Search NASA⌕ Search

SEARCH · Search NASA

Results for “PDV”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

PDV Inspection and Analysis Demonstration: 2024 PDV Workshop

This document walks a user through a demonstration of working with PDV digitizer data using python. This demonstration and included suggested exercises will be used at the 2024 PDV workshop hands-on session as an example and skill-development training session. The tutorial allows the user to generate synthetic but realistic PDV waveform data and visualize/inspect the results using spectrograms and waveform viewing tools.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1999

Investigation of Point Doppler Velocimetry (PDV) for Transition Detection in Boundary Layers

A two-component Point Doppler Velocimeter (PDV) system has been improved through the use of vapor-limited iodine cells that have responses that are insensitive to temperature variations. Two-component PDV velocity measurements have been obtained for a 1 inch diameter uniform circular jet flow at a nominal exit velocity of 60 m/sec, corresponding to a Reynolds number of 100,000. Similar data have also been obtained for an annular jet and a swirling jet, These PDV data runs have been duplicated to judge the repeatability of these measurements, and also have been compared with hot wire anemometer data for the same flow conditions. PDV mean velocity results are repeatable to within approximately 1-2 meters per second; the PDV RMS velocity results are also quite repeatable. Exit profiles of PDV mean axial velocity data generally agree with hot wire anemometer results to within about 2 meters per second as well. However, the PDV RMS velocity results are consistently lower than the hot wire results everywhere but at the exit of the standard jet, where they are too high relative to the hot wire data. This is believed to at least be partially due to the method used to compute the RMS (Root Mean Square).

Kuhlman, John↗
NASA NTRS1999

Investigation of Point Doppler Velocimetry (PDV) for Transition Detection in Boundary Layers

A two component Point Doppler Velocimetry (PDV) system has been developed and tested. Improvements were made to an earlier PDV system, in terms of experimental techniques, as well as the data acquisition and reduction software. Measurements of the streamwise and spanwise mean and fluctuating velocities for flows from a rectangular channel and over an NACA 0012 airfoil were made, and the data were compared against hot wire data. The closest to the airfoil surface that PDV measurements could be made was on the order of 0.005 m(0.2", z/c = 0.0169). When the PDV and hot wire data were compared, the time traces for each appeared similar. The mean velocities agreed to within plus or minus 2 m/sec, while the RMS velocities agreed to plus or minus 0.4 m/sec. While the PDV time autocorrelations agreed with those of the hot wire data, the PDV power spectral densities were noisier above 750 Hz. A major source of error in these experiments was determined to be the drifting of the iodine cell stem temperatures. While the stem temperatures were controlled to within plus or minus 0.1 C, this could lead to a frequency shift of as much as 6 MHz, which translates into an error of 1.6 m/sec for the back scatter channel, and up to 6.9 m/sec for the forward scatter channel. These error estimates are consistent with the observed error magnitudes.

Kuhlman, John M.↗
DOE OSTI2024

Enhanced PDV waveform search and analysis method using parallel circular-convolution / cross-correlation for improved dynamic surface velocity extraction [Poster]

Previous work on exhaustive search methodologies for extracting best-match parameters pertaining to dynamic surface quantities from PDV was done by cross-correlating synthetically generated PDV waveforms with observed counterparts using the circular-convolution theorem. This work was further developed into an open-source PDV analysis toolkit called CCPDVANALYSIS which expands upon and enhances the previously tested methods by parallelizing serial algorithmic components and incorporating a comprehensive script library for different flavors of instantaneous frequency functions utilized in generating synthetic PDV waveforms. Results of these enhancements have been shown to markedly decrease execution times of exhaustive search and extraction algorithms and produce improved velocity recoveries for low-velocity and dynamically varying velocity signals. The CCPDVANALYSIS script library demonstrates an advanced method for extracting velocities from low-velocity and non-constant velocity signals further extending and improving the methods beyond capabilities of traditional frequency domain tools.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1997

Accuracy Study of a 2-Component Point Doppler Velocimeter (PDV)

A two-component Point Doppler Velocimeter (PDV) which has recently been developed is described, and a series of velocity measurements which have been obtained to quantify the accuracy of the PDV system are summarized. This PDV system uses molecular iodine vapor cells as frequency discriminating filters to determine the Doppler shift of laser light which is scattered off of seed particles in a flow. The majority of results which have been obtained to date are for the mean velocity of a rotating wheel, although preliminary data are described for fully-developed turbulent pipe flow. Accuracy of the present wheel velocity data is approximately +/- 1 % of full scale, while linearity of a single channel is on the order of +/- 0.5 % (i.e., +/- 0.6 m/sec and +/- 0.3 m/sec, out of 57 m/sec, respectively). The observed linearity of these results is on the order of the accuracy to which the speed of the rotating wheel has been set for individual data readings. The absolute accuracy of the rotating wheel data is shown to be consistent with the level of repeatability of the cell calibrations. The preliminary turbulent pipe flow data show consistent turbulence intensity values, and mean axial velocity profiles generally agree with pitot probe data. However, there is at present an offset error in the radial velocity which is on the order of 5-10 % of the mean axial velocity.

Kuhlman, John↗
NASA NTRS1992

Simultaneous mapping of the unsteady flow fields by Particle Displacement Velocimetry (PDV)

Current experimental and computational techniques must be improved in order to advance the prediction capability of the longitudinal vortical flows shed by underwater vehicles. The generation, development, and breakdown mechanisms of the shed vortices at high Reynolds numbers are not fully understood. The ability to measure hull separated vortices associated with vehicle maneuvering does not exist at present. The existing point-by-point measurement techniques can only capture approximately the large 'mean' eddies but fail to meet the dynamics of small vortices during the initial stage of generation. A new technique, which offers a previously unavailable capability to measure the unsteady cross-flow distribution in the plane of the laser light sheet, is called Particle Displacement Velocimetry (PDV). PDV consists of illuminating a thin section of the flowfield with a pulsed laser. The water is seeded with microscopic, neutrally buoyant particles containing imbedded fluorescing dye which responds with intense spontaneous fluorescence with the illuminated section. The seeded particles in the vortical flow structure shed by the underwater vehicle are illuminated by the pulse laser and the corresponding particle traces are recorded in a single photographic frame. Two distinct approaches were utilized for determining the velocity distribution from the particle traces. The first method is based on matching the traces of the same particle and measuring the distance between them. The direction of the flow can be identified by keeping one of the pulses longer than the other. The second method is based on selecting a small window within the image and finding the mean shift of all the particles within that region. The computation of the auto-correlation of the intensity distribution within the selected sample window is used to determine the mean displacement of particles. The direction of the flow is identified by varying the intensity of the laser light between pulses. Considerable computational resources are required to compute the auto-correction of the intensity distribution. Parallel processing will be employed to speed up the data reduction. A few examples of measured unsteady vortical flow structures shed by the underwater vehicles will be presented.

Huang, Thomas T.↗
DOE OSTI2024

PDV Sensitivity [Slides]

The concept of PDV sensitive is not new. But we tend not to discuss sensitivity in a rigorous, quantified way. That should change. Indeed there is a natural definition, which is just the energy of light from the target within the analysis window that gives a SNR of 10 in the frequency domain. Systems designers, diagnostic engineers, and operators should use the language of sensitivity: (1) Absolute sensitivity: nW * ns; (2) Launch power: mW or dBm; (3) Relative surface return: dB; (4) Relative sensitivity: dB * ns. This will help us design better systems, choose the correct system for the job, maintain our systems, and tune our systems.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Modeling and Design of a Sub-Nyquist PDV Digitization System

Photon-Doppler Velocimetry (PDV) uses interferometry of a transmitted optical signal reflected from a fast-moving device under test (DUT) to generate a product signal at a doppler shifted frequency which is proportional to the velocity of the object being measured. The technique has applications in shock physics, where it is used to measure fast-moving objects over a short lifespan of travel, such as a bullet, shrapnel, or other shock accelerated object.

42 ENGINEERING↗
DOE OSTI2025

PDV Methods and Analysis for Surveillance of Explosive Components

The Weapons Evaluation Test Laboratory (WETL) at Sandia is collaborating with Lawrence Livermore National Laboratory (LLNL) to enhance explosive surveillance testing by integrating Photon Doppler Velocimetry (PDV) data. This project has streamlined the testing environment, reducing hardware costs and training needs while improving data collection efficiency and usability for lab technicians.

Kress, Matthew Kip [Sandia National Laboratories (↗
NASA NTRS1997

Planar Doppler Velocimetry for Large-Scale Wind Tunnel Testing

Recently, Planar Doppler Velocimetry (PDV) has been shown by several laboratories to offer an attractive means for measuring three-dimensional velocity vectors everywhere in a light sheet placed in a flow. Unlike other optical means of measuring flow velocities, PDV is particularly attractive for use in large wind tunnels where distances to the sample region may be several meters, because it does not require the spatial resolution and tracking of individual scattering particles or the alignment of crossed beams at large distances. To date, demonstrations of PDV have been made either in low speed flows without quantitative comparison to other measurements, or in supersonic flows where the Doppler shift is large and its measurement is relatively insensitive to instrumental errors. Moreover, most reported applications have relied on the use of continuous-wave lasers, which limit the measurement to time-averaged velocity fields. This work summarizes the results of two previous studies of PDV in which the use of pulsed lasers to obtain instantaneous velocity vector fields is evaluated. The objective has been to quantitatively define and demonstrate PDV capabilities for applications in large-scale wind tunnels that are intended primarily for the production testing of subsonic aircraft. For such applications, the adequate resolution of low-speed flow fields requires accurate measurements of small Doppler shifts that are obtained at distances of several meters from the sample region. The use of pulsed lasers provides the unique capability to obtain not only time-averaged fields, but also their statistical fluctuation amplitudes and the spatial excursions of unsteady flow regions such as wakes and separations. To accomplish the objectives indicated, the PDV measurement process is first modeled and its performance evaluated computationally. The noise sources considered include those related to the optical and electronic properties of Charge-Coupled Device (CCD) arrays and to speckle effects associated with coherent illumination from pulsed lasers. The signal noise estimates are incorporated into the PDV signal analysis process and combined with computed scattering signals using a Mie scattering theory for polydisperse smoke particles. The relevant parameters incorporate a range of practical aerodynamic test conditions and facility sizes. The results define the optimum instrument configurations, show that the expected signal levels from a practical PDV system are sufficiently large to allow its useful application in large facilities, and show that the expected velocity measurement uncertainties are small compared to the mean velocities of interest for most subsonic, large-scale wind tunnel testing. Experimental studies using several experimental bench-top setups are then described that validate the physics of the PDV model and to calibrate its computed results. The validated model allows estimates of the uncertainties of PDV measurements and a complete definition of the PDV capabilities to be made with sufficient confidence to decide the viability of PDV for large-scale wind tunnel applications.

McKenzie, Robert L.↗
NASA NTRS1994

Measurement Capabilities of Single-Pulse Planar Doppler Velocimetry

Preliminary investigations are described of a method that is capable of measuring instantaneous, 3-D, velocity vectors everywhere in a light sheet generated by a pulsed laser. The technique, here called Planar Doppler Velocimetry (PDV), is a variation of a new concept for velocity measurements that was called Doppler Global Velocimetry (DGV) in its original disclosure. The concept relies on the use of a narrowband laser and measurements of the Doppler shift of scattered light from particles moving with a flow. The Doppler shift is recorded as a variation in transmission through a sharp-edged spectral filter provided by iodine vapor in a cell. Entire fields of velocity can be determined by using a solid-state camera to record the intensity variations throughout the field of view. However, the implementation of DGV has been centered principally on the use of high power, continuous-wave, ion lasers and measurement times that are determined by the 30-ms framing times of standard video cameras. Hence, they provide velocity fields that are averaged in time at least over that period. On the other hand, the PDV concept described in this presentation incorporates a high energy, repetitively pulsed, Nd-YAG laser that is injection-seeded to make it narrowband and then frequency-doubled to provide light at frequencies absorbed by the iodine vapor. The duration of each pulse is less than 10 nanoseconds. When used in combination with nonstandard, scientific quality, solid state cameras, a sequence of images can be obtained that provides instantaneous velocity vectors everywhere in the field of view. The investigations described in this paper include an accurate characterization of the iodine cell spectral behavior and its influence on the PDV measurements, a derivation of the PDV signal analysis requirements, and the unique aspects of the pulsed laser behavior related to this application. In addition, PDV measurements are to be demonstrated using data from a rotating wheel target and from the flow of a subsonic jet. Initially, single optical fiber light collection and photomultiplier detectors will be substituted for solid state cameras. Those results will allow the determination of the fundamental limitations of the PDV technique without the complications of image acquisition and processing. They will then be used to provide an analysis of the measurement capabilities of PDV both in small aerodynamic research wind tunnels and in large wind tunnels designed for production airframe and propulsion testing. Future plans include the implementation of solid state cameras and the development of the required image acquisition and processing software. Eventually, the PDV technique will be applied to an aerodynamic research program related to transonic wing flutter.

McKenzie, Robert L.↗