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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.

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At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
NASA NTRS2011

Sensitivity of the Mid-Winter Arctic Stratosphere to QBO Width in a Simplified Chemistry-Climate Model

In the stratosphere, equatorial winds continually alternate between easterly (westward) and westerly (eastward). This phenomenon is called the quasi-biennial oscillation (QBO). The average QBO cycle (Le. easterly to westerly to easterly) lasts approximately 27 months. Large-scale 'planetary' waves can only travel upward through the atmosphere when equatorial winds are westerly, and below a critical threshold. Thus, the amount of wave energy that reaches the middle atmosphere depends on the wind direction. When equatorial winds are easterly, wave energy is concentrated at higher latitudes, weakening the high-latitude eastward wind feature known as the 'polar jet' during the Northern Hemisphere winter season. Holton and Tan (1980) used atmospheric observations to show the dependence of the strength of the northern polar jet on the phase (easterly vs. westerly) of the QBO. This modeling study finds that the width of the quasi-biennial oscillation (QBO) varies from one cycle to the next, and that variation in QBO width may exert equal influence on the Arctic stratosphere as does the QBO wind direction. High latitude winds are weaker and ozone values are higher in a wide-QBO model simulation, as compared with a realistic simulation. This result implies that a relatively wider QBO acts like a preferential shift toward the easterly phase of the QBO.

Hurwitz, M. M.↗
DOE OSTI2025

Spectral distortions to momentum and scalar exchanges by non-turbulent motion and patchy landscape variability

Modifications to the spectra of turbulent velocity and scalars and co-spectra of vertical fluxes of momentum and scalars due to patchy landscape heterogeneity and non-stationarity are explored for a Mediterranean ecosystem. About 9 months of high frequency measurements of the three velocity components, water vapor concentration, carbon dioxide concentration, and air temperature were analyzed for different seasons (spring/summer) and prevalent wind directions (southeast/northwest). The two wind directions sampled a contrast of clumped and patchy landscape comprised of olive trees (southeast) and wall bounded flow disturbed by the presence of few upwind trees (northwest). The measured spectra and co-spectra were also compared to theoretical scaling forms from stationary, planar homogeneous flow, in the absence of subsidence as derived from the Kansas experiment. To assess the role of low frequency non-turbulent motion on the spectral and co-spectral content, a 5-min Fourier cutoff was introduced and the analysis was limited to near-neutral conditions where the boundary layer depth is shallow compared to its unstable counterpart. It was shown that the velocity statistics were not appreciably impacted by the low-frequency motion causing non-stationarity. Moreover, the turbulent scalar fluxes were also shown not to be significantly impacted by such low frequency motion. The scalar variances were impacted, especially the water vapor variance and its concomitant spectral shape. When the non-turbulent motion was filtered, the scalar spectra at low wavenumbers followed expectations from the so-called attached eddy hypothesis (i.e. exhibited a $k^{-1}_x$ scaling with $k_x$ defining the longitudinal wavenumber) applicable for near-neutral conditions. For momentum co-spectra, the canonical shapes from the Kansas experiment appear to describe well the measurements here and in both dominant directions and seasons with some adjustment to the integral time scales based on wind direction. For the scalar co-spectra, deviations from the Kansas experiment were prevalent. The most noticeable and surprising deviations were their slow decay with increased sampling frequency at inertial subrange scales. This slow decay was shown not to contribute appreciably to the overall scalar fluxes. At those fine scales, predictions from local isotropy were expected to hold. The scalar co-spectral deviations from local isotropy were then discussed using a simplified co-spectral budget model where scalar–scalar co-spectra naturally emerged and the interplay between landscape heterogeneity and a scale-dependent pressure-scalar de-correlation time was postulated. It is also envisaged that the findings here offer a preliminary template for analyzing eddy-covariance data in situations that deviate from ideal conditions, especially regarding low-frequency modulations of scalar spectra and vertical scalar flux co-spectra.

Canopy turbulence↗
NASA NTRS1985

Participation in the Mars Data Analysis Program: Analysis of cloud forms in Viking and Mariner 9 images

The first systematic account of the climate of Mars, based upon observations was produced. Cloud data were used to determine spatially and temporally varying near-surface wind direction, relative wind speed, static stability, and humidity conditions on a global scale. Existing models of meteorological processes were critically reexamined in light of the data, and more stringent constraints were set on global processes. Several discoveries were made, including the large extent and seasonal variability of the Mars equatorial Hadley cell, the failure of high latitude winds to reverse direction in early northern spring, the change in meridional wind component in southern midautum, and the almost constant cloud cover in the northern hemisphere, during spring and summer primarily by condensate clouds and in fall and winter by condensates and dust. The implications of these observations are discussed.

Gierasch, P.↗
DOE OSTI2026

Assessment of Consensus and Wake Steering Wind Farm Control for the American WAKE ExperimeNt (AWAKEN)

As part of the AmericanWAKE ExperimeNt (AWAKEN), a wind farm control experiment is being conducted at the King Plains wind plant in northern Oklahoma from May 2024 to summer 2025. Two types of wind farm control are being evaluated: 1) wake steering, in which upstream wind turbines are misaligned relative to the wind direction to deflect their wakes away from downstream turbines and increase total wind plant power, and 2) consensus yaw control, whereby each turbine's yaw position is controlled to track a "consensus" weighted average of the wind directions measured at neighboring turbines rather than the turbine's own nacelle wind direction measurement. By replacing the noisy wind direction measured by an individual turbine with the smoother, more slowly varying consensus wind direction, consensus yaw control is intended to reduce yaw activity and increase power capture by improving yaw alignment. To help balance the potential increase in yaw activity for the turbines implementing wake steering, they are also operated using consensus yaw control. In this presentation we highlight the impacts of consensus yaw control and wake steering on both energy production and yaw travel at the wind plant. Results show that the change in energy from wake steering is minor overall, but significant increases in energy are observed for closely spaced turbines. Further, larger increases in energy occur during low turbulence periods. The impact of consensus yaw control on energy production is currently inconclusive, with some energy gains measured for some turbines and losses measured for others. Lastly, consensus yaw control was found to reduce yaw travel significantly, even when combined with wake steering.

17 WIND ENERGY↗
DOE OSTI2020

Long-range Doppler lidar measurements of wind turbine wakes and their interaction with turbulent atmospheric boundary-layer flow at Perdigao 2017

As part of the Perdigão 2017 campaign, vertical RHI (range-height indicator) scans with long-range pulsed Doppler wind lidars were performed aligned with the main wind direction and a wind turbine (WT) located on a mountain ridge. The measurements are used to not only retrieve flow velocities, but also their variance and - by using the turbulent broadening of the Doppler spectrum - also turbulent kinetic energy (TKE) dissipation rate. The study shows that turbulence in the WT wake is dependent on the turbulence of the inflow, but also on atmospheric stability. In stable atmospheric conditions, wakes could be analyzed up to five rotor diameters downstream (D) and showed the maximum turbulence in the wake at 2-3 D, whereas in unstable conditions, the maximum was found at 2 D and the wake could not be detected further than 3 D. A clear dependency of wake turbulence enhancement on inflow turbulence intensity is found, which levels out to no further enhancement at turbulence intensities of 30%.

17 WIND ENERGY↗
DOE OSTI2021

Results from a wake-steering experiment at a commercial wind plant: investigating the wind speed dependence of wake-steering performance

Wake steering is a wind farm control strategy in which upstream wind turbines are misaligned with the wind to redirect their wakes away from downstream turbines, thereby increasing the net wind plant power production and reducing fatigue loads generated by wake turbulence. In this paper, we present results from a wake-steering experiment at a commercial wind plant involving two wind turbines spaced 3.7 rotor diameters apart. During the 3-month experiment period, we estimate that wake steering reduced wake losses by 5.6% for the wind direction sector investigated. After applying a long-term correction based on the site wind rose, the reduction in wake losses increases to 9.3%. As a function of wind speed, we find large energy improvements near cut-in wind speed, where wake steering can prevent the downstream wind turbine from shutting down. Yet for wind speeds between 6–8 m/s, we observe little change in performance with wake steering. However, wake steering was found to improve energy production significantly for below-rated wind speeds from 8–12 m/s. By measuring the relationship between yaw misalignment and power production using a nacelle lidar, we attribute much of the improvement in wake-steering performance at higher wind speeds to a significant reduction in the power loss of the upstream turbine as wind speed increases. Additionally, we find higher wind direction variability at lower wind speeds, which contributes to poor performance in the 6–8 m/s wind speed bin because of slow yaw controller dynamics. Further, we compare the measured performance of wake steering to predictions using the FLORIS (FLOw Redirection and Induction in Steady State) wind farm control tool coupled with a wind direction variability model. Although the achieved yaw offsets at the upstream wind turbine fall short of the intended yaw offsets, we find that they are predicted well by the wind direction variability model. When incorporating the expected yaw offsets, estimates of the energy improvement from wake steering using FLORIS closely match the experimental results.

17 WIND ENERGY↗
NASA NTRS1974

The power associated with density fluctuations and velocity fluctuations in the solar wind

Direct observations from Pioneer 6 of solar-wind-proton fluctuations have been used to obtain the power spectra associated with solar-wind-proton number density and velocity fluctuations in the frequency range of 0.001 to 0.01 Hz, extending previous analyses by an order of magnitude at the higher frequencies. The slopes of the power spectra associated with the density fluctuations and the velocity fluctuations are similar and are in agreement with the shape of the power spectra found at the lower frequencies. The power spectra indicate that the power-law density spectrum observed at lower frequencies extends to at least 0.01 Hz. This smooth variation in the spectrum at these frequencies is consistent with previous extrapolations of both spacecraft and interplanetary scintillation observations.

Intriligator, D. S.↗
NASA NTRS1989

Scatterometer azimuthal response and wind wave directionality

Azimuthal response of a scatterometer to radiation scattered by the sea surface was studied in a wind-wave tank. The variation of the normalized radar cross section with the azimuth angle is fitted by a three-term series. Results show that the upwind-downwind asymmetry decreases as the wind speed increases. The crosswind modulation depends on the wind velocity. The results show that the evolution of the long-wind-crosswind ratio evolves with wind speed in a manner similar to the evolution of the isotropy of short capillary-gravity waves. The maximum of the isotropy of the short wind waves is obtained for wind velocities close to 4 m/s. For the same value of the velocity, the variations of radar response between long-wind and crosswind directions is minimum. For lower or higher values of wind velocities the directional accuracy of the radar increases, since the wind-wave field tends to align in the wind direction.

Giovanangeli, J. P.↗
DOE OSTI2023

Evaluating wind profiles in a numerical weather prediction model with Doppler lidar

We use Doppler lidar wind profiles from six locations around the globe to evaluate the wind profile forecasts in the boundary layer generated by the operational global Integrated Forecast System (IFS) from the European Centre for Medium-range Weather Forecasts (ECMWF). The six locations selected cover a variety of surfaces with different characteristics (rural, marine, mountainous urban, and coastal urban). We first validated the Doppler lidar observations at four locations by comparison with co-located radiosonde profiles to ensure that the Doppler lidar observations were of sufficient quality. The two observation types agree well, with the mean absolute error (MAE) in wind speed almost always less than 1 m s –1 . Large deviations in the wind direction were usually only seen for low wind speeds and are due to the wind direction uncertainty increasing rapidly as the wind speed tends to zero. Time–height composites of the wind evaluation with 1 h resolution were generated, and evaluation of the model winds showed that the IFS model performs best over marine and coastal locations, where the mean absolute wind vector error was usually less than 3 m s –1 at all heights within the boundary layer. Larger errors were seen in locations where the surface was more complex, especially in the wind direction. For example, in Granada, which is near a high mountain range, the IFS model failed to capture a commonly occurring mountain breeze, which is highly dependent on the sub-grid-size terrain features that are not resolved by the model. The uncertainty in the wind forecasts increased with forecast lead time, but no increase in the bias was seen. At one location, we conditionally performed the wind evaluation based on the presence or absence of a low-level jet diagnosed from the Doppler lidar observations. The model was able to reproduce the presence of the low-level jet, but the wind speed maximum was about 2 m s –1 lower than observed. This is attributed to the effective vertical resolution of the model being too coarse to create the strong gradients in wind speed observed. Our results show that Doppler lidar is a suitable instrument for evaluating the boundary layer wind profiles in atmospheric models.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2001

Wind Advisory System

The present invention is a two-part wind advisory system comprising a ground station at an airfield and an airborne unit placed inside an aircraft. The ground station monitors wind conditions (wind speed, wind direction, and wind gust) at the airfield and transmits the wind conditions and an airfield ID to the airborne unit. The airborne unit identifies the airfield by comparing the received airfield ID with airfield IDs stored in a database. The airborne unit also calculates the headwind and crosswind for each runway in both directions at the airfield using the received wind conditions and runway information stored in the database. The airborne unit then determines a recommended runway for takeoff and landing operations of the aircraft based on th~ runway having the greatest headwind value and displays the airfield ID, wind conditions, and recommended runway to the pilot. Another embodiment of the present invention includes a wireless internet based airborne unit in which the airborne unit can receive the wind conditions from the ground station over the internet.

Curto, Paul A.↗
DOE OSTI2024

Modeling the effect of wind speed and direction shear on utility‐scale wind turbine power production

Abstract Wind speed and direction variations across the rotor affect power production. As utility‐scale turbines extend higher into the atmospheric boundary layer (ABL) with larger rotor diameters and hub heights, they increasingly encounter more complex wind speed and direction variations. We assess three models for power production that account for wind speed and direction shear. Two are based on actuator disc representations, and the third is a blade element representation. We also evaluate the predictions from a standard power curve model that has no knowledge of wind shear. The predictions from each model, driven by wind profile measurements from a profiling LiDAR, are compared to concurrent power measurements from an adjacent utility‐scale wind turbine. In the field measurements of the utility‐scale turbine, discrete combinations of speed and direction shear induce changes in power production of −19% to +34% relative to the turbine power curve for a given hub height wind speed. Positive speed shear generally corresponds to over‐performance and increasing magnitudes of direction shear to greater under‐performance, relative to the power curve. Overall, the blade element model produces both higher correlation and lower error relative to the other models, but its quantitative accuracy depends on induction and controller sub‐models. To further assess the influence of complex, non‐monotonic wind profiles, we also drive the models with best‐fit power law wind speed profiles and linear wind direction profiles. These idealized inputs produce qualitative and quantitative differences in power predictions from each model, demonstrating that time‐varying, non‐monotonic wind shear affects wind power production.

Energy & Fuels↗
DOE OSTI2022

Oceanographic Conditions. 2007 - 2040. North Slope Alaska.

Complete representations of oceanographic conditions require spatial and temporal information about the significant wave height (Hs), peak wave period (Tp), wind speed, wind direction, wave direction, water level, salinity, and temperature. This data develops location-independent typologies to reduce the number of boundary conditions needed to assess nearshore oceanographic environments in both a Historical (2007-2019) and Future (2020-2040) timespan along the Alaskan North Slope. Wave information for both time spans were generated from WaveWatch III, Delft3D-FLOW, and Delft3D-WAVE simulations forced by wind conditions from reanalysis data (e.g., ASRv2 and ERA5) for the historical simulations while projected conditions were obtained from downscaled GFDL-CM3 forced under RCP8.5 conditions. Salinity was generated from GOFS 3.1 for the years between 2008-2015 and skin temperature of the ocean was obtained from ASRv2 reanalysis data for the years between 2007-2016. To identify generalized oceanographic typologies, K-means clustering was applied to the energy-weighted joint-probability distribution of Hs and Tp at six sites along the North Slope of Alaska. Distributions of wave and wind direction, wind speed, and water level associated with locaiton-indepndent centroids were assigned single values to describe a reduced order, typological rendition of offshore oceanographic conditions. These final typologies and their constituent data are provided here and can be used to evaluate the change in ocean energy over the next two decades in response to climate change and provide insight into expected consequences such as coastal erosion and flooding. A full assessment of the findings and techniques developed can be found in: Eymold, W.K., Flanary, C., Erikson, L., Nederhoff, K., Chartrand, C.C., Jones, C., Kasper, J., and Bull, D.L. Typological Representation of the Offshore Oceanographic Environment along the Alaskan North Slope. Continental Shelf Research (2022). 10.1016/j.csr.2022.104795

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2009

Determining Surface Roughness in Urban Areas Using Lidar Data

An automated procedure has been developed to derive relevant factors, which can increase the ability to produce objective, repeatable methods for determining aerodynamic surface roughness. Aerodynamic surface roughness is used for many applications, like atmospheric dispersive models and wind-damage models. For this technique, existing lidar data was used that was originally collected for terrain analysis, and demonstrated that surface roughness values can be automatically derived, and then subsequently utilized in disaster-management and homeland security models. The developed lidar-processing algorithm effectively distinguishes buildings from trees and characterizes their size, density, orientation, and spacing (see figure); all of these variables are parameters that are required to calculate the estimated surface roughness for a specified area. By using this algorithm, aerodynamic surface roughness values in urban areas can then be extracted automatically. The user can also adjust the algorithm for local conditions and lidar characteristics, like summer/winter vegetation and dense/sparse lidar point spacing. Additionally, the user can also survey variations in surface roughness that occurs due to wind direction; for example, during a hurricane, when wind direction can change dramatically, this variable can be extremely significant. In its current state, the algorithm calculates an estimated surface roughness for a square kilometer area; techniques using the lidar data to calculate the surface roughness for a point, whereby only roughness elements that are upstream from the point of interest are used and the wind direction is a vital concern, are being investigated. This technological advancement will improve the reliability and accuracy of models that use and incorporate surface roughness.

Holland, Donald↗
DOE OSTI2024

Robust wind farm layout optimization

Wake interactions in wind farms cause losses in annual energy production (AEP) on the order of 10%. Wind farm designers optimize the layout of the farm to mitigate wake losses, especially in the dominant site-specific wind directions. As wind turbines and wind farms grow in scale, optimization becomes more complex. Offshore wind farms regularly comprise more than 100 wind turbines and are characterized by complex boundaries due to shipping lanes, neighboring wind farms, and other constraints. Layout optimization methods are broadly split between gradient-based and gradient-free approaches. Gradient-based approaches can converge quickly and perform well for smaller, academic problems but are often sensitive to initial conditions and tuning parameters and require expert knowledge to use. On the other hand, gradient-free approaches can be more robust to problem complexities. We present a robust layout optimization approach based on a random search algorithm. The algorithm is intended for those who are not optimization experts and has few tuning parameters that need specification to achieve satisfactory results. Unlike off-the-shelf methods, which use generally available, non-domain-specific optimization routines that accept as inputs an optimization function and constraint definitions, this approach takes advantage of the relative computational costs of the different evaluations by evaluating cheaper computations first (boundary and minimum distance constraints) and running expensive AEP evaluations only if all other checks pass. Moreover, an outer genetic algorithm allows multiple solutions to evolve in parallel, enabling rapid solution development on high-performance computers. We discuss the relative ease of selecting necessary tuning parameters and demonstrate the efficacy of the genetic random search on a complex layout problem consisting of placing 70 turbines in a nonconvex and unconnected boundary region.

17 WIND ENERGY↗
DOE OSTI2024

Sounding System / Raw Data

This dataset contains twice or four times daily radiosonde observations of the atmosphere from the quarterly LTER ship transects. Variables include atmospheric pressure, temperature, relative humidity, wind speed, wind direction, north wind speed, and east wind speed. Note that variables are a function of height.

17 WIND ENERGY↗
DOE OSTI2024

Sounding System / Raw Data

This dataset contains twice or four times daily radiosonde observations of the atmosphere at the Woods Hole weather station. Variables include atmospheric pressure, temperature, relative humidity, wind speed, wind direction, north wind speed, and east wind speed. Note that variables are a function of height.

17 WIND ENERGY↗
DOE OSTI2025

Site A1 - Cup Anemometer / Reviewed Data

This dataset contains cup anemometer data deployed on tethered balloon system at AWAKEN site A1. The cup data include time stamp, wind speed, wind direction, gust wind speed, vertical wind speed, roll, pitch, latitude, longitude, and altitude. Z01 refers to the first height position of the cup anemometers mounted on the tethered balloon.

17 WIND ENERGY↗