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At least 253 records · Page 14

Wind Tunnel to Full Scale Mapping of Winds and Loads for Launch-Vehicle Ground Wind Loads

A launch vehicle ground-wind-loads program was conducted at the NASA Langley Transonic Dynamics Tunnel. The objectives were to quantify key aerodynamic and structural characteristics that impact the occurrence of large wind-induced oscillations of a launch vehicle when exposed to ground winds prior to launch. Of particular interest is the dynamic response of a launch vehicle when a von Kármán vortex street forms in the wake of the vehicle resulting in quasiperiodic lift and drag forces.Vehicle response to these quasiperiodic forces can become quite large when the frequency of vortex shedding nears that of a lowly-damped structural mode thereby exciting a resonant response. The study of ground-wind-loads presents unique challenges to quantify significant characteristics of the approaching wind and to relate the wind-tunnel acquired static, dynamic, and gravitational loads to full-scale vehicles. This paper will explain the correlation process between model-scale and full-scale wind characteristics and resulting structural loads. Characterization of the wind at a launch site requires knowledge of the vehicle dynamics and the anemometer performance. Vehicle dynamics dictate the frequency range of interest in characterization of the atmospheric turbulence, and in defining the required data window-length to model dynamic oscillation build-up during wind-gust analysis. Knowledge of the anemometer performance is required to infer peak wind magnitudes since most anemometers cannot directly measure instantaneous peak values. Following proper wind-speed correlation, static and dynamic wind-tunnel loads must be converted to full-scale equivalent values to derive design and operational guidance. In addition to the direct computation of full-scale equivalent loads using relevant scaling parameters, the corrections applied to dynamic loads accounting for differences in structural damping will also be discussed. Furthermore, a phenomenon that manifests in many slender and flexible launch vehicles is a load magnification due to gravity resulting from mass offset under deflection. A method for scaling this contribution from model-scale to full-scale is presented. Finally, a comparison of wind-tunnel derived loads using the described methodologies to launch vehicle measured loads is presented.

ground wind loads↗

Wind-Tunnel to Full-Scale Mapping of Winds and Loads for Launch-Vehicle Ground Wind Loads

A launch vehicle ground-wind-loads program was conducted at the NASA Langley Transonic Dynamics Tunnel. The objectives were to quantify key aerodynamic and structural characteristics that impact the occurrence of large wind-induced oscillations of a launch vehicle when exposed to ground winds prior to launch. Of particular interest is the dynamic response of a launch vehicle when a von Kármán vortex street forms in the wake of the vehicle resulting in quasiperiodic lift and drag forces. Vehicle response to these quasiperiodic forces can become quite large when the frequency of vortex shedding nears that of a lowly-damped structural mode thereby exciting a resonant response. The study of ground-wind-loads presents unique challenges to quantify significant characteristics of the approaching wind and to relate the wind-tunnel acquired static, dynamic, and gravitational loads to full-scale vehicles. This paper will explain the correlation process between model-scale and full-scale wind characteristics and resulting structural loads. Characterization of the wind at a launch site requires knowledge of the vehicle dynamics and the anemometer performance. Vehicle dynamics dictate the frequency range of interest in characterization of the atmospheric turbulence, and in defining the required data window-length to model dynamic oscillation build-up during wind-gust analysis. Knowledge of the anemometer performance is required to infer peak wind magnitudes since most anemometers cannot directly measure instantaneous peak values. Following proper wind-speed correlation, static and dynamic wind-tunnel loads must be converted to full-scale equivalent values to derive design and operational guidance. In addition to the direct computation of full-scale equivalent loads using relevant scaling parameters, the corrections applied to dynamic loads accounting for differences in structural damping will also be discussed. Furthermore, a phenomenon that manifests in many slender and flexible launch vehicles is a load magnification due to gravity resulting from mass offset under deflection. A method for scaling this contribution from model-scale to full-scale is presented. Finally, a comparison of wind-tunnel derived loads using the described methodologies to launch vehicle measured loads is presented.

Ground wind loads↗

Novel Mars In-situ Wind & Wind-Blown Particle Measurement Instruments

Mars Sonic Anemometer We have developed the Mars Sonic Anemometer: an in-situ wind measuring instrument. It uses transducers to transmit ‘chirps’ through a volume of atmosphere and measures wind direction, speed, and temperature based on the chirp travel times when received by a paired transducer (fig 1). This method can rapidly take multiple samples of wind (~20 Hz) and can measure gentler wind speeds (~0.05 m/s) than current planetary weather stations (~1Hz and ~2m/s) and is more durable than recent Mars wind sensors. The Sonic Anemometer opens new avenues of research into Mars surface-to-atmosphere exchange (e.g., of heat, momentum or trace gases). It also can be recast as a hydrogen ortho-para sensor for a giant planet descent probe or as an infrasound detector for Earth or Venus. The performance of the sensor at Mars-like conditions of 6mbar of CO2 was validated at the Danish Mars Wind Tunnel.

Donald J Banfield↗

NCSU Flux Tower Data

NCSU Flux Tower Data Level b1: QC checks applied to measurements Data Format: CSV Description: See "instrument descripotion Site: Houston, TX; Tracking Aerosol Convection interactions ExpeRiment (HOU) Location: Houston, TX; AMF1 (main site for TRACER) Facility Code: M1 Category: Aerosol Properties Data Type: PI Data Source Instrument/Data: Sonic Anemometer, 3 Condensation Particle Counters, 1 SP2, 1 POPS Start Date: 2022-06-01 End Date: 2022-9-26 Contact PI: Markus Petters (mdpetter@ncsu.edu) Funding Source: DOE ASR award US Department of Energy, Office of Science, Biological and Environment Research (grant no. DE-SC 0021074) Instrument description The NCSU flux tower was located next to the AMF sampling pad. The flux tower consisted of a 10 m telescoping tower. Mounted at the top of the tower was a sonic anemometer (RM Young 8000) and a Krypton Hygrometer. A sample line (¼” conductive tubing) and communication line was laid to a trailer located underneath and slightly adjacent to the tower. The sample line brought aerosol inside the trailer at 4.5 L min-1, where it was distributed between 3 CPCs (TSI 3776c, Dc ~ 2.5 nm, TSI 3771, Dc ~10nm, and TSI 3772, Dc ~40 nm) with different size cuts. Also sampling was a printed particle optical spectrometer (POPS) and a single particle soot photometer (SP2). Please contact mdpetter@ncsu.edu for further information.

54 ENVIRONMENTAL SCIENCES↗

Review of IRT CFD & Diffuser Study Results - February 2024 Test Entries

In February 2024, multiple test entries were conducted in the NASA Glenn Research Center Icing Research Tunnel (IRT) to collect CFD validation data, particularly near the facility heat exchanger inlet and outlet planes, measure the boundary layer characteristics in the test section, and survey the diffuser flow field. The CFD validation testing utilized ultrasonic anemometers to acquire 3-components of flow velocity at the inlet and outlet planes across a row of heat exchanger blocks. The test section boundary layer surveys used 18-inch boundary layer rakes centered on each of the four test section surfaces with pitot tubes at turntable centerline. The diffuser study included surveys at three streamwise positions within the diffuser with a 5-hole flow angularity probe at between 3 and 7 lateral locations at a given streamwise position. All IRT diffuser data were acquired with the flow angularity probe at the centerline of the test section. An ultrasonic anemometer was also used in the diffuser to survey near-wall velocity profiles.

Icing↗

Observations of the Marine Atmospheric Boundary Layer’s Response to a Solar Eclipse

The atmospheric response to the solar eclipse of 8 April 2024 in North America is investigated with a specific focus on the marine atmospheric boundary layer (MABL). We leverage measurements collected during the Third Wind Forecast Improvement Project (WFIP3), including Doppler lidars, sonic anemometers, and thermodynamic profiler data to investigate the atmospheric response across sites that experienced partial eclipse conditions with nearly 90% obscuration. Using these measurements, we examine eclipse-induced changes in key meteorological parameters, such as temperature, wind speed, and turbulent fluxes. Most previous eclipse studies have been conducted over land, whereas this study provides new observations for both coastal and marine environments, offering additional insight into eclipse-driven variability in the MABL. The findings confirm a notable decrease in downwelling shortwave radiation during the eclipse, which results in rapid cooling of surface air. The temperature reduction ranges from $1.2^\circ \text {C}$ to $1.4^\circ \text {C}$ in coastal regions and from $0.3^\circ \text {C}$ to $0.5^\circ \text {C}$ over the ocean. This analysis suggests that the MABL’s higher thermal inertia compared to coastal regions moderates the temperature decrease during the eclipse. Wind speed exhibits a more complex behavior, as it is influenced by both the MABL and preexisting synoptic conditions. Although a reduction in wind speed is observable up to approximately 140 m above ground level (AGL) at more inland sites, at other locations closer to the coast, this reduction is constrained to the lowest 100 m AGL. Turbulence parameters retrieved from sonic anemometers, such as turbulence kinetic energy, turbulent heat flux, and friction velocity, decrease during the eclipse at coastal sites, accompanied by a brief transition of atmospheric stability from unstable to neutral or weakly stable conditions. For the open-ocean sites, the variability in turbulence statistics and atmospheric stability is minimal during the occurrence of the eclipse.

16 TIDAL AND WAVE POWER↗

Behavior and mechanisms of Doppler wind lidar error in complex terrain: stable flow case study at Perdigão

A numerical experiment is carried out investigating the magnitude of biases in ground-based lidar measurements in complex flow conditions. Biases assessed include those arising from flow curvature and from the interaction of turbulence with the wind field reconstruction (WFR) algorithms used by a WindCube lidars and anemometers. RANS-CFD and WRF-LES simulations were performed for the Perdig˜ao Field Experiment site for a range of atmospheric conditions. Virtual anemometer and lidar data were generated for four locations: two near exposed ridge tops and two in low-speed regions in the valley. The LES data at these four locations show that the scalar inflation terms (the relation between scalar and vector averaged wind speed) for virtual lidar and virtual cups agree very well with predictions using perturbation theory. While the lidar errors vary greatly with location and height, the contribution from the flow curvature tends to be larger than the differences arising from scalar inflation. For one lidar/mast pair near the ridge top, comparisons between simulations and measurements are carried out for a resonant mountain wave event on June 14th, 2017, and for the whole duration of the Perdigão campaign for winds perpendicular to the ridges. The lidar error during the mountain wave, a period of strong stability and low inversion height, is significantly larger than the campaign average. The sensitivity of the lidar error to atmospheric stability is confirmed by the RANS simulations, which suggests strong sensitivity of flow curvature error to stability conditions and to the shape of the wind speed profile near the top of the boundary layer.

17 WIND ENERGY↗

CROCUS 3-D wind data from Argonne Deployable Mast during Urban Canyon IOP July 2024

During the Community Research on Climate and Urban Science (CROCUS) Urban Integrated Field Laboratory (UIFL) project, led by Argonne National Laboratory, this dataset was collected by METEK uSonic-3 Class A MP sonic anemometer at 10 meter height of the Argonne Deployable Mast (ADM) from July 26 to July 28, 2024, .As part of the CROCUS 2024 Urban Canyon Intensive Observation Period (IOP), 3-D sonic anemometer on the ADM was set up to provide continuous measurements of wind and temperature in Chicago during July 2024. These measurements cover winds in X-, Y- and Z- direction and sonic temperature at 30-Hz resolution. The data are formatted as NetCDF (.nc) files, making them easily accessible using common software such as MATLAB, R, and Python.

54 ENVIRONMENTAL SCIENCES↗

Isolated Building Wake Experiment At Texas Tech University’s Wind Engineering Research Field Lab

Texas Tech University’s (TTU’s) Wind Engineering Research Field Lab (WERFL) building is an idealized rectangular building on the scale of a typical suburban house. It is surrounded almost entirely by unobstructed planes and is built on a turn table. WERFL is ideal for providing measurements of an idealized building under real-world conditions including atmospheric stability and variable relative wind angles. The intent of this data set is for verification and validation of computational fluid dynamics model simulations of flow around buildings. The primary focus of this dataset features 24 3D sonic anemometers (20 downwind and 4 upwind) and 10 2D sonic anemometers (9 downwind and 1 upwind) located in the immediate vicinity of the WERFL building. It also includes a variety of instruments intended to measure the ambient meteorological parameters and the undisturbed wind profile.

17 WIND ENERGY↗

Automated Generation of Weather and Climate Analysis Products

Wind roses are an important part to site operations as they depict the wind speed and direction percentage over a time period. In this project, I automated the production of wind roses for SRS meteorological towers. I developed a script to sequence through the dataset by period and create a wind rose for each of those period sets. Graphics were generated for every 4-hour period of the day, for each of the 4 heights of the instruments, for every half of each month. A 10-year climatological period consisted of wind speed and directions measurements at 15-minute intervals. We used the years 2014-2024 as the wind instruments on the tower were upgraded to sonic anemometers in early 2014. I then compared these wind roses to those from a previous study done in 2003 to analyze the differences and similarities in the wind patterns. The wind roses are also used to understand environmental transport conditions at SRS. When comparing the different heights of the anemometers to the 2003 report, there is a similarity in the fact that the direction is relatively the same across the levels, with the wind speeds increasing as you get higher in the air. When comparing the season of our graphs against the 2003 report, we noticed that the winter and summer months appear to have very similar wind directions. However, in the spring we noticed that there were more southerly winds compared to the 2003 report which had more westerly winds. Finally, in the fall months, we noticed that there was a higher percentage of northeasterly winds, while the 2003 report showed more southeasterly winds. We can use these summaries to estimate the directional dependence of dose that the surrounding areas receive throughout the year according to time of day.

47 OTHER INSTRUMENTATION↗

Eddy Covariance Flux / Processed Data

This dataset consists of estimates of mean and turbulent quantities from a vertical array of over a dozen instruments, which include sonic anemometers, relative humidity and temperature, pressure, rain, ocean temperature and salinity, ocean skin temperature, and surface wave spectra and bulk wave parameters. One of the levels consists of a paired sonic anemometer and infrared gas analyzer for direct covariance sensible and latent heat fluxes. Mean and turbulent quantities are determined over 20-minute averaging periods.

17 WIND ENERGY↗

Eddy Covariance Flux / Processed Data

This dataset consists of estimates of mean and turbulent quantities from a vertical array of instruments installed on the barge met mast, including: sonic anemometers, relative humidity and temperature, pressure, and rain. One of the levels consists of a paired sonic anemometer and infrared gas analyzer for direct covariance sensible and latent heat fluxes. Mean and turbulent quantities are determined over 20-minute averaging periods.

17 WIND ENERGY↗

Eddy Covariance Flux / Processed Data

This dataset consists of estimates of mean and turbulent quantities from a vertical array of instruments installed on the barge met mast, including: sonic anemometers, relative humidity and temperature, pressure, and rain. One of the levels consists of a paired sonic anemometer and infrared gas analyzer for direct covariance sensible and latent heat fluxes. Mean and turbulent quantities are determined over 20-minute averaging periods.

17 WIND ENERGY↗

Combined Mesonet and Tracker

Title: Combined Mesonet and Trackers (UNL Mobile Mesonets) Authors University of Nebraska PI: Adam Houston, UNL Professor (ahouston2@unl.edu) Mailing Address: 126 Bessey Hall P.O. Box 880340 Lincoln, NE 68588-0340 CoMeT Overview The University of Nebraska-Lincoln operates three Combined Mesonet and Tracker (CoMeTs). CoMeTs are Ford Explorers (model years 2015, 2017, and 2019) with forward-mounted suites of meteorological sensors and dual moonroofs, combining the capability of a mobile mesonet to collect near-surface observations with the capability of an unmanned aircraft systems (UAS) tracker vehicle, which enables an observer in the second row of seats to see the aircraft and maintain compliance with Federal Aviation Administration policies on UAS operation. The CoMeTs collect observations of slow temperature and humidity at ~2 m above ground level (AGL) using a Vaisala HMP155A, fast temperature at ~2 m AGL using a Campbell Scientific 109SS-L thermistor, pressure at ~2.5 m AGL using a Vaisala PTB210, wind speed and direction at ~3.25 m AGL using an R.M. Young 05103 propeller anemometer, and vehicle heading using a KVH Industries C-100 fluxgate compass (Barbieri et al. 2019). The HMP155A and 109SS-L thermistor are shielded and aspirated within a U-tube (Waugh and Frederickson 2010; Houston et al. 2016). This list of sensors is also included in the CoMeT data file metadata. Manufacturer specifications for these instruments are given in Table 1 of Hanft and Houston (2018). The reported measured quantities are summarized below.CoMeT-3 was funded through an equipment allocation included in the NSF TORUS award (AGS-1824649). Instrument Description The specific sensors included on each CoMeT are summarized in the table at the end of this section. In general each CoMeT collects observations of slow temperature and humidity at ~2 m above ground level (AGL) using a Vaisala HMP155, fast temperature at ~2 m AGL using a Campbell Scientific 109SS-L thermistor, pressure at ~2.5 m AGL using a Vaisala PTB210 barometer with a Gill pressure port, wind speed and direction at ~3.25 m AGL using an R.M. Young 05103 propeller anemometer, position using a Garmin 19x HVS receiver, and vehicle heading using a KVH Industries C-100 fluxgate compass. The HMP155 and 109SS are shielded and aspirated within a U-tube (Waugh and Frederickson 2010; Houston et al. 2016). Fast temperature and corrected RH measurements (using sensors housed within the U-tube) have a time constant of 10-12 s based on data collected across a temperature and RH shock during the CLOUD-MAP 2017 calibration/validation tests on June 26, 2017. Vehicle speed was < 10 kts for this test. CoMeT-1 CoMeT-2 CoMeT-3 Slow Temperature Slow RH Vaisala HMP155A-L20-PT Part #: 22280-7 Vaisala HMP155E Part #: E1AA11A0B1A1A0A Vaisala HMP155E Part #: E1AA11A0B1A1A0A Fast temperature Campbell Scientific 109SS-L20-PT Part #: 21448-3 Campbell Scientific 109SS-L12-PW Part #: 21448-109 Campbell Scientific 109SS-L12-PW Part #: 21448-150 Pressure Vaisala PTB-210 Part #: A1A1B Gill Pressure Port Part #: 61002 Vaisala PTB-210 Part #: A1A1B Gill Pressure Port Part #: 61002 Vaisala PTB-210 Part #: A1A1B Gill Pressure Port Part #: 61002 Wind RM Young 05103-L20-PT Part #: 18435-310 RM Young 05103-L20-PW Part #: 18435-244 RM Young 05103-L20-PW Part #: 18435-244 GPS Garmin GPS 19x HVS (NMEA 0183) Part #: 010-01010-00 Garmin GPS 19x HVS (NMEA 0183) Part #: 010-01010-00 Garmin GPS 19x HVS (NMEA 0183) Part #: 010-01010-00 Compass KVH C-100 Part #: 01-0177-15 KVH C-100 Part #: 01-0177-15 KVH C-100 Part #: 01-0177-15 Logger Campbell Scientific CR6-NA-XT-SW Part #: 28385-9 Campbell Scientific CR6-WIFI-XT-SW Part #: 28385-6 Campbell Scientific CR6-WIFI-XT-SW Part #: 28385-6 Data Collection and Real-Time Processing The reported measured quantities are summarized in the table below. Quantity Units Source Epoch time Seconds GPS Latitude and longitude Degrees GPS Altitude m GPS Pressure hPa PTB210 Temperature (fast) deg C 109SS-L Temperature (slow) deg C HMP155 RH (slow) % HMP155 Vehicle speed m/s GPS Vehicle heading deg C-100 and GPS In addition to the measured variables, several derived variables are calculated. Corrected/fast relative humidity (%) Relative humidity is adjusted to the fast temperature following Richardson et al. (1998) and Houston et al. (2016). Water vapor mixing ratio (g/kg) Dew point temperature (&deg;C) Potential temperature (Kelvin) Virtual potential temperature (Kelvin) Equivalent potential temperature (Kelvin) Regular intercomparisons between all three CoMeTs were performed during TORUS 2019. Comparisons were also conducted between CoMeT-1 and CoMeT-2 during LAPSE-RATE (2018) on 14 July. In these intercomparisons, the vehicles were parked adjacent to each other aligned perpendicular to (and facing into) the wind. To minimize engine heating effects, intercomparisons were only conducted when the wind speed was >10 kts. Data Format Original data files for each deployment are saved as text files and then converted to NetCDF. NetCDF versions have units that are CF compliant and may not match the original units in the txt files. The naming convention for the NetCDF files is as follows: UNL.CoMeT3.{deployment date YYYYMMDD}.{start time of observation collection in UTC HHMM}.L2.{post-processing codes}.cdf example: UNL.CoMeT3.20190627.1931.L2.g1.f1.cdf Post-processing codes are included to track modifications to the raw data. These codes are closely connected to error flags associated with each record. Each letter corresponds to a particular instrument: g: GPS p: Barometer tf: Fast temperature ts: Slow temperature rh: Relative humidity f: Compass w: Wind monitor a: All instruments Each number corresponds to a particular post-processing action described more below. Measured and derived variables are included in the following table. Variable Heading Standard Name Units time Time seconds since 00:00:00, 01-01-1970 Alt Altitude meters lat Latitude degrees north lon Longitude degrees east fast_temp Air Temperature kelvin slow_temp Air Temperature kelvin pressure Air Pressure pascals logger_RH Relative Humidity percent calc_corr_RH Relative Humidity percent wind_speed Wind Speed meters per second wind_dir Wind From Direction degrees vehicle_dir Vehicle Direction degrees dewpoint Dew Point Temperature kelvin mixing_ratio Humidity Mixing Ratio g/g theta Air Potential Temperature kelvin theta_v Virtual Potential Temperature kelvin theta_e Equivalent Potential Temperature Kelvin error_flag The error_flag variable is a string that matches the post-processing codes listed above. All instruments will have an associated code, but will have a &ldquo;0&rdquo; if the datum is unchanged from the initial processed value. Error Codes The following table summarizes the error codes for data collected before 2020: Error Code Relevant CoMeT Description g1 1,2,3 Exact correction. GPS position and time reprocessed from raw data g2 1 As far as we can tell this is an exact correction to an error in the GPS time. During the correct time periods the time suddenly went backwards ~250s and stayed at this offset for 750s when it corrected itself. The offset was applied to the &ldquo;time warp&rdquo; period. p1 2 Approximate correction. Hole in the pressure tube connecting the pressure port to the barometer. Resulted in erroneously low air pressure measurements when the vehicle was in motion. Derived variables recalculated (dew point temperature [e depends on qv and p], water vapor mixing ratio, potential temperature, virtual potential temperature, equivalent potential temperature) a1 3 Exact correction. Missing data reprocessed from raw data a2 1 Bug fix to bias correction for ts1, ts2, and rh1: water vapor mixing ratio was off by a factor of 10 and virtual potential temperature was wrong because of this. f1 3 No correction, missing data. Fluxgate compass inoperable. Wind speed and direction calculated using GPS-derived vehicle heading instead. rh1 1 Approximate correction. Constant bias of +1.7% removed from relative humidity. Derived variables recalculated (corrected/fast relative humidity, dew point temperature, water vapor mixing ratio, virtual potential temperature, equivalent potential temperature) ts1 1 Approximate correction. Constant bias of +0.6 K removed from slow temperature. Derived variables recalculated (corrected/fast relative humidity, dew point temperature, water vapor mixing ratio, virtual potential temperature, equivalent potential temperature) ts2 1 Approximate correction. Constant bias of +1.0 K removed. Derived variables recalculated (corrected/fast relative humidity, dew point temperature, water vapor mixing ratio, virtual potential temperature, equivalent potential temperature) References Bolton, D., 1980: The Computation of Equivalent Potential Temperature. Mon. Wea. Rev., 108, 1046&ndash;1053, https://doi.org/10.1175/1520-0493(1980)108<1046:TCOEPT>2.0.CO;2. Hanft, W., and A. L. Houston, 2018: An Observational and Modeling Study of Mesoscale Air Masses with High Theta-E. Mon. Wea. Rev., 146, 2503&ndash;2524, https://doi.org/10.1175/MWR-D-17-0389.1.Wexler Houston, A. L., R. J. Laurence III, T. W. Nichols, S. Waugh, B. Argrow, and C. L. Ziegler, 2016: Intercomparison of unmanned aircraft-borne and mobile mesonet atmospheric sensors. Journal of Atmospheric and Oceanic Technology. 33, 1569-1582, doi: 10.1175/JTECH-D-15-0178.1. Lowe, P. R., 1977: An Approximating Polynomial for the Computation of Saturation Vapor Pressure. J. Applied Meteorology, 16, 100&ndash;103. Richardson, S. J., S. E. Frederickson, F. V. Brock, and J. A. Brotzge, 1998: Combination temperature and relative humidity probes: Avoiding large air temperature errors and associated relative humidity errors. Preprints, 10th Symp. On Meteorological Observations and Instrumentation, Phoenix, AZ, Amer. Meteor. Soc., 278&ndash;283. Waugh, S., and S. E. Frederickson, 2010: An improved aspirated temperature system for mobile meteorological observations, especially in severe weather. 25th Conf. on Severe Local Storms, Denver, CO, Amer. Meteor. Soc., P5.2. [Available online at https://ams.confex.com/ams/25SLS/techprogram/paper_176205.htm.]

54 ENVIRONMENTAL SCIENCES↗

An investigation of the open-loop amplification of Reynolds number dependent processes by wave distortion

Analytical and experimental studies were initiated to determine if the response of a constant temperature hot wire anemometer to acoustic oscillations could serve as an analog to the response of the drop vaporization burning rate process to acoustic oscillations, and, perhaps, also as an analog to any Reynolds number dependent process. The motivation behind this study was a recent analytical study which showed that distorted acoustic oscillations could amplify the open-loop response of vaporization limited combustion. This type of amplification may be the cause of unstable combustion in liquid propellant rocket engines. The analytical results obtained for the constant temperature anemometer are similar in nature to those previously obtained for vaporization limited combustion and indicate that the response is dependent on the amount and type of distortion as well as other factors, such as sound pressure level, Mach number and hot wire temperature. Preliminary results indicate qualitative agreement between theory and experiment.

Purdy, K. R.↗

A comparison of a coaxial focused laser Doppler system in atmospheric measurements

Measurements of atmospheric velocities and turbulence with the laser Doppler system were obtained, and the results compared with cup anemometer and hot-wire measurements in the same wind field. The laser Doppler velocimeter (LDV) is described along with the test procedures. It was found that mean values determined from the LDV data are within 5% of other anemometer data for long time periods, and the LDV measures higher velocities.

Karaki, S.↗

Hot-wire coil probe for high-speed flows

Small-diameter-wire coil probes developed for use in a hypersonic helium tunnel are discussed. The springlike properties of the coil minimize strain-gauge effects, and allow to use a higher length-to-diameter ratio for a given flow. In addition, the coil is more rugged for sudden flow changes, and since it can be mounted straight across the support tips, there is less support interference in cross flows. In addition to measuring fluctuating quantities in a boundary layer, the probes were used with a constant temperature anemometer for measuring mean mass flow profiles, and with a constant current anemometer for measuring mean total temperature profiles.

Weinstein, L. M.↗

CO2 laser doppler systems for the measurement of atmospheric winds and turbulence

Two CO2 laser doppler systems developed by NASA and some results obtained with them are discussed. A continuous wave, monostatic system for short-range wind measurement is described, and direct comparisons between the data obtained with it and with a cup-anemometer/wind vane system and a hot-wire anemometer show excellent agreement between the systems. Improvements being made in three CW, CO2 laser doppler systems, including a filter bank for optimized signal processing and a versatile scanning system, are noted. A pulsed CO2 system for measuring clear air turbulence is described, and results of test performance on board a Convair 990 are presented. It is noted that while the system was able to measure air speed and turbulence, the range of its transmitter-atmosphere-receiver was lower than predicted, and a difference of about 20 to 30 dB existed between the actual and theoretical turbulence measurements. Factors that may account for this loss are listed.

Huffaker, R. M.↗