High Resolution Planar Doppler Velocimetry Using a cw Laser

Transcription

1 47th AIAA Aerospace Sciences Meeting Including The New Horizons Forum and Aerospace Exposition 5-8 January 2009, Orlando, Florida AIAA High Resolution Planar Doppler Velocimetry Using a cw Laser Thomas P. Jenkins * MetroLaser, Inc., Irvine, California, Robert L. McKenzie Sunnyvale, California, Planar Doppler Velocimetry (PDV) has typically been applied using pulsed lasers, for which the limiting factor on velocity resolution is speckle noise. The present work demonstrates improvements in velocity resolution obtained by using a continuous wave (cw) laser instead, enabling the speckle noise to be averaged out. Measurements are made in a seeded laminar jet using PDV with a cw laser, and are compared to measurements of the same jet using the more established technique of Particle Imaging Velocimetry (PIV). The PDV results demonstrate a velocity resolution of 0.3 m/s, which was near the photonstatistical shot noise limit for this experiment. The velocity field obtained shows good agreement with the PIV results. Nomenclature â = unit vector in the direction of observation ν = Doppler shift in frequency lˆ = unit vector in the direction of laser sheet propagation λ = laser wavelength S r = direction vector of measured component, equal to aˆ lˆ T = cell transmission V r = total velocity vector ν = frequency of scattered light Ω = angle between V r and S r I. Introduction experimental investigations of low speed, large scale flows present special challenges, particularly if one wants Eto obtain a planar velocity vector field. For example, the flow field around a parachute canopy generally involves flow structure dimensions measured in meters and velocities on the order of a few meters per second. Existing planar velocimetry techniques, such as Particle Imaging Velocimetry (PIV), do not have the spatial range to cover the largest flow scales while still resolving individual particles for determining velocities. Point techniques, such as conventional laser Doppler velocimetry, are impractical for characterizing large flow fields owing to the large number of measurement points required. On the other hand, Planar Doppler Velocimetry (PDV) has been demonstrated in recent years to have the ability to obtain vector flow fields larger than m x m in practical low speed applications such as full-scale helicopter rotors. PDV measures velocity from the Doppler shift of light scattered from particles seeded into the flow field, and does not require individual particles to be resolved. However, previous applications of PDV to practical flows have relied mainly on pulsed lasers for which the measurement resolution is typically to 2 m/s, and thus not suitable for very low speed applications such as flows around parachute canopies. * Senior Scientist, MetroLaser, AIAA Member. Consultant, AIAA Associate Fellow. Copyright 2009 by the, Inc. All rights reserved.

2 In the early applications of PDV, then called Doppler Global Velocimetry, continuous wave (cw) lasers with modest power were used to demonstrate the technique. 2,3,4 Velocity resolution is then limited mainly by the photonstatistical (shot) noise of the scattered light. As the applications of interest moved to large-scale facilities and the need arose to resolve temporal variations in the flow, scattering signals from the cw lasers used were inadequate, leading to PDV systems using frequency-narrowed, high energy, pulsed lasers. 4 However, the velocity resolution from the short pulses is then limited mainly by speckle noise, which is typically much greater than shot noise. Recently, the availability of high power cw laser sources with narrow line widths has reopened the possibility of using these sources for PDV applications. Planar Doppler Velocimetry with a cw laser eliminates the speckle problem by enabling temporal integration, which averages the fluctuating speckle, thereby enabling performance near the shot noise limit. In our earlier work 5, we demonstrated a velocity resolution of about 0.2 m/s on a rotating wheel. The objective of the work described here is to demonstrate that high-resolution velocity measurements with near-shot noise uncertainties can be achieved in a simple laminar jet flow using PDV based on a cw laser. II. Experimental Setup The experimental setup for validating the PDV technique using a cw laser in a laminar jet is shown in Fig.. It consisted of a seeded jet imaged by both a PDV system and a PIV system. The jet was produced from a source of compressed air flowing through a straight 60-mm-long stainless steel tube with an inside diameter of 9.8 mm and a wall thickness of.4 mm. The air flow rate for all experiments presented here was l/min. The air source was found to have negligible particulate contamination, as verified by observing scattered laser light from the unseeded jet. Smoke particles were seeded into the flow by passing the air through a sealed chamber containing burning incense sticks. Seeding levels could be controlled by varying the number of incense sticks used. To reduce laser scattering by the nozzle surfaces, a section of the tube near the exit was painted black. A. PIV System A Master digital PIV system from LaVision, GmbH, was used that incorporated a double-pulsed laser and a dual-framestorage camera. The laser was a dual-cavity Nd:YAG design, frequency doubled to produce a 532-nm output with 25 mj/pulse at a 0-Hz pulse rate. The camera sensor was a 600 x 200 CCD array that was synchronized to obtain one frame for each laser pulse. Timing was controlled via a computer using the DaVis 7 software package from LaVision, which sent pulses to fire the flash lamps and Q-switches for each of the two laser cavities, and a trigger pulse to the camera. The delay between the two laser pulses was set to 00 µs for all experiments presented here. A laser sheet was formed using a concave cylindrical lens with a focal length of -9 mm, and a convex spherical lens Seeded Jet Mirror PIV Camera PIV Laser (532-nm Double Pulse) PIV Sheet Optics (Removable) PDV Sheet Optics PDV Laser (064- nm CW) PDV Camera with a focal length of 250 mm. The thickness of the laser sheet was calculated from the beam diameter, focal length, and M 2 parameter of the laser to be 540 µm. A mirror directed the laser sheet along the axis of the nozzle for PIV measurements. During PDV measurements, this mirror and the 250-mm spherical lens, shown in gray in Fig., were removed. Scattered light from a region near the nozzle exit was imaged onto the camera by a 85-mm f.8 Nikkor lens. The front of the lens was located 220 mm from the measurement region, with the viewing axis at a right angle to the jet axis. B. PDV system The PDV setup consisted of a 500 mw cw laser of wavelength 064 nm that was frequency-doubled with a PPLN second harmonic generator (labeled SHG in Fig. ) to produce a 532-nm beam. The two wavelengths were separated with a dielectric mirror, which reflected the 532-nm beam and transmitted the 064-nm beam into a beam Mirror Cube BS Iodine Cell Mirror SHG Beam Dump Fig.. Experimental setup for validating the cw-pdv technique in a jet flow by comparing it with PIV. 2

3 dump. The 532-nm beam was sent through a 950-nm low pass filter to remove any residual 064-nm light, and was spatially filtered to produce a smooth beam profile. The spatial filter consisted of a microscope objective lens followed by a 0-µm-diameter pinhole aperture, and was necessary to remove intensity variations that resulted from superposition of coherent scattering off the parallel surfaces of the PPLN crystal. To form the laser into a sheet, a convex spherical lens with a focal length of 00 mm was placed one focal length from the spatial filter, followed by a convex cylindrical lens with a focal length of 650 mm. The spherical lens collimated the light after the spatial filter, and the cylindrical lens focused it to form a sheet with an observed thickness of about -mm at the measurement region. Although the power of the 064-nm cw laser was 500 mw, the power of the 532-nm beam was just over 2 mw after second harmonic conversion. Passing the beam through the spatial filter, although necessary, significantly reduced the laser sheet intensity to a measured laser power of 0.38 mw. As Fig. depicts, scattered light from a region near the jet exit was collected by a mirror, split into filtered and unfiltered paths, and imaged by a Pixis 400B CCD camera from Princeton Instruments. The CCD had a 340 x 400 array of square pixels 26 µm on a side. For all images reported here, the pixels were grouped 2 x 2 to form an array of 670 x 200 super-pixels. This enabled each super-pixel to have an effective well depth of about 0 6 photons. A Nikon 05-mm f2.5 lens was used, with the front of the lens located an optical distance of 0 cm from the measurement region. The vapor cell was a cylinder, 38 mm in diameter, and 50 mm long. It contained 00% iodine at a vapor pressure corresponding to 45º C. The cell was wrapped in a heating blanket, and was maintained at 90º C during all the experiments presented here to ensure that the iodine remained in vapor form. III. Results A. PDV Image Acquisition. Each PDV measurement consisted of a series of three camera frames including ) a Doppler frame, 2) a flatfield frame, and 3) a background frame. The three frames were acquired in rapid succession to minimize changes that might occur in the system between frames. Each frame contained two side-by-side images of the jet: one filtered by the iodine cell, and one unfiltered. For the Doppler frame, the laser frequency was tuned to a point on the edge of an iodine absorption line that has about 50% transmission. For the present study, line 09 in the naming convention of Gerstenkorn and Luc 6 was used, which has a center wavelength of nm. An example of a Doppler frame is shown in Fig. 2. The filtered image appears on the right and the unfiltered on the left. The flow is from left to right, and the signal intensity is indicated in counts by a false color scale on top. One count represents one unit of intensity out of a maximum of about 65,000 counts for the 6-bit dynamic range of the camera. To fill the well depth of each super-pixel to near its limit and thereby minimize the effects of shot noise, the camera exposure time used for this frame was 30 seconds. This extended exposure time also temporally averaged the speckle noise, thereby eliminating its effect. Note, however, that this long exposure time is a consequence of the Unfiltered Filtered Reference Tab Reference Tab Fig. 2. Raw PDV Doppler frame showing the filtered and unfiltered images of a seeded jet. low laser power that was available for these demonstration experiments, and is not a limitation of the technique. The use of higher-power cw lasers for larger-scale experiments will allow the exposure time to be reduced to durations appropriate for resolving temporal variations in the flow field. 3

4 A reference tab used for obtaining the zero-doppler transmission can also be seen in the lower right parts of the images in Fig. 2. The tab was a piece of black paper in view of the camera that scattered light from a portion of the laser sheet outside the jet flow. The flatfield frame was taken at the same nominal seeding conditions as the Doppler image, but with the laser frequency tuned off of the iodine line to a flat portion of the absorption spectrum about GHz beyond the 50 % transmission point. Because of the flatness of the spectrum here, variations in velocity do not cause variations in the transmission. The background frame was taken with the laser blocked and all other conditions the same as for the Doppler and flatfield frames. B. PDV Image Processing The first step in image processing is the subtraction of the background frame from both the Doppler frame and the flatfield frame. This removes the effects of any stray light that may have been present, and also accounts for detector offsets. The Doppler frame records the Doppler-shifted transmission data of the flowfield. Since the laser is tuned to the 50 % transmission point, the filtered image appears about half as bright as the unfiltered image, as can be seen in Fig. 2. Variations in the intensity of the filtered image occur due to Doppler shifts. However, Doppler-induced variations cannot be seen in Fig. 2 because they are overwhelmed by larger intensity variations due to seeding nonuniformities, laser sheet variations, non-uniformities in the transmission efficiency of the collection optics, and pixel-to-pixel variations in the response of the camera. Dividing the filtered image by the unfiltered image removes the effects of spatial and temporal variations in both seeding density and laser intensity. Fig. 3(a) shows the result of dividing the filtered image of Fig. 2 by the unfiltered image. A significant amount of variation can still be seen in the jet, which is due to the effects of spatial variations in collection optics transmission and detector response. Normalizing by the flatfield image removes most of these remaining variations, as shown in Fig. 3(b). To enable a comparison, the color scales at the top of Fig. 3(a) and Fig. 3(b) cover the same range, which is from 0.2 to 0.8. It can be seen that the flatfield-normalized image is significantly more uniform than before the normalization. Thus, Fig. 3(b) represents an image of Doppler-influenced transmission through the iodine cell. What remains is to subtract the intensity value that corresponds to zero Doppler shift. The difference between the relative Normalized by Unfiltered Normalized by Unfiltered and Flatfield (a) Fig. 3. Doppler field image of a jet normalized by (a) unfiltered image and (b) unfiltered and flatfield images frequency measured by the transmission from each point in the Doppler-shifted field and the spatially-averaged relative frequency measured by transmission from the reference tab enables the calculation of the absolute Doppler frequency shift everywhere in the flow image. Ideally, the reference tab image would have a uniform transmission ratio in the flatfield-normalized image of Fig. 3(b). However, light and dark fringes are apparent that are etalon effects caused by interference between parallel optical surfaces in the beam path. Although the laser power varies negligibly as the frequency is tuned from the 50 % to the 00 % transmission frequency, the change in frequency is enough to cause these fringes to appear. The average of the transmission value over the area of the reference tab was taken as the zero-doppler transmission value, and was found to be (b)

5 A transmission difference was computed for each pixel in the jet image by subtracting the averaged zero- Doppler value from each pixel in the transmission image, Fig. 3(b). From our earlier work 5, the slope of the iodine cell response function at a transmission of was known to be dt/dν = MHz -. Assuming that the slope of the iodine transmission, dt/dν, is approximately the same at the Doppler-shifted and zero-doppler frequencies and then dividing the transmission shift field by dt/dν, the Doppler transmission shift field is converted into a frequency shift field. The present one-camera system allows only a single component of the total velocity vector, V r, to be measured. The measured Doppler shift, ν, is related to V r by ν = S r V r, () λ where S r is defined as r S aˆ lˆ, (2) where â is the direction of observation and lˆ is the propagation direction of the incident laser sheet. To measure three velocity components, two additional cameras and iodine cells can be added to the system. Each camera would image the same laser sheet, producing an image of one velocity component, determined by Eq. (). Data from the three images would then be combined to produce a planar field of three-dimensional velocity vectors as described in Ref.. The vector geometry for the present experimental setup is shown in Fig. 4. Since the velocity was known to be directed along the centerline of the jet in this case, the axial velocity can be related to the measured Doppler shift via the angle Ω between S r and V r, which was 48.4º. Thus, for the two-dimensional geometry shown in Fig. 4, Eq. () can be written as ν = S r V r cosω. (3) λ Using Eq. (3), the magnitude of the velocity is related to the Doppler shift by V/ ν = λ/(s cos Ω) so that the magnitude S = 2 cos(48.4º) =.327 and the conversion constant is V/ ν = ( m)(06 MHz - s - ) /.327 cos(48.4º) = m/s per MHz. Velocities were computed from the Doppler transmission shift image of Fig. 3(b) by subtracting the zero- Doppler transmission of 0.378, dividing by the response function slope, MHz -, and multiplying by the conversion constant, m/s per MHz. The results are shown in Fig. 5(a). The general structure of the velocity lˆ V r field is correct for a laminar jet, with striations of nearly Ω = 48.4º uniform velocities along the axial direction and a maximum on the centerline. The magnitudes indicated on â r S = aˆ lˆ the color bar on top range from 0 to 3 m/s. Each pixel in the image represents one 2-pixel x 2-pixel super-pixel of lˆ 52 µm x 52 µm, which images an area in the field of view Fig. 4. Vector diagram of experimental system. that is 308 µm x 308 µm. 5

6 Axial Velocity (m/s) Basic PDV Processing Axial Velocity (m/s) Extended PDV Processing (a) Basic Processing (b) Extended Processing Fig. 5. Velocity images of a laminar jet obtained by Planar Doppler Velocimetry using two different processing techniques: a) basic and b) extended. In the processing technique described above, the filtered and unfiltered images from a given camera frame were aligned with each other before dividing by manually selecting a common point in the two images, and referencing the corresponding pixels to that point. We will refer to this method as Basic PDV Processing. We have shown in previous work that errors can arise due to the misalignment of pixels before dividing. 7 Pixel misalignment errors can be significantly reduced using pixel binning before dividing. We have developed an Extended PDV Processing technique to accomplish this, which is described in detail in previous work 7,8. Briefly, the Extended PDV Processing technique involves a rigorous mapping of the filtered to the unfiltered images based on a calibration target card, and a 3-pixel x 3-pixel interrogation window that minimizes the pixel ratio errors due to the lack of complete overlap between pixel pairs that make up the normalized image. An image obtained using the Extended PDV Processing technique is presented in Fig. 5(b). A Comparison of Fig. 5(a) and Fig. 5(b) reveals similar velocity magnitudes and similar general flow structure, but a smoother and more accurate distribution of velocities is apparent with the Extended Processing technique. An anomalous stripe is observed along the bottom edge of the jet in Fig. 5(b) that is separated from the rest of the jet. These errors are believed to be caused by the flatfield normalization process, resulting perhaps because the mapping of filtered and unfiltered images was not perfect. Near the edge of the jet the signal intensity goes from a high value to near zero abruptly. Thus, even a slight misalignment of the two images in this region during the normalization process can result in the division of a large number by a small one, causing the computed values to be sensitive to such misalignments. In the present experiments, the flatfield was obtained using seeding in the jet only. An improved method would be to seed the entire area within the field of view of the camera, which should reduce or eliminate such anomalies. C. Validation with PIV Characteristics of the jet were obtained using PIV measurements as an established baseline measurement technique. Visual observations of the jet flow revealed that it is laminar near the nozzle exit, transitioning to turbulent flow roughly 00 mm downstream from the exit. Only the laminar portion of the flow is of interest in the present study. The fields of view imaged by both the PIV and PDV systems were selected to be within the laminar region. Velocity vectors were computed from a number of image pairs using the DaVis 7 software to perform spatial correlations between the two images of a given pair. A dual-pass approach was used, first scanning the image using a 64 x 64 pixel interrogation area with 50 % overlap, followed by a pass using a 32 x 32 area, also with 50 % overlap. The average of a series of ten velocity vector fields computed from consecutive pulses at a 0-Hz rate is shown in Fig. 6. A velocity vector was computed for every 32-pixel x 32-pixel interrogation element. However, for clarity of presentation, only every second vector in the vertical direction, and every eighth vector in the horizontal direction, is shown. A reference vector of length 2 m/s is shown in the upper left of Fig. 6. The velocity profiles seen here resemble the expected parabolic shape for a laminar jet. The maximum values occur at the centerline, and have a magnitude of about 2.3 m/s. 6

7 The imaged area for these PIV measurements extended from 7 mm to 39 mm downstream from the nozzle, while for the PDV measurements it extended from 0 to 65 mm from the nozzle. A greater magnification was possible in the PIV case because the camera could be placed closer to the measurement zone. The 38-mm-diameter iodine cell and split-image optics of the PDV system limited the angular field of view to about 0.07 radians in the present setup. This field of view can be increased in future setups by using a larger diameter iodine cell. Centerline velocities are plotted for both the PIV and PDV results in Fig. 7. The noise level is significantly greater in the PDV case, but the magnitudes of the PDV velocities are in good agreement with the PIV measurements. For the PIV case, the velocity varies from 2.34 m/s near the upstream end of the field of view to 2.29 near the downstream end. The average of all PIV velocities along this length section of the centerline is 2.32 m/s, while the average of all PDV velocities is 2.7 m/s. The rms-variance of the PIV velocities is 0.08 m/s, while for the PDV velocities it is 0.29 m/s. A rough approximation of the accuracy of the PIV velocities can be obtained by assuming that the displacement accuracy is one tenth of a pixel, or about.4 µm in the measurement zone. Since the delay between pulses was 00 µs, the corresponding uncertainty in velocity is.4/00 = 0.04 m/s, which is in good agreement with the measured variance of 0.08 m/s. 4 Distance From Centerline, mm m/s Distance From, mm Fig. 6. Average velocity vector field near the exit of a jet, obtained using PIV by averaging the fields from 0 consecutive image pairs Centerline Velocity, m/s 3 2 PIV PDV Velocity, m/s 3 2 PIV PDV Distance from, mm Fig. 7. Centerline velocities for PIV and PDV measurements in a laminar jet Transverse Coordinate, mm Fig. 8. Velocity profiles from PIV and PDV measurements in a laminar jet taken 7 mm downstream of the nozzle. Fig. 8 shows a comparison of velocity profiles taken at a cross-section 7 mm downstream of the nozzle. The PIV result shows a smooth profile resembling the parabolic distribution expected for this Poiseuille flow. The PDV result captures the general trend, but is significantly noisier. The anomalous spike seen in the PDV result near the right edge of the jet is believed to be an artifact due to non-uniformity of the seeding during the flatfield image acquisitions, as discussed in Section III B. IV. Discussion A. Velocity Uncertainty As the PIV measurements of Fig. 6 and Fig. 7 show, the change in centerline velocity of this jet is small in the region of the flow we are interrogating. Therefore, we can use the average of the measured centerline velocities as a 7

8 baseline to evaluate the noise in the PDV measurement. The rms-variance of the PDV velocities plotted along the centerline in Fig. 7 is 0.3 m/s. However, this result includes spatial averaging due to the 3 x 3 pixel window in the processing technique. Thus, the spatial resolution will be about three times worse, or about 900 µm, versus 300 µm for the basic processing. If we compute the variance of centerline velocities obtained from the basic processing for this same data, we obtain 0.38 m/s which probably also includes some fluctuations owing to pixel misalignment noise. We can compare the measured variances to the value we would expect from a shot-noise-limited measurement. The photon shot noise can be estimated from SNR = n 0.5, where n is the number of photons collected. The camera used in the current setup had a well depth of about 0 6 photons. Assuming a signal level of 25%, which approximates the data collected here, this corresponds to 250,000 photons, or SNR = (250,000) 0.5 = 500. Thus, the corresponding uncertainty in the transmission will be T = /500 = The uncertainty in the measured Doppler shift can be computed from ν = T/(dT/dν), where dt/dν is the response function slope, which is about 3.5 x 0-3 MHz -, leading to ν = (0.002)/(3.5 x 0-3 MHz - ) = 0.57 MHz. The uncertainty in velocity is computed from the conversion factor for the present experiment of m/s per MHz, leading to V = (0.57 MHz)(0.603 m/s per MHz) = 0.34 m/s. This is in agreement with the experimental value of 0.38 m/s for the Basic PDV Processing case. On the other hand, if we consider the shot noise in a 3x3 pixel window, it will be 3-times less, leading to a measurement uncertainty of 0. m/s, which is very close to our experimental result of 0.3 m/s rms using Extended PDV processing. Thus, we can conclude that the limiting noise source in these measurements is essentially no worse than expected from photon-statistical shot noise. It should be noted that maximum signal levels from scattering in the jet were approximately 7,000 counts, or about 260,000 photons. Increasing the exposure time to improve the signal level caused saturation of the reference tab in this setup. Thus, in future setups, increasing the laser power or exposure time could increase the signal levels to the maximum of 0 6 photons, which would improve the velocity uncertainty by a factor of two, resulting in a velocity uncertainty of 0.06 m/s using the Extended PDV Processing technique. B. PDV vs. PIV In the results presented here, the uncertainties using the PDV technique are seen to be about an order of magnitude greater than those using PIV. There is no question that PIV offers greater velocity resolution for low speed flows but it is typically limited to areas of about 50 cm x 50 cm, because individual particles, or groups of particles, must be resolved in the images. A significant advantage of the PDV technique is that it can be scaled to larger areas because the requirement of resolving individual particles is eliminated. We envision applying the PDV principles demonstrated here in the near future to the study of flows around parachute canopies, where the flow field region of interest is approximately 2 m x 2 m, and the desired velocity uncertainty is less than 0.5 m/s. V. Conclusions The experimental investigations conducted here demonstrate that shot-noise-limited performance can be obtained with PDV using a cw laser in a flow field application. A simple laminar velocity field was produced in the near field of a low Reynolds number jet, and its velocity field was characterized using Particle Imaging Velocimetry (PIV) measurements of high accuracy. This flow field permitted an evaluation of the PDV technique at velocities representative of low speed aerodynamic research, with a maximum velocity on the jet centerline of 2.3 m/s. Planar distributions of a single velocity component were obtained with PDV that recovered the structure of the flow correctly, with a noise level of 0.3 m/s. Some anomalies were observed near the edges of the jet that resulted from inaccurate normalization by the flatfield image. These anomalies can most likely be avoided by seeding a broader area within the camera field of view when taking flatfield images. Unlike PIV, PDV is not limited to small flow fields. We believe that the results presented here can be scaled to a larger flow field, perhaps 2 m x 2 m, with velocity uncertainties similar to those demonstrated here. Acknowledgments This work was sponsored by the U. S. Army Soldier Systems Center, Natick, Massachusetts. 8

9 References McKenzie, R. L., Reinath, M. S., and Jenkins, T. P., Advances in Planar Doppler Velocimetry for Large-Scale Wind Tunnels, AIAA , 24th AIAA Aerodynamic Measurement Technology and Ground Testing Conference, 28 June - July 2004, Portland, Oregon. 2 Meyers, J.F. Doppler Global Velocimetry, The Next Generation, AIAA , AIAA 7th Aerospace Ground Testing Conference, July 6-8, 992, Nashville, TN 3 Meyers, J.F. Development of Doppler Global Velocimetry As A Diagnostic Tool, Measurement Science Technology, Vol. 6, 995, pp Reinath, M.S. Doppler Global Velocimeter Development for Large Wind Tunnels, Measurement Science Technology, Vol. 2, 200, pp Jenkins, T. P., Turner, K. I., and McKenzie, R. L., Planar Doppler Velocimetry System with Vibration Immunity, 22nd International Congress on Instrumentation in Aerospace Simulation Facilities, Pacific Grove, CA, June 0-4 (2007). 6 Gerstenkorn, S. and Luc, P., Identification des Transitions du Systeme (B X) de la Molecule d Iode et Facteurs de Franck Condon cm-, Laboratorie Aime-Cotton, Centre National de la Recherche Scientifique, II-9405, Orsay, France (986). 7 McKenzie, R. L., Planar Doppler Velocimetry Performance in Low Speed s, AIAA , 35 th AIAA Aerospace Sciences Meeting, Reno, NV (997). 8 McKenzie, R. L. and Jenkins, T. P., Universal Planar Doppler Velocimetry System for Wind Tunnel Tests, Final Report Part 2: Data Processing, Contract No. F C-0008, submitted to the Air Force, AECD, November 28 (2006). 9