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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS1986

The analysis sensitivity to tropical winds from the Global Weather Experiment

The global scale divergent and rotational flow components of the Global Weather Experiment (GWE) are diagnosed from three different analyses of the data. The rotational flow shows closer agreement between the analyses than does the divergent flow. Although the major outflow and inflow centers are similarly placed in all analyses, the global kinetic energy of the divergent wind varies by about a factor of 2 between different analyses while the global kinetic energy of the rotational wind varies by only about 10 percent between the analyses. A series of real data assimilation experiments has been performed with the GLA general circulation model using different amounts of tropical wind data during the First Special Observing Period of the Global Weather Experiment. In exeriment 1, all available tropical wind data were used; in the second experiment, tropical wind data were suppressed; while, in the third and fourth experiments, only tropical wind data with westerly and easterly components, respectively, were assimilated. The rotational wind appears to be more sensitive to the presence or absence of tropical wind data than the divergent wind. It appears that the model, given only extratropical observations, generates excessively strong upper tropospheric westerlies. These biases are sufficiently pronounced to amplify the globally integrated rotational flow kinetic energy by about 10 percent and the global divergent flow kinetic energy by about a factor of 2. Including only easterly wind data in the tropics is more effective in controlling the model error than including only westerly wind data. This conclusion is especially noteworthy because approximately twice as many upper tropospheric westerly winds were available in these cases as easterly winds.

Paegle, J.↗
NASA NTRS1992

The wavenumber spectra of scatterometer-derived winds

The accuracy of scatterometer-derived winds using a frequency-domain analysis and simulation is studied. The wavenumber spectra of the Seasat-A-scatterometer (SASS)-derived wind fields have been observed to be accentuated relative to the input wind field. The results of extensive simulations designed to test this observation are reported. Actual SASS measurements of the normalized radar backscatter (NRB) over an orbit (rev) are used as a template to generate simulated NRB measurements. Pointwise estimation of winds from the simulated NRB measurements is accomplished with the ambiguity closest to the true wind selected as the unique wind vector estimate. For comparison, winds are also estimated using a new model-based approach. After wind retrieval, the spectra of the estimated wind fields are computed and compared to the input wind field. The high wavenumber portion of the spectra of the pointwise estimated winds was higher than the spectra of the true winds by an amount which depends on the wind speed variance.

Long, D. G.↗
NASA NTRS1997

Comparison of TOPEX/Poseidon Sea Level and Linear Model Results forced by Various Wind Products for the Tropical Pacific

The goal of this paper is to compare TOPEX/Posaidon (T/P) sea level with sea level results from linear ocean model experiments forced by several different wind products for the tropical Pacific. During the period of this study (October 1992 - October 1995), available wind products include satellite winds from the ERS-1 scatterometer product of [HALP 97] and the passive microwave analysis of SSMI winds produced using the variational analysis method (VAM) of [ATLA 91]. In addition, atmospheric GCM winds from the NCEP reanalysis [KALN 96], ECMWF analysis [ECMW94], and the Goddard EOS-1 (GEOS-1) reanalysis experiment [SCHU 93] are available for comparison. The observed ship wind analysis of FSU [STRI 92] is also included in this study. The linear model of [CANE 84] is used as a transfer function to test the quality of each of these wind products for the tropical Pacific. The various wind products are judged by comparing the wind-forced model sea level results against the T/P sea level anomalies. Correlation and RMS difference maps show how well each wind product does in reproducing the T/P sea level signal. These results are summarized in a table showing area average correlations and RMS differences. The large-scale low-frequency temporal signal is reproduced by all of the wind products, However, significant differences exist in both amplitude and phase on regional scales. In general, the model results forced by satellite winds do a better job reproducing the T/P signal (i.e. have a higher average correlation and lower RMS difference) than the results forced by atmospheric model winds.

Hackert, Eric C.↗
NASA NTRS2000

A Probabilistic Technique for Improving the Mid-Swath Wind Direction Accuracy of Scanning Pencil Beam Scatterometers

The SeaWinds on QuikSCAT scatterometer is a device developed by NASA JPL to measure the speed and direction of ocean surface winds. Simulations performed in order to determine the expected performance of the instrument prior to its launch have indicated that the accuracy of the retrieved wind vectors varies across the swath. In particular the mid-swath accuracy is worse than that of the rest of the swath. This behavior is a general characteristic of scanning pencil beam scatterometers. For SeaWinds, the accuracy of the rest of the swath, and the size of the swath are such that the instrument meets its science requirements despite mid-swath shortcomings. However, by understanding the problem at mid-swath, we can improve the performance there as well, yielding even better coverage and accuracy than was originally intended. The mid-swath performance degradation is due to suboptimal viewing geometry. In particular mid-swath measurements contains measurements from two azimuths, roughly 180 degrees apart. With the standard wind retrieval technique, mid-swath root mean square (RMS) wind direction errors are approximately 30 degrees for low wind speeds (3 - 5.5 m/s) and 20 degrees for moderate to high wind speeds (5.5 - 30 m/s). We discuss the underlying causes of this phenomenon in detail and propose a modification to the wind retrieval technique in order to improve mid-swath performance. By estimating the range of likely wind directions for each measurement cell, one can optimally apply information from neighboring cells in order to reduce random wind direction errors without significantly degrading the resolution of the resultant wind field. In this manner we are able to achieve mid-swath RMS wind direction errors as low as 15 degrees for low winds and 10 degrees for moderate to high winds, while at the same time preserving high resolution structures such as cyclones and fronts. Since our results are based on simulated data, we also outline a procedure for making use of real data to optimize the technique after launch.

Stiles, Bryan↗
NASA NTRS2008

Simulation of Ground Winds Time Series for the NASA Crew Launch Vehicle (CLV)

Simulation of wind time series based on power spectrum density (PSD) and spectral coherence models for ground wind turbulence is described. The wind models, originally developed for the Shuttle program, are based on wind measurements at the NASA 150-m meteorological tower at Cape Canaveral, FL. The current application is for the design and/or protection of the CLV from wind effects during on-pad exposure during periods from as long as days prior to launch, to seconds or minutes just prior to launch and seconds after launch. The evaluation of vehicle response to wind will influence the design and operation of constraint systems for support of the on-pad vehicle. Longitudinal and lateral wind component time series are simulated at critical vehicle locations. The PSD model for wind turbulence is a function of mean wind speed, elevation and temporal frequency. Integration of the PSD equation over a selected frequency range yields the variance of the time series to be simulated. The square root of the PSD defines a low-pass filter that is applied to adjust the components of the Fast Fourier Transform (FFT) of Gaussian white noise. The first simulated time series near the top of the launch vehicle is the inverse transform of the adjusted FFT. Simulation of the wind component time series at the nearest adjacent location (and all other succeeding next nearest locations) is based on a model for the coherence between winds at two locations as a function of frequency and separation distance, where the adjacent locations are separated vertically and/or horizontally. The coherence function is used to calculate a coherence weighted FFT of the wind at the next nearest location, given the FFT of the simulated time series at the previous location and the essentially incoherent FFT of the wind at the selected location derived a priori from the PSD model. The simulated time series at each adjacent location is the inverse Fourier transform of the coherence weighted FFT. For a selected design case, the equations, the process and the simulated time series at multiple vehicle stations are presented.

Adelfang, Stanley I.↗
NASA NTRS2008

Statistical Short-Range Guidance for Peak Wind Speed Forecasts at Edwards Air Force Base, CA

The peak winds near the surface are an important forecast element for Space Shuttle landings. As defined in the Shuttle Flight Rules (FRs), there are peak wind thresholds that cannot be exceeded in order to ensure the safety of the shuttle during landing operations. The National Weather Service Spaceflight Meteorology Group (SMG) is responsible for weather forecasts for all shuttle landings. They indicate peak winds are a challenging parameter to forecast. To alleviate the difficulty in making such wind forecasts, the Applied Meteorology Unit (AMTJ) developed a personal computer based graphical user interface (GUI) for displaying peak wind climatology and probabilities of exceeding peak-wind thresholds for the Shuttle Landing Facility (SLF) at Kennedy Space Center. However, the shuttle must land at Edwards Air Force Base (EAFB) in southern California when weather conditions at Kennedy Space Center in Florida are not acceptable, so SMG forecasters requested that a similar tool be developed for EAFB. Marshall Space Flight Center (MSFC) personnel archived and performed quality control of 2-minute average and 10-minute peak wind speeds at each tower adjacent to the main runway at EAFB from 1997- 2004. They calculated wind climatologies and probabilities of average peak wind occurrence based on the average speed. The climatologies were calculated for each tower and month, and were stratified by hour, direction, and direction/hour. For the probabilities of peak wind occurrence, MSFC calculated empirical and modeled probabilities of meeting or exceeding specific 10-minute peak wind speeds using probability density functions. The AMU obtained and reformatted the data into Microsoft Excel PivotTables, which allows users to display different values with point-click-drag techniques. The GUT was then created from the PivotTables using Visual Basic for Applications code. The GUI is run through a macro within Microsoft Excel and allows forecasters to quickly display and interpret peak wind climatology and likelihoods in a fast-paced operational environment. A summary of how the peak wind climatologies and probabilities were created and an overview of the GUT will be presented.

Dreher, Joseph↗
NASA NTRS2013

Mars Technologies Spawn Durable Wind Turbines

Sometimes referred to as regenerative life support systems, the concept includes an enclosed self-sufficient habitat that can independently support life for years on end. Such a system aims not only to produce its own food and water but to purify air and convert waste into useful byproducts. In the early 1990s, NASA was planning for an extended stay on Mars, and Bubenheim and his Ames colleagues were concentrating efforts on creating a complete ecological system to sustain human crewmembers during their time on the Red Planet. The main barrier to developing such a system, he says, is energy. Mars has no power plants, and a regenerative system requires equipment that runs on electricity to do everything from regulating humidity in the atmosphere to monitoring the quality of recycled water. The Ames group started looking at how to best make power on a planet that is millions of miles away from Earth and turned to a hybrid concept combining wind and solar power technologies. The reason was that Mars experiences frequent dust storms that can block nearly all sunlight. When theres a dust storm and the wind is blowing, the wind system could be the dominant power source. When the wind is not blowing and the sun is out, photovoltaics could be the dominant source, says Bubenheim.To develop and test the wind power technology, Ames turned to a remote, harsh environment here on Earth: the South Pole. The South Pole was a really good analog for Mars, says Bubenheim. The technology features for going to Mars were the same technology features needed to make something work at the South Pole.Around the same time that NASA started investigating energy technologies for the Red Planet, the National Science Foundation (NSF) was working on a redesign of their station at the South Pole. To power its operations, NSF used fuel that it flew to the remote location, but the Foundation recognized the benefits of also using onsite renewable energy technologies. In the winter they have small crews and their power requirements are less, says Bubenheim. In the summers, they bring in larger groups and photovoltaics could supply a lot of power. Using renewable energy technology could be a way of reducing the amount of fuel they have to fly in.Technology TransferTo advance wind turbine technology to meet the requirements of extremely harsh environments like that on Mars, Ames partnered with NSF and the Department of Energy. It was clear that a lot of the same features were also desirable for the cold regions of the Earth, says Bubenheim. NASA took the leadership on the team because we had the longest-term technology a Mars turbine. Years before, NSF had worked with a company called Northern Power Systems (NPS), based in Barre, Vermont, to deploy a 3-kilowatt wind turbine on Black Island off the coast of Antarctica.Sometimes referred to as regenerative life support systems, the concept includes an enclosed self-sufficient habitat that can independently support life for years on end. Such a system aims not only to produce its own food and water but to purify air and convert waste into useful byproducts. In the early 1990s, NASA was planning for an extended stay on Mars, and Bubenheim and his Ames colleagues were concentrating efforts on creating a complete ecological system to sustain human crewmembers during their time on the Red Planet. The main barrier to developing such a system, he says, is energy. Mars has no power plants, and a regenerative system requires equipment that runs on electricity to do everything from regulating humidity in the atmosphere to monitoring the quality of recycled water. The Ames group started looking at how to best make power on a planet that is millions of miles away from Earth and turned to a hybrid concept combining wind and solar power technologies. The reason was that Mars experiences frequent dust storms that can block nearly all sunlight. When there's a dust storm and the wind is blowing, the wind system could be the dominant power source. When the wind is not blowing and the sun is out, photovoltaics could be the dominant source, says Bubenheim.To develop and test the wind power technology, Ames turned to a remote, harsh environment here on Earth: the South Pole. The South Pole was a really good analog for Mars, says Bubenheim. The technology features for going to Mars were the same technology features needed to make something work at the South Pole.Around the same time that NASA started investigating energy technologies for the Red Planet, the National Science Foundation (NSF) was working on a redesign of their station at the South Pole. To power its operations, NSF used fuel that it flew to the remote location, but the Foundation recognized the benefits of also using onsite renewable energy technologies. In the winter they have small crews and their power requirements are less, says Bubenheim. In the summers, they bring in larger groups and photovoltaics could supply a lot of power. Using renewable energy technology could be a way of reducing the amount of fuel they have to fly in.Technology Transfer To advance wind turbine technology to meet the requirements of extremely harsh environments like that on Mars, Ames partnered with NSF and the Department of Energy. It was clear that a lot of the same features were also desirable for the cold regions of the Earth, says Bubenheim. NASA took the leadership on the team because we had the longest-term technology a Mars turbine.

Mars Technologies↗
NASA NTRSDate not supplied

Lidar and UAS Measurements of Winds for the NASA Advanced Air Mobility Mission

The envisioned future of Advanced Air Mobility involves low-altitude operation of a new class of air vehicles. The low altitudes of the flight paths would have these vehicles spending most of their operation in conditions where unpredictable wind and turbulence effects may occur. Wind measurements will hence be critical to ensure safe and efficient operations for AAM. Such wind measurement could be used as a monitoring system for warning of hazardous wind events, as data input to forecasting models, or as a research tool to understand wind effects in complex environments. To meet this need NASA is evaluating wind sensing technologies with a capability to probe the atmospheric boundary layer. Doppler lidar is a leading candidate ground-based sensor, as shown by the decades-long history of being an effective tool for many wind studies. However, there is a need to re-assess the Doppler wind lidars for the AAM application which involves evaluating wind effects as vehicles operate in and out of vertiports, notably regarding spatial resolution. Meeting the needs for spatial resolution may involve the use of dual-Doppler techniques, rather than just single Doppler lidar. The study was furthermore motivated by looking toward the future of AAM, in which the air vehicles involved can also provide wind measurements. Airborne wind measurements, obtained directly from vehicle-mounted anemometers or indirectly from vehicle navigation data, offer a means to compare remotely sensed wind lidar with in-situ measurements. The following sections report on results of wind measurements obtained using lidar and the small uninhabited aircraft systems (sUAS’s) operating in the same volume of air.

Adam Medina↗
DOE OSTI2025

Urban heat islands can influence the wind energy resource during heatwaves

Urban wind energy is critical for sustainable electricity generation in cities. However, little research has explored how the urban heat island (UHI) effect influences wind energy, particularly in heatwaves when energy demand surges. In this study, we examine wind energy distribution in the Boston–Providence metropolitan area during heatwaves, using Weather Research and Forecasting (WRF) model integrated with Building Energy Parameterization/Building Energy Model (BEP/BEM). Two scenarios, a realistic case and a hypothetical case without urban warmth, were compared to isolate UHI impacts. Results reveal that UHI induces a "wind energy loss zone" in this urban area, reducing wind power density (WPD) by 20–30 W/m 2 at 50–100 m, while suburban/rural areas exhibit a "wind energy gain zone," with WPD increases up to 40 W/m 2 at 150–200 m. These losses diminish with distance from urban centers and become negligible beyond main urban and suburban sprawl. Heatwave expands the urban "loss zone", while amplifying wind energy gains in suburban/rural areas, driven by stronger thermal gradients and weakened background winds that intensify air convergence in urban and urban-rural circulations, thereby exacerbating urban wind energy losses by 15–20 %. An analysis of 235 wind farms using turbine power curves reveals that built areas dependent on stand-alone or off-grid turbines face significant energy deficits during a heatwave. Wind energy drops by up to 25 %, while cooling-related building energy demand rises 30–40 % during a heatwave. These findings underscore the need for strategic urban wind energy planning to ensure reliable power during extreme heat.

Energy - Wind↗
DOE OSTI2024

Characterizing model uncertainties in simulated coast-to-offshore wind over the northeast U.S. using multi-platform measurements from the TCAP field campaign

Numerical weather prediction (NWP) models, such as the Weather Research and Forecasting (WRF) model, are widely used to provide estimates of the offshore wind energy resource owing to their large spatial coverage compared to available observations. Nevertheless, spatiotemporal distribution of model biases is highly dependent on factors including model configuration, location, and the interplay of multi-scale physical processes. Here, in this study, we focus on the characterization of model uncertainties in simulated coast-to-offshore winds over the northeast U.S., by varying sea surface temperature (SST) forcings, surface layer (SL) and planetary boundary layer (PBL) parameterizations, as well as identifying biases that may be directly passed from initial and boundary conditions. Multiple measurements, including aircraft data collected during the U.S. Department of Energy's Two-Column Aerosol Project (TCAP) experiment, are used to constrain the model results and facilitate quantitative comparisons. Our analysis indicates while SST forcing has notable impacts on simulated air temperature and moisture within PBL, the modeled winds are in general more sensitive to the choices of SL and PBL physics than to SST. The model’s forcing data not only controls the vertical dependence of wind speed errors, but also alters regional variability in wind speed’s spatial correlation. Bias comparisons between ERA5 reanalysis and ensemble simulations revealed significant similarity, particularly in wind speed biases during winter, underscoring their dependency on initial and boundary conditions. Coastal and offshore near-surface wind speed biases tend to exhibit much higher similarity in winter than in summer due to the presence of much stronger and more persistent synoptic wind conditions. This study highlights the importance of accurate atmospheric forcing and parameterization choices in improving wind forecasts and suggests the potential for extrapolating coastal wind biases to offshore locations, aiding wind energy forecasting and informing the Wind Forecast Improvement Project-3 (WFIP3).

17 WIND ENERGY↗
DOE OSTI2025

Offshore lightning more active and intense in U.S. East Coast wind areas than in North Sea

Fast winds over open areas off the U.S. East Coast resulted in many offshore areas being opened for wind power deployment. Yet lightning is active in this same general area. Lightning damage has been noted as the leading cause of unplanned downtime of wind farms. Here we use lightning observations from 2020–2022 to quantify lightning activity—U.S. East Coast Wind Lease and Wind Planning areas received about 14-times the lightning strokes and about 18-times the energy transfer compared to the operational wind farms in the North Sea. A strong north-south U.S. gradient of lightning exists : low off the coast of Maine, growing higher off Chesapeake Bay, and high south of Virginia. Very high lightning activity also occurs about 250 km from the U.S. coastline, which is 50–100 km outside currently designated Wind Areas but likely relevant to planning floating wind farms further from shore. International lightning protection standards, such as IEC 614200-24, use lightning stroke density and turbine top-height to estimate damage to wind turbines, but it remains unclear if such standards could protect U.S. offshore wind turbines in active lightning areas where the turbine top-height can be 100 m taller than the next tallest object for tens to hundreds of kilometers. This analysis suggests U.S. East Coast offshore wind farms will be subjected to much higher lightning stroke and lightning energy densities than the North Sea counterparts. Preparing the turbine designs in anticipation of these more active U.S. lightning risks will help foster wind’s growing contribution to a reliable and resilient low-carbon energy future.

lightning↗
DOE OSTI2026

Investigation of onshore wind farm wake recovery with in situ aircraft measurements during AWAKEN

The share of wind power for electricity supply is increasing worldwide. This highly variable resource requires the improved prediction of power output for network stability. The interaction between wind farm wakes and the atmospheric boundary layer (ABL) introduces uncertainties in power production that warrant detailed investigation. The flow downwind of wind farms is characterized by a reduction in wind speed and an increase in turbulence, which both vary with atmospheric conditions. During the American WAKE experimeNt (AWAKEN), the Technische Universität Braunschweig conducted measurement flights with a research aircraft upwind and downwind of onshore wind farms in the southern Great Plains in Oklahoma in the USA. This study utilizes data from 20 flights conducted at approximately hub height in September 2023 to investigate the wind field variability downwind of the wind farms and vertical profiles to observe atmospheric stratification. The flights were aligned perpendicular to the main wind direction downwind of the King Plains and Armadillo Flats wind farms. Additionally, lidar data from both upwind and downwind ground-based measurement sites and sonic anemometer data were used for comprehensive analysis. Results indicate that under stable ABL conditions, the wake persists at greater downwind distances with a higher velocity deficit in the wake relative to the undisturbed flow compared to unstable stratification. In homogeneous terrain under stable conditions, wake recovery to 95 % occurs between a distance of 4.5 and 9 km downwind of the wind farm. In the semi-complex terrain characterized by shallow hills, slopes, and valleys, the wake exhibits a higher velocity deficit compared to homogeneous terrain, while in some cases the wake was amplified by the terrain resulting in higher velocity deficit 10 km downwind of the wind farm compared to the measurements closer to the wind farm. The turbulent kinetic energy (TKE) and “TKE difference” was found to be a valuable measure in understanding wakes in a semi-complex terrain, showing a clear wake recovery and formation depending on the stratification of the ABL.

17 WIND ENERGY↗
DOE OSTI2024

Capacity Density Considerations for Floating Offshore Wind Farms in Ultradeep Waters

Capacity density describes the concentration of wind energy development in an area and is often specified in terms of megawatts-per-square-kilometer (MW/km2). Understanding capacity density trends in wind energy projects helps to inform both energy system and spatial planning efforts. Borrman et al. (2018) and Mulas Hernando et al. (2023) analyze capacity density trends for fixed-bottom offshore wind farms in Europe and the United States, respectively, and Cooperman et al. (2022) explores how floating offshore wind mooring technology choices may impact wind plant layout through setbacks from lease area boundaries in waters up to 1,300 m deep. Technical challenges facing floating offshore wind development in ultradeep waters (beyond 1,300 m) could impact achievable capacity densities, with potential implications to marine spatial planning and project economics. When compared to fixed-bottom commercial-scale wind farms, mooring system footprints from floating offshore wind systems can constrain capacity density in some circumstances. In this study, we conduct an initial investigation of how taut mooring configurations may constrain floating offshore wind turbine placement and estimate capacity density for representative floating wind plants in generic lease areas. In addition, we explore floating wind plant capacity density drivers in ultradeep waters by characterizing area utilization for a range of lease area characteristics. This analysis highlights the primary challenges that floating offshore wind systems may encounter in achieving capacity densities comparable to commercial-scale fixed-bottom projects at ultradeep water depths, from a technical standpoint.

capacity density↗
DOE OSTI2025

Future Wind Energy Resources and Cost Uncertainties Across the United States

This dataset contains results estimating projections of change of annual capacity factors and levelized cost of energy for several turbine technologies in the 2024 Annual Technology Baseline (ATB). Projections of change are based on downscaled earth system model (ESM) data from Sup3rCC. There has been evidence of reductions in average wind speeds over land in North America since the 1980s, and several models project that average wind speeds will continue to decrease. Concurrently, the cost of wind energy systems in the United States has been decreasing since around 2010, a trend also projected to continue. There is considerable uncertainty in these future projections, with quantitative estimates of future wind resource and system costs varying widely. To study this, we run land-based wind energy models with a range of possible future system costs, turbine designs, and meteorological inputs from multiple downscaled earth system models over the contiguous United States to estimate critical system performance metrics such as annual energy production (AEP) and levelized cost of energy. Where multiple earth system models agree, changes in mean AEP from the time period 2000-2019 to 2040-2059 can be as high as +10% in South Texas or as low as -20% in Iowa. Several additional states in the Midwest that currently have considerable wind generation capacity show the possibility of substantial decreases in AEP by mid-century. Larger turbines and moderate reductions in system costs can offset even the largest projected decreases in wind resource, but much uncertainty remains in the extent to which wind resources will actually change into the future and to what extent wind energy systems can drive down future costs. An analysis of variance shows, in several states in the Midwest, the uncertainty in future wind resource can be almost as important for future changes in the cost of wind energy as the uncertainty in future system costs.

17 WIND ENERGY↗
DOE OSTI2026

FOCAL Campaign I: Advanced Wind Turbine Control Strategies

Campaign I of the Floating Offshore-wind Controls Advanced Laboratory Experimental Program (FOCAL) aims to generate a dataset enabling the validation of aerodynamic performance of a scaled turbine mounted on a rigid tower in a fixed condition. The turbine considered in the FOCAL testing campaigns is the IEA-Wind 15MW Reference Wind Turbine. This scaled model is capable of simulating advanced blade-pitch control strategies in a high-quality wind field. The turbine is fully instrumented to record a variety of parameters in real time such as structural loads and dynamics. The test data considered was generated at the University of Maine's Harold Alfond Wind and Wave (W2) testing facility. The Load Cases (LC) considered in this testing campaign are as follows: LC 1.X - Constant wind and blade-pitch with varying rotor speeds LC 2.X - Constant wind and rotor speed with varying blade-pitch LC 3.X - Varying wind with active closed-loop control Detailed properties on the modeled system are found in the following reference: Lenfest E., Floating Offshore-wind Controls Advanced Laboratory (FOCAL) Experimental Program - Campaign I: 1:70 Model-scale Testing of the IEA-Wind 15MW Reference Turbine. UMaine ASCC Report Number 23-40-1183. Details on the results of the verification and validation are found in the following reference: Mendoza, Nicole et al., "Verification and Validation of Model-Scale Turbine Performance and Control for the IEA Wind 15 MW Reference Wind Turbine," Energies, vol. 15, no. 20, 2022, https://doi.org/10.3390/en15207649.

17 WIND ENERGY↗
DOE OSTI2024

Power Production, Inter- and Intra-Array Wake Losses from the U.S. East Coast Offshore Wind Energy Lease Areas

There is an urgent need to develop accurate predictions of power production, wake losses and array–array interactions from multi-GW offshore wind farms in order to enable developments that maximize power benefits, minimize levelized cost of energy and reduce investment uncertainty. New, climatologically representative simulations with the Weather Research and Forecasting (WRF) model are presented and analyzed to address these research needs with a specific focus on offshore wind energy lease areas along the U.S. east coast. These, uniquely detailed, simulations are designed to quantify important sources of wake-loss projection uncertainty. They sample across different wind turbine deployment scenarios and thus span the range of plausible installed capacity densities (ICDs) and also include two wind farm parameterizations (WFPs; Fitch and explicit wake parameterization (EWP)) and consider the precise WRF model release used. System-wide mean capacity factors for ICDs of 3.5 to 6.0 MWkm−2 range from 39 to 45% based on output from Fitch and 50 to 55% from EWP. Wake losses are 27–37% (Fitch) and 11–19% (EWP). The discrepancy in CF and wake losses from the two WFPs derives from two linked effects. First, EWP generates a weaker ‘deep array effect’ within the largest wind farm cluster (area of 3675 km2), though both parameterizations indicate substantial within-array wake losses. If 15 MW wind turbines are deployed at an ICD of 6 MWkm−2 the most heavily waked wind turbines generate an average of only 32–35% of the power of those that experience the freestream (undisturbed) flow. Nevertheless, there is no evidence for saturation of the resource. The wind power density (electrical power generation per unit of surface area) increases with ICD and lies between 2 and 3 Wm−2. Second, EWP also systematically generates smaller whole wind farm wakes. Sampling across all offshore wind energy lease areas and the range of ICD considered, the whole wind farm wake extent for a velocity deficit of 5% is 1.18 to 1.38 times larger in simulations with Fitch. Over three-quarters of the variability in normalized wake extents is attributable to variations in freestream wind speeds, turbulent kinetic energy and boundary layer depth. These dependencies on meteorological parameters allow for the development of computationally efficient emulators of wake extents from Fitch and EWP.

17 WIND ENERGY↗
DOE OSTI2025

Linking large-scale weather patterns to observed and modeled turbine hub-height winds offshore of the US West Coast

The US West Coast holds great potential for wind power generation, although its potential varies due to the complex coastal climate. Characterizing and modeling turbine hub-height winds under different weather conditions are vital for wind resource assessment and management. This study uses a two-stage machine learning algorithm to identify five large-scale meteorological patterns (LSMPs): post-trough, post-ridge, pre-ridge, pre-trough, and California high. The LSMPs are linked to offshore wind patterns, specifically at lidar buoy locations within lease areas for future wind farm development off Humboldt and Morro Bay. While each LSMP is associated with characteristic large-scale atmospheric conditions and corresponding differences in wind direction, diurnal variation, and jet features at the two lidar sites, substantial variability in wind speeds can still occur within each LSMP. Wind speeds at Humboldt increase during the post-trough, pre-ridge, and California-high LSMPs and decrease during the remaining LSMPs. Morro Bay has smaller responses in mean speeds, showing increased wind speed during the post-trough and California-high LSMPs. Besides the LSMPs, local factors, including the land–sea thermal contrast and topography, also modify mean winds and diurnal variation. The High-Resolution Rapid Refresh model analysis does a good job of capturing the mean and variation at Humboldt but produces large biases at Morro Bay, particularly during the pre-ridge and California-high LSMPs. The findings are anticipated to guide the selection of cases for studying the influence of specific large-scale and local factors on California offshore winds and to contribute to refining numerical weather prediction models, thereby enhancing the efficiency and reliability of offshore wind energy production.

17 WIND ENERGY↗
DOE OSTI2026

Emerging mobile lidar technology to study boundary layer winds influenced by operating turbines

The development of a microjoule-class pulsed Doppler lidar and deployment of this compact system on mobile platforms such as aircraft, ships, or trucks have opened a new opportunity to characterize the dynamics of complex mesoscale wind flows. The PickUp-based Mobile Atmospheric Sounder (PUMAS) truck-based lidar system was recently used during the American Wake Experiment (AWAKEN) to assess the general structure of boundary layer (BL) wind and turbulence around wind turbines in central Oklahoma. Wind speed profiles averaged over PUMAS transects influenced by the operating turbines (waked flow) show a 1–2 m s −1 reduction compared to mean undisturbed (free flow) wind speed profiles. Spatial variability in wind speed was observed in time–height cross-sections at different distances from turbines. The wind speeds were about 9–12 m s −1 at 6 km distance compared to 5–7 m s −1 at the transects near the turbines. The PUMAS dataset from AWAKEN demonstrated the capability of the mobile Doppler lidar system to document spatial variability in wind flows at different distances from wind turbines and obtain quantitative estimates of wind speed reduction in the waked flow. The high-frequency, simultaneous measurements of the horizontal and vertical winds provide a new approach for characterizing dynamic processes critical for wind farm wake analyses.

17 WIND ENERGY↗