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Acoustic images of a silicon carbide ceramic disk were obtained using a precision scanning contact pulse echo technique. Phase and cross-correlation velocity, and attenuation maps were used to form color images of microstructural variations. These acoustic images reveal microstructural variations not observable with X-ray radiography.
Acoustic images of silicon carbide ceramic disks were obtained using a precision scanning contact pulse-echo technique. Phase and cross-correlation velocity and attenuation maps were used to form color images of microstructural variations. These acoustic images reveal microstructural variations not observable with X-radiography.
High resolution (100 m), sequential Multispectral Atmospheric Mapping Sensor (MAMS) images were used in a study to calculate advective surface velocities using the Maximum Cross Correlation (MCC) technique. Radiance and brightness temperature gradient magnitude images were formed from visible (0.48 microns) and infrared (11.12 microns) image pairs, respectively, of Chandeleur Sound, which is a shallow body of water northeast of the Mississippi delta, at 145546 GMT and 170701 GMT on 30 Mar. 1989. The gradient magnitude images enhanced the surface water feature boundaries, and a lower cutoff on the gradient magnitudes calculated allowed the undesirable sunglare and backscatter gradients in the visible images, and the water vapor absorption gradients in the infrared images, to be reduced in strength. Requiring high (greater than 0.4) maximum cross correlation coefficients and spatial coherence of the vector field aided in the selection of an optimal template size of 10 x 10 pixels (first image) and search limit of 20 pixels (second image) to use in the MCC technique. Use of these optimum input parameters to the MCC algorithm, and high correlation and spatial coherence filtering of the resulting velocity field from the MCC calculation yielded a clustered velocity distribution over the visible and infrared gradient images. The velocity field calculated from the visible gradient image pair agreed well with a subjective analysis of the motion, but the velocity field from the infrared gradient image pair did not. This was attributed to the changing shapes of the gradient features, their nonuniqueness, and large displacements relative to the mean distance between them. These problems implied a lower repeat time for the imagery was needed in order to improve the velocity field derived from gradient imagery. Suggestions are given for optimizing the repeat time of sequential imagery when using the MCC method for motion studies. Applying the MCC method to the infrared brightness temperature imagery yielded a velocity field which did agree with the subjective analysis of the motion and that derived from the visible gradient imagery. Differences between the visible and infrared derived velocities were 14.9 cm/s in speed and 56.7 degrees in direction. Both of these velocity fields also agreed well with the motion expected from considerations of the ocean bottom topography and wind and tidal forcing in the study area during the 2.175 hour time interval.
In this work we focus on the height dependence of particle concentration, average velocity components, fluctuations in these velocities and, with the flow turned off, the sedimentation velocity. The latter quantities are measured using Particle Imaging Velocimetry (PIV). The PIV technique uses a 1-megapixel camera to capture two time-displaced images of particles in the bed. The depth of field of the imaging system is approximately 0.5 cm. The camera images a region with characteristic length of 2.6 cm for the small particles and 4.7 cm. for the large particles. The local direction of particle flow is determined by calculating the correlation function for sub-regions of 32 x 32 pixels. The velocity vector map is created from this correlation function using the time between images (we use 15 to 30 ms). The software is sensitive variations of 1/64th of a pixel. We produce velocity maps at various heights, each consisting of 3844 velocities. We break this map into three vertical zones for increased height information. The concentration profile is measured using an expanded (1 cm diameter) linearly polarized HeNe Laser incident on the fluidized bed. A COHU camera (gamma=1, AGC off) with a lens and a polarizer images the transmitted linearly polarized light to minimize the effects of multiply scattered light. The intensity profile (640 X 480 pixels) is well described by a Gaussian fit and the height of the Gaussian is used to characterize the concentration. This value is compared to the heights found for known concentrations. The sedimentation velocity is estimated using by imaging a region near the bottom of the bed and using PIV to measure the velocity as a function of time. With a nearly uniform concentration profile, the time can be converted to height information. The stable fluidized beds are made from large pseudo-monodisperse particles (silica spheres with radii (250-300) microns and (425-500) microns) dispersed in a glycerin/water mix. The Peclet number is sufficiently large that Brownian motion of the particles can be ignored and the Reynolds number sufficiently small that particle inertia is negligible. A packed particle bed is used to randomize and disperse the flowing fluid introduced by a peristaltic pump. The bed itself is a rectangular glass cell 8 cm wide (x), 0.8 cm deep and a height of 30.5 cm (z). The depth of field of the camera is approximately 0.5 cm so depth information is averaged. Over flow fluid is returned to the reservoir making a closed loop system. In these experiments the particles form a sediment approximately 5.7 cm high with the pump off and expand to 22 cm with the pump on. For the smaller particles the pump velocity is .5 millimeters per second and 1.1 millimeters per second for the large particles. At this concentration the bed has a very well defined top where particle concentration rapidly drops to zero.
Symmetrical phase-only filtering (SPOF) can be exploited to obtain substantial improvements in the results of data processing in particle-image velocimetry (PIV). In comparison with traditional PIV data processing, SPOF PIV data processing yields narrower and larger amplitude correlation peaks, thereby providing more-accurate velocity estimates. The higher signal-to-noise ratios associated with the higher amplitude correlation peaks afford greater robustness and reliability of processing. SPOF also affords superior performance in the presence of surface flare light and/or background light. SPOF algorithms can readily be incorporated into pre-existing algorithms used to process digitized image data in PIV, without significantly increasing processing times. A summary of PIV and traditional PIV data processing is prerequisite to a meaningful description of SPOF PIV processing. In PIV, a pulsed laser is used to illuminate a substantially planar region of a flowing fluid in which particles are entrained. An electronic camera records digital images of the particles at two instants of time. The components of velocity of the fluid in the illuminated plane can be obtained by determining the displacements of particles between the two illumination pulses. The objective in PIV data processing is to compute the particle displacements from the digital image data. In traditional PIV data processing, to which the present innovation applies, the two images are divided into a grid of subregions and the displacements determined from cross-correlations between the corresponding sub-regions in the first and second images. The cross-correlation process begins with the calculation of the Fourier transforms (or fast Fourier transforms) of the subregion portions of the images. The Fourier transforms from the corresponding subregions are multiplied, and this product is inverse Fourier transformed, yielding the cross-correlation intensity distribution. The average displacement of the particles across a subregion results in a displacement of the correlation peak from the center of the correlation plane. The velocity is then computed from the displacement of the correlation peak and the time between the recording of the two images. The process as described thus far is performed for all the subregions. The resulting set of velocities in grid cells amounts to a velocity vector map of the flow field recorded on the image plane. In traditional PIV processing, surface flare light and bright background light give rise to a large, broad correlation peak, at the center of the correlation plane, that can overwhelm the true particle- displacement correlation peak. This has made it necessary to resort to tedious image-masking and background-subtraction procedures to recover the relatively small amplitude particle-displacement correlation peak. SPOF is a variant of phase-only filtering (POF), which, in turn, is a variant of matched spatial filtering (MSF). In MSF, one projects a first image (denoted the input image) onto a second image (denoted the filter) as part of a computation to determine how much and what part of the filter is present in the input image. MSF is equivalent to cross-correlation. In POF, the frequency-domain content of the MSF filter is modified to produce a unitamplitude (phase-only) object. POF is implemented by normalizing the Fourier transform of the filter by its magnitude. The advantage of POFs is that they yield correlation peaks that are sharper and have higher signal-to-noise ratios than those obtained through traditional MSF. In the SPOF, these benefits of POF can be extended to PIV data processing. The SPOF yields even better performance than the POF approach, which is uniquely applicable to PIV type image data. In SPOF as now applied to PIV data processing, a subregion of the first image is treated as the input image and the corresponding subregion of the second image is treated as the filter. The Fourier transforms from both the firs and second- image subregions are normalized by the square roots of their respective magnitudes. This scheme yields optimal performance because the amounts of normalization applied to the spatial-frequency contents of the input and filter scenes are just enough to enhance their high-spatial-frequency contents while reducing their spurious low-spatial-frequency content. As a result, in SPOF PIV processing, particle-displacement correlation peaks can readily be detected above spurious background peaks, without need for masking or background subtraction.
A new all-electronic Particle Image Velocimetry technique that can efficiently map high speed gas flows has been developed in-house at the NASA Lewis Research Center. Particle Image Velocimetry is an optical technique for measuring the instantaneous two component velocity field across a planar region of a seeded flow field. A pulsed laser light sheet is used to illuminate the seed particles entrained in the flow field at two instances in time. One or more charged coupled device (CCD) cameras can be used to record the instantaneous positions of particles. Using the time between light sheet pulses and determining either the individual particle displacements or the average displacement of particles over a small subregion of the recorded image enables the calculation of the fluid velocity. Fuzzy logic minimizes the required operator intervention in identifying particles and computing velocity. Using two cameras that have the same view of the illumination plane yields two single exposure image frames. Two competing techniques that yield unambiguous velocity vector direction information have been widely used for reducing the single-exposure, multiple image frame data: (1) cross-correlation and (2) particle tracking. Correlation techniques yield averaged velocity estimates over subregions of the flow, whereas particle tracking techniques give individual particle velocity estimates. For the correlation technique, the correlation peak corresponding to the average displacement of particles across the subregion must be identified. Noise on the images and particle dropout result in misidentification of the true correlation peak. The subsequent velocity vector maps contain spurious vectors where the displacement peaks have been improperly identified. Typically these spurious vectors are replaced by a weighted average of the neighboring vectors, thereby decreasing the independence of the measurements. In this work, fuzzy logic techniques are used to determine the true correlation displacement peak even when it is not the maximum peak, hence maximizing the information recovery from the correlation operation, maintaining the number of independent measurements, and minimizing the number of spurious velocity vectors. Correlation peaks are correctly identified in both high and low seed density cases. The correlation velocity vector map can then be used as a guide for the particle-tracking operation. Again fuzzy logic techniques are used, this time to identify the correct particle image pairings between exposures to determine particle displacements, and thus the velocity. Combining these two techniques makes use of the higher spatial resolution available from the particle tracking. Particle tracking alone may not be possible in the high seed density images typically required for achieving good results from the correlation technique. This two-staged velocimetric technique can measure particle velocities with high spatial resolution over a broad range of seeding densities.
Application of high spectral resolution, Earth-based Fabry-Perot spectroscopy to the study of planetary atmospheres, for which current topics are outer planet HD and H2 spectra (atmospheric structure, D/H ratio), Mars CO2, CO, O2, and H2O spectra (atomspheric photochemistry), Venus H2O and HDO, associated laboratory spectroscopy (especially H2 overtone bands, HDO) was accomplished. Monochromatic charge coupled device (CCD) imaging photometry of the Jovian nebula, with images taken in rapid sequence among the diagnostic spectral lines of ionized sulfur species, provided self-supporting snapshots of the Jupiter/Io plasma conditions (spatially resolved electron and ion densities and temperatures), covering the post-Voyager period from 1981 and leading up to the Galileo tour in the early 1990s. High spectral resolution Fabry-Perot/charge coupled device (CCD) imaging of comets (OI, CI, and H2O(+) velocity maps and spatial distriubtions), and Io's charge exchanged neutral jet was studied.
Report describes simple flow experiment in which streak images of particles suspended in fluid and illuminated for short times processed into maps of velocity vectors. In experiment, semiautomatic data-reduction scheme used which recovers significant image data more efficiently than currently available automatic scheme.
Electronic Particle Image Velocimeter (PIV) techniques offer many advantages over conventional photographic PIV methods such as fast turn around times and simplified data reduction. A new all electronic PIV technique was developed which can measure high speed gas velocities. The Particle Displacement Tracking (PDT) technique employs a single cw laser, small seed particles (1 micron), and a single intensified, gated CCD array frame camera to provide a simple and fast method of obtaining two-dimensional velocity vector maps with unambiguous direction determination. Use of a single CCD camera eliminates registration difficulties encountered when multiple cameras are used to obtain velocity magnitude and direction information. An 80386 PC equipped with a large memory buffer frame-grabber board provides all of the data acquisition and data reduction operations. No array processors of other numerical processing hardware are required. Full video resolution (640x480 pixel) is maintained in the acquired images, providing high resolution video frames of the recorded particle images. The time between data acquisition to display of the velocity vector map is less than 40 sec. The new electronic PDT technique is demonstrated on an air nozzle flow with velocities less than 150 m/s.
Electronic Particle Image Velocimetric (PIV) techniques offer many advantages over conventional photographic PIV methods such as fast turn around times and simplified data reduction. A new all electronic PIV technique was developed which can measure high speed gas velocities. The Particle Displacement Tracking (PDT) technique employs a single CW laser, small seed particles (1 micron), and a single intensified, gated CCD array frame camera to provide a simple and fast method of obtaining two-dimensional velocity vector maps with unambiguous direction determination. Use of a single CCD camera eliminates registration difficulties encountered when multiple cameras are used to obtain velocity magnitude and direction information. An 80386 PC equipped with a large memory buffer frame-grabber board provides all of the data acquisition and data reduction operations. No array processors of other numerical processing hardware are required. Full video resolution (640 x 480 pixel) is maintained in the acquired images, providing high resolution video frames of the recorded particle images. The time between data acquisition to display of the velocity vector map is less than 40 sec. The new electronic PDT technique is demonstrated on an air nozzle flow with velocities less than 150 m/s.
Abstract We test the hypothesis that an embedded giant planet in the IM Lupi protostellar disk can produce velocity kinks seen in CO line observations as well as the spiral arms seen in scattered light and continuum emission. We inject planets into 3D hydrodynamics simulations of IM Lupi, generating synthetic observations using Monte Carlo radiative transfer. We find that an embedded planet of 2–3 M Jup can reproduce non-Keplerian velocity perturbations, or “kinks”, in the 12 CO J = 2–1 channel maps. Such a planet can also explain the spiral arms seen in 1.25 mm dust continuum emission and 1.6 μ m scattered-light images. We show that the wake of the planet can be traced in the observed peak velocity map, which appears to closely follow the morphology expected from our simulations and from analytic models of planet–disk interaction.
As part of the Advanced Subsonic Technology Program, a series of experiments was conducted at NASA Glenn Research Center on the effect of mixing enhancement devices on the aeroacoustic performance of separate flow nozzles. Initial acoustic evaluations of the devices showed that they reduced jet noise significantly, while creating very little thrust loss. The explanation for the improvement required that turbulence measurements, namely single point mean and RMS statistics and two-point spatial correlations, be made to determine the change in the turbulence caused by the mixing enhancement devices that lead to the noise reduction. These measurements were made in the summer of 2000 in a test program called Separate Nozzle Flow Test 2000 (SFNT2K) supported by the Aeropropulsion Research Program at NASA Glenn Research Center. Given the hot high-speed flows representative of a contemporary bypass ratio 5 turbofan engine, unsteady flow field measurements required the use of an optical measurement method. To achieve the spatial correlations, the Particle Image Velocimetry technique was employed, acquiring high-density velocity maps of the flows from which the required statistics could be derived. This was the first successful use of this technique for such flows, and shows the utility of this technique for future experimental programs. The extensive statistics obtained were likewise unique and give great insight into the turbulence which produces noise and how the turbulence can be modified to reduce jet noise.
Particle Image Velocimetry provides a means of measuring the instantaneous 2-component velocity field across a planar region of a seeded flowfield. In this work only two camera, single exposure images are considered where both cameras have the same view of the illumination plane. Two competing techniques which yield unambiguous velocity vector direction information have been widely used for reducing the single exposure, multiple image data: cross-correlation and particle tracking. Correlation techniques yield averaged velocity estimates over subregions of the flow, whereas particle tracking techniques give individual particle velocity estimates. The correlation technique requires identification of the correlation peak on the correlation plane corresponding to the average displacement of particles across the subregion. Noise on the images and particle dropout contribute to spurious peaks on the correlation plane, leading to misidentification of the true correlation peak. The subsequent velocity vector maps contain spurious vectors where the displacement peaks have been improperly identified. Typically these spurious vectors are replaced by a weighted average of the neighboring vectors, thereby decreasing the independence of the measurements. In this work fuzzy logic techniques are used to determine the true correlation displacement peak even when it is not the maximum peak on the correlation plane, hence maximizing the information recovery from the correlation operation, maintaining the number of independent measurements and minimizing the number of spurious velocity vectors. Correlation peaks are correctly identified in both high and low seed density cases. The correlation velocity vector map can then be used as a guide for the particle tracking operation. Again fuzzy logic techniques are used, this time to identify the correct particle image pairings between exposures to determine particle displacements, and thus velocity. The advantage of this technique is the improved spatial resolution which is available from the particle tracking operation. Particle tracking alone may not be possible in the high seed density images typically required for achieving good results from the correlation technique. This two staged approach offers a velocimetric technique capable of measuring particle velocities with high spatial resolution over a broad range of seeding densities.
This paper presents a novel technique to validate and predict the rover slips on Martian surface for NASA's Mars Exploration Rovers. Different from the traditional approach, the proposed method uses the actual velocity profile of the wheels and the digital elevation map (DEM) from the stereo images of the terrain to formulate simplified equations of motion of the rovers. A weighted factor to the wheel-ground speed from the empirical data comprises the velocity equations of the simplified differential-algebraic system of the rover motion. Applying the discretization operator to these equations, the full kinematics state of the rover is then resolved by the configuration kinematics solution in the Robot Sequencing and Visualization Program (RSVP). This method produced accurate simulation of the rover movements compared with these of the earth testing vehicle. Using the telemetry from the onboard Visual Odometry, the simulated rover path also compares well with the actual track of the vehicle. Preliminary results indicated that the proposed computational method is very effective in planning the path of the rovers on the high-slope areas.
The mechanisms giving rise to certain airglow and auroral emissions and their height distribution are briefly surveyed. The basic principles of operation of scanning Fabry-Perot spectrometers are outlined in order to illustrate the calibration and use of such instruments to measure Doppler broadening and shift of atomic emission lines in the spectrum of the airglow and aurora and hence to infer atmospheric temperatures and wind velocities. Technical details of some specific instruments are given. Brief mention is made of the use of wide angle Michelson interferometers for the same application. Emphasis is given to the future importance of imaging Fabry-Perot spectrometers to permit the detailed mapping of temperature and wind velocity, especially in the auroral regions.
Video-based altimetric and obstruction detection systems for aircraft have been partially developed. The hardware of a system of this type includes a downward-looking video camera, a video digitizer, a Global Positioning System receiver or other means of measuring the aircraft velocity relative to the ground, a gyroscope based or other attitude-determination subsystem, and a computer running altimetric and/or obstruction-detection software. From the digitized video data, the altimetric software computes the pixel velocity in an appropriate part of the video image and the corresponding angular relative motion of the ground within the field of view of the camera. Then by use of trigonometric relationships among the aircraft velocity, the attitude of the camera, the angular relative motion, and the altitude, the software computes the altitude. The obstruction-detection software performs somewhat similar calculations as part of a larger task in which it uses the pixel velocity data from the entire video image to compute a depth map, which can be correlated with a terrain map, showing locations of potential obstructions. The depth map can be used as real-time hazard display and/or to update an obstruction database.
The Particle Image Velocimetry, PIV, has been implemented for the investigation of high-speed jet flows at the NASA Langley Research Center. In this approach the velocity (displacement) is found as the location of a peak in the correlation map of particle images acquired in quick succession. In the study, the technique for the correct seeding of the flow field were developed and implemented and the operational parameters influencing the accuracy of the measurement have been optimized.
Particle imaging velocimetry, which is rapidly becoming an essential tool for the synoptic measurement of two dimensional velocity fields, is addressed. This rapid development is carried out by a growing number of fluid mechanics experimentalists who recognize the unique capabilities of the technique to measure velocity fields in both space and time, and is supported by the concomitant development and availability of microcomputer hardware and software. Various applications of the technique to map different flow regimes are described and illustrated. Hardware implementations of the technique which utilize both conventional photography for image acquisition as well as digital 'online' methods for integrated image acquisition and processing are discussed. Recommendations to further enhance the technique and make it possible to map the three velocity components in three dimensional flow regions are given.