an all-DGV configuration).
Besides the advantages cited so far, DGV as a large scale imaging technique
is also affected by some inherent limitations. First of all, the intensity ratioing
approach for the computation of velocities makes DGV into an essentially
analogue technique. The intensity reading of a single camera pixel is affected
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by a number of factors (sensor non-uniformities, background noise, gain nonlinearities, etc.) which have to be carefully compensated. In addition, pulsed
laser illumination leads to the appearance of speckles in the images which have
to be eliminated. Spatial filtering can achieve this, but at the cost of a reduced
spatial resolution.
Figure 4 shows an image sequence acquired with a special test target (rotating disc). The processed ratio image shows a component of the overall disc
rotation velocity vector. Note also that the surface features can be used in connection with a second image pair to compute the in plane velocity components
with PIV.
Fig. 4: DGV images or a rotating disc target: filtered (left), unfiltered (center), processed (right)
3D Particle Path Line Tracking
The path line tracking system which is described next may serve as an example
for a Lagrangian imaging technique. It is based on the tracking of helium filled
soap bubbles as they flow through the field of view of a stereo camera system
(Fig. 5).
In contrast to most established tracking schemes, the bubbles are not imaged based on a short-time flash exposure but rather using continuous lighting.
Their signature on the integrating CCD sensors (120 frames/s) is thus a short,
continuous path line segment. The correspondence problem is solved by
looking at the bubble signatures as they cross the boundaries between consecutive image frames. This creates a unique coordinate in space-time which
can easily be linked to the complementary second camera view using the
epipolar matching condition known from photogrammetry theory. Velocity
information is derived from the measurement of the bubbles’ displacement
within and across the individual image frames (Machacek 2002).
While the approach is quite robust, the technique is affected by some of the
problems listed above. Reflections from walls or models can become quite
prominent and the best tracking results tend to be generated in the wake regions away from any solid surface. The situation may be improved by increasing the number of independent views of the scene (i.e. cameras) but other alternatives appear to be more promising in the long term. Preliminary studies
have been performed on the utility of fluorescent and / or smoke filled bubbles
which could increase the image contrast in the surface reflection regions.
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However, issues regarding the environmental safety and pollution have to be
considered and have not yet been resolved.
Fig. 5: Schematic of the stereo path line tracking system
Another aspect of generic importance is the photogrammetric calibration of
the camera systems involved. This can be divided into the tasks of finding the
cameras’ inner parameters (e.g. lens distortion, focal lengths) and the outer parameters such as camera orientation in space and relative positioning (Ruyten
2002). Especially the measurement of the spatial orientation requires a calibration or reference structure of known geometry which has to be inserted into
the field of view. When large volumes have to be imaged these calibration
structures also tend to become very large and difficult to handle / position.
Point source LED’s
Fig. 6: Outer parameter calibration using a synthesized calibration target:
calibration rod (left); cloud of accumulated reference points (right)
In the present arrangement, the outer parameters are determined using a
synthetic target created by moving a calibration rod (length L=0.5m) through
the measurement volume (see Fig. 6). Two point-source LEDs on the rod cre-
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ate a “cloud” of reference points with a known relative distance from which
the unknown parameters can be deduced (Borghese 2000).
A typical result of a reconstructed path line field is shown in Fig. 7. The
vortex flow behind a delta wing is clearly visible in this 3D rendering of the reconstructed bubble tracks. The color coding of the individual tracks is an indication of the relative velocity of the helium bubbles forming the path lines.
Fig. 7: Flow behind a delta wing visualized using 3D reconstructed path lines
Direct Digital Visualization
As a last example, some elements and results of a direct visualization technique
will be described. Here the idea is to reduce the amount of data processing
and, more importantly, data interpretation, to a minimum and to produce
simply an enhanced view of the measurement scene which is interpreted by the
observer rather than the computer.
As a last example, some elements and results of a direct visualization technique will be described. Here the idea is to reduce the amount of data processing and, more importantly, data interpretation, to a minimum and to produce simply an enhanced view of the measurement scene which is interpreted
by the observer rather than the computer.
This type of context-insensitive processing can usually be done in real-time,
that is faster than the normal camera acquisition frame rate (25 frames/s).
Since the data is updated rapidly, no sophisticated noise reduction and / or error handling is required: the human observer is usually very tolerant towards
transient visualization faults.
The main advantage of such a tool, its true interactiveness, is difficult to depict in static images. To give an indication, Fig. 8 shows a typical scene from a
wind tunnel test where a smoke probe is used to visualize the flow around a
car. The second image shows a streak line picture which is assembled out of
the live camera frames in real time. As the smoke probe is traversed, the computer builds up a cumulative image of all streak lines as they are extracted from
the smoke trails.
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The challenges in this type of processing are once again primarily the lighting and choice of seeding. In addition, fast processing hardware is required but
the rapid increase in computing power means that dedicated hardware such as
pipelined image processors can successively be replaced by general purpose
CPUs.
Fig. 8: Real-time processing of smoke trails into a cumulative streak line picture
Summary
Quantitative flow visualization based on the processing of remotely acquired
image data can significantly enhance the understanding and analysis of engineering flows. Modern techniques such as Doppler global velocimetry, pressure sensitive (fluorescent) paints or infrared thermography make use of advanced lasers and image detectors to provide the optical raw data that are
subsequently transformed into the desired flow information (velocity, temperature, pressure, etc.). While the different principles of operation have been
successfully verified, there are still a number of generic technical issues which
have to be resolved before one can expect a routine use in commercial facilities.
Examples were given for a DGV system, a 3D particle tracking velocimeter
and a simple yet powerful real-time visualization tool. All systems are designed
for operation irrespective of the large scales involved (several meters field of
view). The control of the illumination and the seeding density remain as a
problem which is directly related to the geometrical size and complexity of the
measurement environment. The scaling issues will remain one of specific
problems in large scale flow diagnostics which are not found on the laboratory
scale and must thus be addressed separately.
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