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Results for “Wind Direction”

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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Wind Fields in Category 1–3 Tropical Cyclones Are Not Fully Represented in Wind Turbine Design Standards

Abstract Offshore wind energy deployment in the U.S. is expected to increase in the years to come, with proposed wind farm sites located in regions with high risk for tropical cyclones. Yet, the wind turbine design criteria outlined by the International Electrotechnical Commission for extreme events may not account for wind field characteristics unique to tropical cyclones. To evaluate if current design standards capture the extreme conditions of these storms, we perform idealized large‐eddy simulations of three tropical cyclones (one Category 1, one Category 2, and one Category 3 storms) using the Weather Research and Forecasting model. Wind conditions near the eyewall of Cat‐1, Cat‐2, and Cat‐3 storms can exceed current design standards for offshore wind turbines. Winds at 90 m can be faster than outlined in the design criteria for Class I and Class T turbines for 50‐year recurrence periods. Wind speed shear across the turbine rotor layer is also larger than assumed in design specifications. Moreover, vertical variations in wind direction across the turbine rotor layer are large for tropical cyclones of all intensity levels, suggesting design standards should include veer, which can amplify loads in wind turbines. The existence of hurricane wind field characteristics that are not represented in design standards does not imply that damage or failure will certainly occur. Engineering safety factors incorporated in the design of the turbine's blades and structural components may prevent damage from occurring.

17 WIND ENERGY↗
DOE OSTI2025

Site A1 - Sonic Anemometer / Reviewed Data

This dataset contains sonic anemometer data deployed on tethered balloon system at AWAKEN site A1. The sonic data include time stamp, wind speed, wind direction, turbulence, and boom attitude and motion.

17 WIND ENERGY↗
DOE OSTI2025

COURAGE S7 TBS AIRBORNE SONIC WIND AND TURBULENCE DATA

These data were collected with airborne wind instrumentation booms onboard the TBS at CoURAGE S7 during February 2025. The data include 60 Hz wind speed, component wind speed, wind direction, TKE, TI, and altitude measurements.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Lidar Measurements and High-Resolution Mesoscale Modeling of Coastally Trapped Disturbances off the Coast of California

Coastally Trapped disturbances (CTDs) are shifts in wind direction from the pre-dominant direction to equatorward to poleward for a period of time. These CTDs occur during the warm season off the California coast and impact coastal weather conditions and planned offshore wind plants. This study assesses the characteristics of CTD events as observed by lidar and other offshore buoys, then evaluates the ability of modeling systems to capture the correct characteristics, leveraging model output from the High-Resolution Rapid Refresh (HRRR) operational modeling system and the NOW-23 (National Offshore Wind) model dataset. CTDs were analyzed for October 2020 and May through to October of 2021, identifying 18 unique CTD events, confirmed by a nearby National Data Buoy Center (NDBC) buoy. The HRRR model captured most of these events, but the NOW-23 model output contained only 12 events. Composites of the wind, temperature, and pressure perturbations pre-, during, and post-event demonstrated the diminishment in wind speed, particularly for the alongshore component. Although the NOW-23 model captured the alongshore wind component and pressure perturbations well, the cross-shore wind component and temperature perturbations varied substantially. When the turbulent kinetic energy deviation and wind shear was positive across all levels pre-event, the NOW-23 modeling system was less likely to capture the CTD event. In contrast, the events that were captured by the model tended to have negative wind shear aloft pre-event.

coastal meteorology↗
DOE OSTI2024

The Role of Cloud Radiative Effects in the Propagating Southern Annular Mode

Abstract The Southern Annular Mode (SAM) is the most dominant natural mode of variability in the mid‐latitudes of the Southern Hemisphere (SH). However, both the sign and magnitude of the feedbacks from the diabatic processes, especially those associated with clouds, onto the SAM remain elusive. By applying the cloud locking technique to the Energy Exascale Earth System Model (E3SM) atmosphere model, this study isolates the positive feedback from the cloud radiative effect (CRE) to the SAM. Feedback analysis based on a wave activity‐zonal momentum interaction framework corroborates this weak but positive feedback. While the magnitude of the CRE feedback appears to be secondary compared to the feedbacks from the dry and other diabatic processes, the indirect CRE effects through the interaction with other dynamical and thermodynamical processes appear to play as important a role as the direct CRE in the life cycle of the SAM. The cross‐EOF analysis further reveals the obstructive effect of the interactive CRE on the propagation mode of the SH zonal wind directly through the CRE wave source and/or indirectly through modulating other diabatic processes. As a result, the propagation mode becomes more persistent and the SAM it represents becomes more predictable when the interactive CRE is disabled by cloud locking. Future efforts on inter‐model comparisons of CRE‐denial experiments are important to build consensus on the dynamical feedback of CRE.

58 GEOSCIENCES↗
DOE OSTI2020

Empirical Validation of Multi-Zone HVAC System Model: Evaluation of Existing Infiltration Models used in Building Energy Simulation

Infiltration can have a significant impact on building loads. Studies have shown that infiltration can account for 15-40% of annual space conditioning needs in commercial buildings (Emmerich et al. 2019; Younes et al. 2012). The driving force of infiltration is the pressure difference across the building envelope caused by wind, the stack effect (known as buoyancy effect), and the operation of ventilation equipment. Wind pressure is governed by wind direction, speed, building shape, and other structures around the building. The stack effect is a function of the building height and air density differences of ambient air (Han, 2015). The effect of wind is dominant in low-rise residential buildings, and the stack effect is dominant in highrise buildings (ASHRAE 2017). In building energy simulation programs (e.g., EnergyPlus), various empirical infiltration models (e.g., the effective leakage area model, the flow coefficient model) are available to simulate infiltration rates. To help users in selecting a proper infiltration model for modeling of the two-story Flexible Research Platform (FRP), the team evaluates the existing infiltration models in EnergyPlus based on field measurements from the FRP. The blower door and tracer gas decay tests were performed in the FRP. The blower door test result was used to estimate input parameters required in the infiltration models. The actual infiltration rates were estimated with the tracer gas decay test results.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

ARM X-SAPR Velocity Azimuth Display

This dataset contains Velocity Azimuth Displays from the XSAPR radars in SGP. Velocity Azimith Displays provide estimates of the 2D wind direction with height from radar scans. For more information on how these Velocity Azimuth Displays are derived, see Michelson and Seaman (2000). Michelson, S.A. and N.L. Seaman, 2000: Assimilation of NEXRAD-VAD Winds in Summertime Meteorological Simulations over the Northeastern United States. J. Appl. Meteor., 39, 367–383, https://doi.org/10.1175/1520-0450(2000)039<0367:AONVWI>2.0.CO;2

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Lifetime fatigue response due to wake steering on a pair of utility-scale wind turbines

Quantifying the impacts on turbine loads during wind farm control is an important consideration in assessing power production benefits. Wake steering controls aim to improve total wind farm performance by coordinating the control actions of individual turbines, wherein an upstream turbine is intentionally yawed at an offset angle from the measured wind direction. Consequently, this redirects its wake for improved power production and potentially reduces fatigue loads of the downstream turbines. This paper studies the lifetime fatigue loads associated with wake steering by using utility-scale wind turbine experimental data to conduct an analysis on a pair of wind turbines. This study was part of a large field experiment in which a group of five GE 1.5-MW SLE CWE turbines were selected as targets for conducting wake steering research. A standard loads instrumentation package and data acquisition system were installed on two turbines within the cluster to measure turbine fundamental loads. The time-series databases were used to calculate loads statistics as well as short-term and lifetime damage equivalent loads. Fatigue calculations followed the guidance in Annex H of the International Electrotechnical Commission standard 61400-1, Edition 4. Lifetime fatigue calculation results are presented in this analysis; three methods of assessing lifetime fatigue were used to determine percent difference for blade root moments, main shaft moments, main shaft torque, and tower top torque. For all three fatigue treatments, some components’ lifetime fatigue increases for the controlled turbine; however, the downwind turbine experienced a reduction in lifetime fatigue and combined effect for the turbine pair results in a reduction of fatigue when wake steering controls are applied.

17 WIND ENERGY↗
DOE OSTI2021

Frequency and Characteristics of Inland Advecting Sea Breezes in the Southeast United States

Sea breezes have been observed to move inland over 100 km. These airmasses can be markedly different from regional airmasses, creating a shallow layer with differences in humidity, wind, temperature and aerosol characteristics. To understand their influence on boundary layer and cloud development on subsequent days, we identify their frequency and characteristics. We visually identified sea breeze fronts on radar passing over the Savannah River Site (SRS) between March and October during 2015–2019. The SRS is ~150 km from the nearest coastal location; therefore, our detection suggests further inland penetration. We also identified periods when sea breeze fronts may have passed but were not visually observed on radar due to the shallow sea breeze airmass remaining below the radar beam elevation that ranges between approximately 1–8 km depending on the beam angle and radar source (Columbia, SC or Charleston, SC). Near-surface atmospheric measurements indicate that the dew point temperature increases, the air temperature decreases, the variation in wind direction decreases and the aerosol size increases after sea breeze frontal passage. A synoptic classification procedure also identified that inland moving sea breezes are more commonly observed when the synoptic conditions include weak to moderate offshore winds with an average of 35 inland sea breezes occurring each year, focused primarily in the months of April, May and June.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Sensitivity analysis of numerical modeling input parameters on floating offshore wind turbine loads

Abstract. Floating wind turbines must withstand a unique and challenging set of loads from the wind and ocean environment. To de-risk development, accurate predictions of these loads are necessary. Uncertainty in modeling predictions leads to larger required safety factors, increasing project costs and the levelized cost of energy. Complex aero-hydro-elastic modeling tools use many input parameters to represent the wind, waves, current, aerodynamic loads, hydrodynamic loads, and structural properties. It is helpful to understand which of these parameters ultimately drives a design. In this work, an ultimate and fatigue-proxy load sensitivity analysis was performed with 35 different input parameters, using an elementary effects approach to identify the most influential parameters for a case study involving the National Renewable Energy Laboratory (NREL) 5 MW baseline wind turbine atop the OC4-DeepCwind semisubmersible during normal operation. The importance of each parameter was evaluated using 14 response quantities of interest across three operational wind speed conditions. The study concludes that turbulent wind velocity standard deviation is the parameter with the strongest sensitivity; this value is important not just for turbine loads, but also for the global system response. The system center of mass in the wind direction is found to have the highest impact on the system rotation and tower loads. The current velocity is found to be the most dominating parameter for the system global motion and consequently the mooring loads. All tested wind turbulence parameters in addition to the standard deviation are also found to be influential. Wave characteristics are influential for some fatigue-proxy loading but do not significantly impact the extreme ultimate loads in these operational load cases. The required number of random seeds for stochastic environmental conditions is considered to ensure that the sensitivities are due to the input parameters and not due to the seed. The required number of analysis points in the parameter space is identified so that the conclusions represent a global sensitivity. The results are specific to the platform, turbine, and choice of parameter ranges, but the demonstrated approach can be applied widely to guide focus in parameter uncertainty.

17 WIND ENERGY↗
DOE OSTI2021

Machine learning enables national assessment of wind plant controls with implications for land use

Summary As deployment of wind energy continues to expand, computationally efficient tools for predicting wind plant performance over a wide range of layout designs, technology innovations, and spatial locations are increasingly important for policy and investment decisions. We demonstrate two approaches to training a surrogate model to predict annual energy production (AEP) of parameterized wind plant layouts: one using a Gaussian process (GP) and the other using a fully convolutional neural network (FCNN). We leverage the powerful FCNN architecture by encoding wind plant design parameters and output response surface as an image. The FCNN produces more accurate results than the GP with mean absolute errors equivalent to 1% and 1.9% of plant rated power, respectively, although the GP performs well under limited training data and provides useful uncertainty information. We also evaluate a surrogate model for wake steering, enabling a nationwide assessment of the impact of plant control strategies and plant layout decisions. Across two million locations, we find that wake steering strategies boost AEP with relative gains upwards of 3%. Gains are most pronounced at sites without a dominant wind direction and where layout optimization is less fruitful. Additionally, we perform a nationwide sensitivity analysis showing that wake steering can mitigate wake losses from higher density plant layouts. Our results suggest that regions which have not been previously viable for wind deployment due to moderate wind resources are especially well enhanced by wake steering strategies that could help overcome land constraints and inflexible layout options, potentially identifying new deployment opportunities.

17 WIND ENERGY↗
DOE OSTI2022

Computational Methods to Characterize Panel Loading Conditions for Accelerated Testing

Panel cracking and degradation due to wind loading are known to have a detrimental effect on power output. Recreating these damaging conditions in a controlled experimental setting requires an understanding of the loads being generated at the panel surface as a function of both wind speed, wind direction, and panel orientation. To better understand these relationships, a computational fluid dynamics (CFD) simulation package was constructed using an open-source Python library for solving partial differential equations with the finite element method. This CFD package allows the simulated wind speed and panel orientation of a multi-panel array to be easily changed and provides the capability to measure the traction forces at discrete points along the sun-facing and ground-facing surface of an interior panel residing in the wake of one or more upstream panels. A parameter exploration was performed in which the panel angle was varied in even increments from -70 deg to 70 deg and the wind speed was varied from 2 to 30 m/s. During post processing, the measured traction along the panel surface was averaged spatially and interpreted as a time-varying signal, where further processing of this signal yielded the root mean square amplitude and a characteristic frequency associated with the loading. This study found that higher wind speeds are generally associated with increased amplitude of loading and that the panel orientation angle can significantly exacerbate or mitigate this loading. These outcomes are presented along with current work on higher-fidelity verification simulations and recommendations for performing accelerated experimental testing.

loading↗
DOE OSTI2021

Tonopah Test Range Air Monitoring: Meteorological, Radiological, and Wind Transported Particulate Observations (CY 2019)

In 1963, the U.S. Department of Energy (DOE) (formerly the Atomic Energy Commission [AEC]), implemented Operation Roller Coaster on the Tonopah Test Range (TTR) and an adjacent area of the Nevada Test and Training Range (NTTR) (formerly the Nellis Air Force Range). This operation resulted in radionuclide-contaminated soils at the Double Tracks site and Clean Slate I, II, and III sites. This report documents observations made during ongoing monitoring of radiological, meteorological, and dust conditions at stations installed adjacent to the Clean Slate sites and at the TTR Sandia National Laboratories (SNL) Range Operations Center (ROC). The main objectives of the monitoring effort are to support the DOE Environmental Management Nevada Program (EM NV) in the safe remediation of the environmental legacy of nuclear device development and testing by determining if wind blowing across the Clean Slate sites is transporting particles of radionuclide-contaminated soil beyond the physical and administrative boundaries of the sites, and providing information for designing long-term monitoring of the sites. The monitoring program in 2019 included five stations. Station 400, located within TTR Area 3 and near the ROC, monitors conditions near the local workforce center. Stations 401 and 403 are located on the northern and southeastern perimeter fence lines, respectively, of the Clean Slate III site. Stations 404 and 405 are along the northern and eastern boundary fence, respectively, at Clean Slate II. The stations are generally downwind of the contaminated areas during either northwesterly or southerly winds, which are the predominant wind directions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

WINDPROF: Merged Best-Estimate Wind Profile Data – Block Island (WFIP3 Campaign)

WINDPROF provides 10-minute wind and turbulence profiles that integrate a scanning Doppler lidar, a profiling lidar, a 915 MHz radar wind profiler, and a surface met station across Northeast U.S. coastal and offshore sites during the WFIP3 campaign. Variables include wind speed, wind direction, vertical velocity, turbulence intensity, and turbulent kinetic energy, each with per-instrument quality control and inter-instrument agreement validation. Profiles are mapped to a standardized height grid – a dedicated near-surface level at the 10 m surface-wind height, 20 m spacing to 100 m, and 30 m spacing above – and carry component and derived uncertainty estimates. Heights are reported above ground level; the site's ground elevation is stored separately. The Block Island dataset covers 1 February 2024 – 8 September 2025.

17 WIND ENERGY↗