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At least 1,405 records · Page 78
All sourcesNASA NTRSDOE OSTI
NASA NTRS1995

Theoretical Plasma Distribution Consistent With Ulysses Magnetic Field Observations in a High-Speed Solar Wind Tangential Discontinuity

The overall multi-layer structure of the magnetic field observed by Ulysses across a broad solar wind tangential discontinuity can be reproduced fairly well by means of a kinetic model. Such a simulation provides complementary information about the velocity distribution functions, which are not always known due to the low time resolution inherent in plasma measurements. The success of such a simulation proves that our kinetic model can be used as a realistic basis for further studies of the structure and stability of tangential discontinuities.

solar wind Ulysses plasma↗
NASA NTRS2008

Tropospheric Airborne Meteorological Data Reporting (TAMDAR) Overview

This paper is an overview of the Tropospheric Airborne Meteorological Data Reporting (TAMDAR) project, giving some history on the project, various applications of the atmospheric data, and future ideas and plans. As part of NASA's Aviation Safety and Security Program, the TAMDAR project developed a small low-cost sensor that collects useful meteorological data and makes them available in near real time to improve weather forecasts. This activity has been a joint effort with FAA, NOAA, universities, and industry. A tri-agency team collaborated by developing a concept of operations, determining the sensor specifications, and evaluating sensor performance as reported by Moosakhanian et. al. (2006). Under contract with Georgia Tech Research Institute, NASA worked with AirDat of Raleigh, NC to develop the sensor. The sensor is capable of measuring temperature, relative humidity, pressure, and icing. It can compute pressure altitude, indicated and true air speed, ice accretion rate, wind speed and direction, peak and average turbulence, and eddy dissipation rate. The overall development process, sensor capabilities, and performance based on ground and flight tests is reported by Daniels (2002), Daniels et. al. (2004) and by Tsoucalas et. al. (2006). An in-service evaluation of the sensor was performed called the Great Lakes Fleet Experiment (GLFE), first reported by Moninger et. al. (2004) and Mamrosh et. al. (2005). In this experiment, a Mesaba Airlines fleet was equipped to collect meteorological data over the Great Lakes region during normal revenue-producing flights.

Daniels, Taumi S.↗
NASA NTRS2014

Low-Speed Dynamic Wind Tunnel Test Analysis of a Generic 53 Degree Swept UCAV Configuration With Controls

Several static and dynamic forced-motion wind tunnel tests have been conducted on a generic unmanned combat air vehicle (UCAV) configuration with a 53deg swept leading edge. These tests are part of an international research effort to assess and advance the state-of-art of computational fluid dynamics (CFD) methods to predict the static and dynamic stability and control characteristics for this type of configuration. This paper describes the dynamic forced motion data collected from two different models of this UCAV configuration as well as analysis of the control surface deflections on the dynamic forces and moments.

Vicroy, Dan D.↗
NASA NTRS1945

High-Speed Wind-Tunnel Tests of Dive-Recovery Flaps of a 0.3-Scale Model of the P-47D Airplane

This report presents the results of tests of a 0.3-Scale Model of the Republic P-47D airplane equipped with several dive-recovery flaps. Results are presented showing the drag coefficient and pitching-moment coefficient produced by the various flaps. The results of computations made to predict the normal acceleration due to deflecting the flaps in a vertical dive, the lift coefficient of the tail required for balance, and the relative magnitudes of the various factors which contribute to the pitching-moment increment are also given.

William T Hamilton↗
NASA NTRSDate not supplied

Summary of the NASA Semi-Span High-Lift Common Research Model Wind Tunnel Test at the QinetiQ 5-Metre Low-Speed Facility

A summary and selected results from experimental testing of the NASA 10% scale semi-span High-Lift Common Research Model (CRM-HL) at the QinetiQ 5m (5mWT) pressurized low-speed wind tunnel are presented. There were many objectives of this test, the primary being to define a set of reference configurations for dissemination and use within the CRM-HL ecosystem, one in the landing configuration and one in the takeoff configuration. A database of force, moment, pressure and surface flow visualization data was collected to support several objectives: aerodynamic sensitivity to high-lift device positioning, Reynolds number effects on configuration selection, isolation of the effects of various components of the reference configuration e.g. landing gear, nacelle, horizontal tail, etc., and limited repeatability data for uncertainty quantification assessment. Some results were compared to limited data collected at the NASA Langley Research Center 14x22 wind tunnel to assess half-model effects and facility differences. Half-model effects were further studied with use of floor blowing to change boundary layer profile and assess effects on model aerodynamics. Flow visualization data were gathered with fluorescent mini-tufts and UV oil. Finally, test time was spend working on the development of new Particle Image Velocimetry (PIV) and Model Deformation Measurement (MDM) capabilities at the 5mWT facility.

Ashley Evans↗
NASA NTRSDate not supplied

Restoring the Dunes: Using Satellite Imagery to Help Restore Turtle Nesting Grounds In Puerto Rico After Hurricane Maria

The North coast of Puerto Rico sustained heavy damage from Hurricane Maria and other subsequent tropical storms. This damage included erosion of beaches and foredunes. The foredunes are a delicate ecological environment, which provides important nesting grounds for endangered turtles. Local ecologists have been working to restore these dunes with mixed results. Presented here is an evaluation of dune restoration sites, and strategies to improve the success of subsequent restoration efforts. This involves both historical satellite image analysis and field work to evaluate sediment migration pathways. Local ecologists use a technique called Biomimicry to restore dunes. This involves inserting sticks or plants into the ground, simulating the effects of natural vegetation to slow wind speeds and deposit wind-blown sand. Manually planted vegetation can follow to stabilize the trapped sand. While often successful, this technique occasionally fails to accumulate significant amounts of sand. Satellite imagery shows that one of the controlling factors is the orientation of the dune and beach to the prevailing wind. Dunes that are oriented parallel to the prevailing wind are relatively slower-growing due to sediment bypass. Satellite imagery also indicates that the fastest growing dunes are in close proximity to major sediment sources. This is complicated by large dams, which limit the amount of sediment delivered to the coast. Field observation shows that biomimicry matrices are occasionally located too close to the swash zone, where storms over-top the dunes, poisoning stabilizing vegetation with salt water. Another common observation was that many dunes were starting to form behind fallen trees, suggesting that a different type of biomimicry might be successful. Satellite and field observation suggest that strategies to protect nearshore environments and improve dune restoration biomimicry efforts include: 1) orient biomimicry matrices at a higher angle to prevailing winds. 2) Prioritize sites near river mouths, particularly those without upstream dams. 3) Test other orientations of biomimicry, such as horizontal boards to mimic fallen trees. 4) Improve sediment supply by preventing further dam development, and releasing trapped sediment.

Paul Bremner↗
NASA NTRS2002

8- by 6-Foot Supersonic Wind Tunnel Compressor Inspected

The NASA Glenn Research Center's 8- by 6-Foot Supersonic Wind Tunnel (8 6 SWT) is NASA's only transonic propulsion wind tunnel. The test section speed range is between Mach 0.25 and 2.0. The 9- by 15-Foot Low-Speed Wind Tunnel (9 15 LWST), which has a speed range from 0 to 175 mph, is housed in the return leg of the 8 6 SWT and uses the same compressor. The 8 6 SWT uses a large, seven-stage axial flow compressor to drive the air through the tunnel. The compressor is 17 ft in diameter and is rated at 1600 m3 (56,600 ft3) of air/sec. It is driven by three electric motors with a combined horsepower of 87,000. A close examination of this compressor was performed in 2001, the first time since February of 1966.

Krupar, Martin J.↗
DOE OSTI2021

wtDigiTwin (Wind Turbine Digital Twin) [SWR-21-15]

This wind turbine digital twin software (wtDigiTwin) provides a digital twin solution for wind turbine applications. The focus of wtDigiTwin is to estimate loads, motions and environmental conditions for an operating wind turbine. The program uses supervisory control and data acquisition (SCADA) measurements as inputs, together with a wind turbine model. The wind industry is currently challenged by the high cost of operation and maintenance. These costs could be mitigated if component failures are predicted, but such predictions are difficult unless the turbines are equipped with expensive measuring devices. The alternative is to use a digital twin such as wtDigiTwin to estimate the necessary signals. wtDigiTwin can perform online prediction of signals that are otherwise not measured, using a limited set of reliable measurements and a physics-based model. The predicted signals can be used in applications that have direct cost benefits: 1) real-time estimation of the fatigue consumption of key components of the wind turbine; 2) root cause analyses and failure detections ; 3) lifetime reassessments ; 4) improvements to follow-on designs. The current version provides examples to estimate wind speed, thrust, torque, tower-top position, and tower loads on an onshore wind turbine using the following measurements tower top acceleration, generator torque, pitch, and rotational speed. The model combines a linear state-space model, a wind speed estimator, and a Kalman filter algorithm that integrates measurements with the state model to perform state estimations. The state space model is obtained either using OpenFAST linearizations, or using the yams package provided with the software.

Branlard, Emmanuel↗
NASA NTRS1976

A low speed self streamlining wind tunnel

A two dimensional test section in a low speed wind tunnel is producing flow conditions free from wall interference. The test section has flexible top and bottom walls, and rigid sidewalls from which the models are mounted spanning the tunnel. All walls are unperforated, and the flexible walls are positioned by screw jacks. To eliminate wall interference, the wind tunnel itself supplies the information required in this streamlining process, when run with the model present. Measurements taken at the flexible walls are used by the tunnel computer to check wall contours. When the static pressure distribution in the test section along a contoured flexible wall matches that computed for an imaginary flow field passing over the outside of the same contour, the wall is a streamline in an infinite flow field and the test section flow is free from wall interference. A series of iterations brings the walls from straight to streamlines. Illustrative aerodynamic data is presented, taken on a bluff body and a lifting wing.

Goodyer, M. J.↗
NASA NTRS1978

Wind-tunnel results for an improved 21-percent-thick low-speed airfoil section

Low speed wind tunnel tests were conducted to evaluate the effects on performance of modifying a 23 percent thick low speed airfoil. The airfoil contour was altered to reduce the upper-surface adverse pressure gradient and hence reduce boundary layer separation. The chord Reynolds number varied from about 2,000,000 to 9,000,000.

Mcghee, R. J.↗
DOE OSTI2026

Wind and Temperature Consensus at Horn Point, HU-Beltsville, Piney Run (Maryland) in support of CoURAGE

The Maryland Department of the Environment (MDE) operates a ground-based atmospheric profiling network consisting of collocated radar wind profilers (RWP) and radio acoustic sounding systems (RASS) as part of its Ambient Air Monitoring Program. This network provides continuous observations of wind and temperature structure in the lower troposphere to support air quality forecasting, regulatory analysis, and atmospheric research. The network currently includes three fixed sites across Maryland: Horn Point (HP, lower eastern shore) [38.587525°,-76.141006°], Howard University-Beltsville (HUB, central Maryland) [39.055277°, -76.878632°], and Piney Run (PR, western Maryland) [39.705950°, -79.012000°] The network is designed to capture regional variability in atmospheric transport and boundary-layer processes. These systems measure vertical profiles of horizontal wind speed and direction using Doppler radar techniques, with observations typically spanning from ~100 m above ground level up to approximately 2.5–4 km. Measurements are derived from the Doppler shift of backscattered electromagnetic signals, enabling retrieval of wind vectors at multiple altitudes with high temporal resolution (e.g., 30-minute averages reported every 6 minutes). Each radar wind profiler is paired with a Radio Acoustic Sounding System (RASS) to provide profiles of virtual temperature in the lower atmosphere (~100–200 m AGL) by measuring the propagation speed of acoustic waves. Together, the RWP/RASS system yields a coupled data set of thermodynamic and kinematic atmospheric structure, including additional parameters such as vertical velocity, radial velocity, signal-to-noise ratio, and spectral width for advanced analysis. There are two types of files for each station: wind data (files with a "w" prefix) and virtual temperature RASS data (files with a "t" prefix). The wind data files are in the format wYYDDD.cns, where YY is the 2-digit year and DDD is the day of the year. The RASS virtual temperature data files are in the format tYYDDD.cns. Each record has the following header structure: Line 1 : Station Name RASS files Line 2 : RASS rev DeTect_2.0, WINDS files Line 2 : WINDS rev ATI 5.1 Line 3 : N latitude, W longitude, and site elevation (m) Line 4 : Date and begin time of consensus: yy mm dd hh mn ss plus # minutes to add to get UTC Line 5 : Consensus averaging time (minutes); number of beams; number of range gates Line 6 : Number of records required to make consensus (num) total number of records (tot) and the consensus window size (m/s) in the format: num:tot (window) RASS files Line 7 : no. of coded cells, no. of spec, pulse width (ns), and inter-pulse period (µs), WINDS files Line 7 : No. of coded cells, no. of spectra, pulse width (ns), and inter-pulse period (µs), each with a pair of values: first value is for oblique beams, second for vertical RASS files Line 8 : Full scale Doppler value (m/s) Delay to first gate (ns) Number of gates Spacing of gates (ns), WINDS files Line 8 : Full scale Doppler velocity (m/s), oblique and vertical Vertical correction applied to oblique beams? (0 = no, 1 = yes) Delay to first gate (ns), oblique and vertical Number of gates, oblique and vertical Spacing of gates (ns), oblique and vertical Line 9 : Azimuth and elevation (9s indicate vertical beam not used) RASS files Line 10, values : HT = Height above ground (km), T = Uncorrected virtual temperature consensus (deg C), Tc = Corrected virtual temperature consensus (deg C), W = Vertical wind consensus (9s indicate vertical beam not used, w-component, positive upward, m/s), CNT = Number of records that made consensus (for the 3 values in same order), SNR = Average signal to noise ratio (dB) of records in consensus (same order) WINDS files Line 10, values : HT = Height above ground (km), SPD = Wind speed (m/s), DIR = Wind direction (deg E of N from N), RAD = Radial velocities for each beam (m/s) in order given in azimuth and elevation line (positive toward radar; 9s indicate vertical beam not used, CNT = Number of records that made consensus, SNR = Average signal to noise ratio (dB) of records in consensus

{"wind speed and direction",temperature}↗
DOE OSTI2025

Quantifying Uncertainties in Modeling Wind Resource Data from Different PBL Schemes in the WRF Model: A Case Study Over the Puerto Rico Region

This study examines the modeling uncertainty of wind resource data stemming from the use of various planetary boundary layer (PBL) parameterizations available in the Weather Research and Forecasting (WRF) model. WRF-based wind simulations spanning 20 years at 3-km resolution using 11 different PBL schemes are used to objectively investigate the uncertainty in modeling wind speed for land-based wind (LBW) and offshore wind (OSW) locations in Puerto Rico. The uncertainty in the wind modeling for the 20-year dataset is quantified using the spread index (SI) and standard deviation (SD). For virtual LBW and OSW sites, the SI and SD values are analyzed as calculated across various spatial and temporal scales. Because the PBL's atmospheric stability conditions can be characterized into two dominant categories, the study focuses on analyzing the SI and SD for daytime (mainly unstable PBL conditions) and nighttime (mainly stable PBL conditions). For wind shear (10 m-200 m) at the OSW and LBW sites, WRF-based numerical experiments indicate the following SI (or SD) ranges: 39%-94% (0.74 m/s-1.44 m/s) during the daytime for OSW, 50%-75% (0.68 m/s-1.19 m/s) during the daytime for LBW, 37%-60% (0.73 m/s-1.12 m/s) during the nighttime for OSW, and 57%-143 % (0.65 m/s-1.43 m/s) during the nighttime for LBW. While a high SI is observed when modeling LBW during the nighttime, there are notable modeling uncertainties during the daytime on the leeward side of the orographic barriers for Puerto Rico.

17 WIND ENERGY↗
DOE OSTI2022

Towards multi-fidelity deep learning of wind turbine wakes

We report engineering wake models that accurately predict wake in a computationally efficient manner are very important for tasks such as layout optimization and control of wind farms. In this paper, we explore an application of deep learning (DL) to learn the wake model from hierarchies of physics-based approaches ranging from analytical models to an approximate form of the Reynolds-averaged Navier-Stokes equations. We first illustrate the application of principal component analysis to obtain a lower-dimensional representation that allows a computationally tractable training and deployment of DL models. Then, the DL model is trained to learn the mapping from input parameter space to the principal components, which are then used to reconstruct the three-dimensional flow field. Additionally, we investigate a composite framework consisting of two neural networks to learn the correlation between low- and high-fidelity data with Gauss and curl models treated as proxies for low- and high-fidelity models, respectively. The prediction from both DL models matches well with the high-fidelity data with a maximum relative percentage error for the kinetic energy flux of <1%. This work opens up possibilities for data-efficient construction of surrogate models for wake prediction that can be used to study the influence of wind speed and yaw angles on wind farm power production.

17 WIND ENERGY↗