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At least 451 records · Page 25
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Benchmarking of three DWM-based wake models at below-rated wind speeds

Wind turbine wake models are essential tools for predicting power losses and structural loads in wind farms. Among these, the dynamic wake meandering (DWM) model, included as a recommended approach in the International Electrotechnical Commission design standard, is a widely used engineering-fidelity method that balances accuracy and computational cost. This study compares the performance of three DWM-based wake model implementations (from the Technical University of Denmark, the National Renewable Energy Laboratory, and the Institute for Energy Technology) under below-rated wind speed conditions. Model predictions of wake flow, power output, and structural loads for a four-turbine row are evaluated across different ambient turbulence levels and wind-direction misalignments and compared against high-fidelity large-eddy simulation results. All three models captured the overall wake evolution and mean turbine performance with reasonable accuracy; their predicted time-averaged thrust and power were typically within 5 %–10 % of the large-eddy simulation benchmark. However, notable differences emerged in wake structure and unsteady load predictions, with discrepancies increasing for turbines further downstream. These differences highlight the importance of modelling choices such as wake summation and turbulence treatment, which strongly influence power-deficit and fatigue-load predictions. Comparison with large-eddy simulations reveals each approach's strengths and weaknesses, indicating where improvements are needed. Overall, the findings point to specific refinements for DWM models to improve their fidelity, ultimately enabling more robust wake predictions for wind farm design and operation.

17 WIND ENERGY↗
DOE OSTI2019

Sensitivity analysis of the effect of wind characteristics and turbine properties on wind turbine loads

Proper wind turbine design relies on the ability to accurately predict ultimate and fatigue loads of turbines. The load analysis process requires precise knowledge of the expected wind-inflow conditions as well as turbine structural and aerodynamic properties. However, uncertainty in most parameters is inevitable. It is therefore important to understand the impact such uncertainties have on the resulting loads. The goal of this work is to assess which input parameters have the greatest influence on turbine power, fatigue loads, and ultimate loads during normal turbine operation. An elementary effects sensitivity analysis is performed to identify the most sensitive parameters. Separate case studies are performed on (1) wind-inflow conditions and (2) turbine structural and aerodynamic properties, both cases using the National Renewable Energy Laboratory 5 MW baseline wind turbine. The Veers model was used to generate synthetic International Electrotechnical Commission (IEC) Kaimal turbulence spectrum inflow. The focus is on individual parameter sensitivity, though interactions between parameters are considered. The results of this work show that for wind-inflow conditions, turbulence in the primary wind direction and shear are the most sensitive parameters for turbine loads, which is expected. Secondary parameters of importance are identified as veer, u -direction integral length, and u components of the IEC coherence model, as well as the exponent. For the turbine properties, the most sensitive parameters are yaw misalignment and outboard lift coefficient distribution; secondary parameters of importance are inboard lift distribution, blade-twist distribution, and blade mass imbalance. This information can be used to help establish uncertainty bars around the predictions of engineering models during validation efforts, and provide insight to probabilistic design methods and site-suitability analyses.

17 WIND ENERGY↗
DOE OSTI2026

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

WINDPROF provides 10-minute wind and turbulence profiles that integrate scanning Doppler lidars, a profiling lidar, a 915 MHz radar wind profiler, and a sonic anemometer 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 sonic measurement height (5 m AGL), 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 Nantucket dataset covers 1 February 2024 – 8 September 2025.

17 WIND ENERGY↗
DOE OSTI2026

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

WINDPROF provides 10-minute wind and turbulence profiles that integrate a scanning Doppler lidar, a continuous-wave profiling lidar, and a sonic anemometer 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 sonic measurement height (4 m AGL), 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 Rhode Island dataset covers 7 February 2024 – 4 September 2025.

17 WIND ENERGY↗
DOE OSTI2020

The effect of dynamic near-wake modulation on utility-scale wind turbine wake development

High-resolution field-scale experiments using flow visualization with natural snowfall and high-fidelity large eddy simulations are combined to investigate the effect of dynamic turbine operation and atmospheric conditions on wind turbine wake mixing and recovery in the wake of a 2.5 MW wind turbine. Instantaneous near-wake expansion and deflection in response to changes in blade pitch and wind direction, termed dynamic wake modulation, is quantified using both techniques, demonstrating excellent agreement. The simulations are used to extend these results by calculating the energy flux into the wake 7 rotor diameters downstream, showing that dynamic turbine-atmospheric interactions enhance mixing in the far-wake. This finding is exhibited under both uniform and turbulent inflow conditions. Under turbulent flow, a synergistic relationship is also observed between dynamic wake modulation and wake meandering, as wake recovery can be further accelerated when the two phenomena occur together. The results of this study have implications for the development of more realistic far-wake models that include the significant impact of dynamic wake modulation on wake mixing and development. Additionally, the findings from the current study can be used to develop advanced control algorithms to speed up wake breakdown and recovery, further improving wind farm efficiency.

17 WIND ENERGY↗
DOE OSTI2020

Modelling and assessing the near-wake representation and turbulence behaviour of control-oriented wake models

Due to the interaction between the wake of an upstream turbine on a downstream turbine, power losses and increased fatigue loads occur. By yawing the upstream turbine with regard to the wind direction, one can potentially reduce the power losses of the downstream turbine and reduce the fatigue loads. The evolution of the wake depends on the pressure gradient within the near-wake region and the turbulent kinetic energy and must be incorporated in existing wake steering algorithms to provide an accurate estimation of the wake flow. This paper will show a first approach to implement a near-wake model and a turbulence model in the curled wake model within the controls-oriented framework FLORIS. The near-wake model is based on an analytical expression of the velocity profile to model the pressure gradient. Furthermore, two turbulence models are incorporated within the curled wake model based on a Gaussian-distribution and a mixing length formulation. The adapted curled wake model is then assessed with the measurement data acquired in the wind tunnel at ForWind – University of Oldenburg. The evaluation of the models show good agreement for the velocity deficit and representation of the near-wake region. Furthermore, the turbulent kinetic energy behaved as expected in comparison to other work, showing a ring of high turbulent kinetic energy at non-yawed condition which is deflected to a curled shape at large yaw angles with the turbulence model based on a mixing length formulation.

FLORIS↗
DOE OSTI2026

WINDPROF: Merged Best-Estimate Wind Profile Data – Cape Cod (WFIP3 Campaign)

WINDPROF provides 10-minute wind and turbulence profiles that integrate a scanning Doppler lidar, a profiling Doppler lidar, and two sonic anemometers 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 – dedicated near-surface levels at the sonic measurement heights (4 m and 10 m AGL), 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 Cape Cod dataset covers 1 February 2024 – 6 September 2025.

17 WIND ENERGY↗
NASA NTRS1999

Multi-Scale Interactions Associated with the Monsoon Onset Over South China Sea and Adjacent Regions during SCSMEX-98

Using data collected during The South China Sea Monsoon Experiment (SCSMEX) (1998) as well as from the TRMM Microwave-Imager (TMI) and precipitation radar (PR), we have studied the multi-scale interactions (meso-synoptic-intraseasonal) associated with monsoon onset over South China Sea (SCS) and its subsequent evolution. Results show that the monsoon onset (defined by development of steady wind direction and heavy precipitation) over the northern SCS occurred around May 15 -17. Prevailing southerlies and southwesterlies developed over the central SCS after May 20. Shortly after, monsoon convection developed over the whole SCS region around May 23-27. The entire onset process appeared to be delayed by about a week to 10 days compared with climatology. During late spring of 1998, mid-latitude frontal systems were particularly active. These systems strongly impacted the northern SCS convection and may have been instrumental in triggering the onset of the SCS monsoon. The Tropical Oceans and Global Atmosphere (TOGA) and Bureau of Meteorology Research Centre (BMRC) radar showed a wide variety of convective systems over the Intensive Flux Array, from frontal bands to shear-banded structure, deep convection, pop-corn type shallow convection, slow moving "fine lines" to water spout. Analysis of SSM/I wind and moisture data suggested that the delayed convective activity over the SCS may be linked to the weakened northward propagation of monsoon rain band, hence contributing to a persistence of the rainband south of the Yangtze River and the disastrous flood that occurred over this region during mid to late June, 1998.

Lau, William K. M.↗
NASA NTRS2019

Acoustic Phased Array Quantification of Quiet Technology Demonstrator 3 Advanced Inlet Liner Noise Component

Acoustic phased array flyover noise measurements were acquired as part of the Boeing 737 MAX-7 NASA Advanced Inlet Liner segment of the Quiet Technology Demonstrator 3 (QTD3) flight test program. This paper reports on the processes used for separating and quantifying the engine inlet, exhaust and airframe noise source components and provides sample phased array-based comparisons of the component noise source levels associated with the inlet liner treatment configurations. Full scale flyover noise testing of NASA advanced inlet liners was conducted as part of the Quiet Technology Demonstrator 3 flight test program in July and August of 2018. Details on the inlet designs and testing are provided in the companion paper of Reference 1. The present paper provides supplemental details relating to the acoustic phased array portion of the analyses provided in Ref. 1. In brief, the test article was a Boeing 737MAX-7 aircraft with a modified right hand (starboard side) engine inlet, which consisted of either a production inlet liner, a NASA designed inlet liner or a simulated hard wall configuration (accomplished by applying speed tape over the inlet acoustic treatment areas). In all three configurations, the engine forward fan case acoustic panel was replaced with a unperforated (hardwall) panel. No other modifications to any other acoustic treatment areas were made. The left hand (port side) engine was a production engine and was flown at idle thrust for all measurements in order to isolate the effects of the inlet liners to the right hand engine. As described in Ref. 1, the NASA inlet treatment consists of laterally cut slots (cut perpendicular to the flow direction) which are designed to reduce excrescence drag while maintaining or exceeding the liner acoustic noise reduction capabilities. The NASA inlet liner consists of a Multi-Degree of Freedom (MDOF) design with two breathable septum layers inserted into each honeycomb cell [1]. The aircraft noise measurements were acquired for both takeoff (flaps 1 setting, gear up) and approach (flaps 30 gear up and gear down) configurations. The inlet and flight test configurations are summarized in Table 1. Table 1: Inlet Treatment and Flight Configurations Inlet Forward Fan Case Aircraft Production Hardwall Flaps 1, gear up; flaps 30 gear up; flaps 30 gear down NASA Hardwall Flaps 1, gear up; flaps 30 gear up; flaps 30 gear down Hardwall Hardwall Flaps 1, gear up; flaps 30 gear up; flaps 30 gear down III.Test Description and Hardware The flight testing was conducted at the Grant County airport in Moses Lake, WA, between 27 July and 6 August 2018. The noise measurement instrumentation included 8 flush dish microphones arranged in a noise certification configuration as well as an 840 microphone phased array. The flush dish microphones were used to quantify the levels and differences in levels between the various inlet treatments. The phased array was used to separate and quantify the narrowband (tonal) and broadband noise component levels from the engine inlet/exhaust and from the airframe. Phased array extraction of the broadband component was critical to this study because it allowed for the separation of the inlet component from the total airplane level noise even when it was significantly below the total level. Figure 1 provides an overview of the phased array microphone layout as well as a detailed image of an individual phased array microphone mounted in a plate holder (the microphone sensor is the dot in the center of the plate). The ground plane ensemble array microphones (referred to as “ensemble array” in this paper) were mounted in plates with “flower petal” edges designed to minimize edge scattering effects. Fig. 1 Flyover test microphone layout. The phased array configuration was the result of a progressive development of concepts originally implemented in Ref. 2 and refined over the following years, consisting namely of multiple multi-arm logarithmic spiral subarrays designed to cover overlapping frequency ranges and optimized for various aircraft emission angles. For the present case, the signals from all 840 microphones were acquired on a single system. The 840 microphones were parsed into 11 primary subarray sets spanning from smallest to largest aperture size and labeled accordingly as “a, b, …, k”, where “a” corresponds to the smallest fielded subarray and “k” corresponds to the largest aperture subarray. The apertures ranged from approximately 10 ft to 427 ft in size (in the flight direction) with the subarrays consisting of between 215 and 312 microphones. Figure 2 shows three such subarrays, k, h and a. As done in Ref. 2, microphones were shared between subarrays in order to reduce total channel count. Fig. 2 Sample subarray sizes (20° from overhead – refer to Figure 3a discussion). In addition to the above, each of the 11 primary subarray sets consisted of four subarrays optimized to provide near equivalent array spatial resolution in both the flight and lateral directions within 30 degrees of overhead (i.e., airplane directly above the center of the array), namely, at angles  of 0, ±10, ±20 and ±30 degrees relative to overhead where angle  is defined as shown in Figure 3a. This allowed for optimized aircraft noise measurements from 60 to 120 degree emission angle.6 An example of this pletharray design is shown in Figure 3b for the k subarray. When the aircraft is at overhead, the microphones indicated by the blue markers are used for beamforming. When the aircraft is at angles ±10 degrees from overhead, both the blue and red colored microphones are used, and so on for the ±20 and ±30 degree aircraft locations. See Ref. 3 for extensive details on pletharray design for aeroacoustic phased array testing. 6 In the discussions that follow, emission angle values are used. These are the angles at the time sound is emitted relative to the engine axis and are calculated based on flight path angle, body aircraft body angle with respect to the relative wind direction, and engine axis angle relative to aircraft body angle.

Brusniak, Leon↗
DOE OSTI2021

Probabilistic Day-Ahead Forecasting Using an Analog Ensemble Approach for Wind Farm Grid Services

Wind resource assessment and wind power forecasting are used in research and industry to anticipate future power output at scales ranging from individual wind turbines to entire wind farms. Probabilistic day-ahead wind forecasting is useful for anticipating how a wind farm could potentially participate in the day-ahead market by providing upper and lower bounds for expected power generation, thus informing grid operators of its uncertainty. Understanding this uncertainty is part of a larger project focused on building a platform that combines efforts in weather forecasting, aerodynamic and economic modeling to create maximum value of a wind plant to better provide services to the grid. This effort is also known as the Atmosphere to Electrons to Grid (A2E2G) project. One method for producing a probabilistic forecast is through the analog ensemble approach (Delle Monache et al., 2011). This method leverages historical forecasts and their corresponding observations as a training data set from which future forecasts can be made. For some future forecast, the most similar historical forecasts (analogs) are identified on a regular time basis such as once per a 3-hour window. The most similar analogs, based on a metric such as root mean square error (RMSE), are recorded and their corresponding verifying observations are used as an ensemble member for this future forecast. Prior work in this area demonstrates improvements over raw Numerical Weather Prediction (NWP) forecasts and shows skill similar to techniques such as logistic regression and machine learning (Delle Monache et al., 2013; Alessandrini et al., 2015). Here, we take the High-Resolution Rapid Refresh model (HRRR) day-ahead forecast (0-36 hours) to create a probabilistic day-ahead forecast using an analog ensemble approach. The HRRR has an hourly temporal resolution, with a spatial resolution of 3 km. The 12 UTC HRRR model run is downloaded every day for one year from August 2019 - July 2020, with the first 11 months serving as a bank of analogs from which the forecasting algorithm can create a probabilistic forecast. Once downloaded, the original HRRR forecast is temporally interpolated to 5-minutes, aligning with both the temporal resolution of the observations as well as the timescale relevant for day-ahead power forecasts. The forecast is validated at the M2 tower at the Flatirons Campus of the National Renewable Energy Laboratory (NREL) at a typical wind turbine height of 80 m. Variables such as wind speed, wind direction, and turbulence intensity are incorporated into the probabilistic forecast model and weighted according to their relative importance to the forecast. Based on metrics such as mean bias error (MBE), mean absolute error (MAE), and root mean square error, the analog ensemble forecast outperforms the raw HRRR forecast during the testing period of July 2020. Figure 1 illustrates an example day-ahead forecast compared against the verifying observations. The general variability and ramps are captured throughout the day, with potential to further improve the analog ensemble model through machine learning techniques.

numerical weather prediction↗
DOE OSTI2021

Mechanisms of Dynamic Near-Wake Modulation of a Utility-Scale Wind Turbine

The current study uses large eddy simulations to investigate the transient response of a utility-scale wind turbine wake to dynamic changes in atmospheric and operational conditions, as observed in previous field-scale measurements. Most wind turbine wake investigations assume quasi-steady conditions, but real wind turbines operate in a highly stochastic atmosphere, and their operation (e.g. blade pitch, yaw angle) changes constantly in response. Furthermore, dynamic control strategies have been recently proposed to optimize wind farm power generation and longevity. Therefore, improved understanding of dynamic wake behaviours is essential. First, changes in blade pitch are investigated and the wake expansion response is found to display hysteresis as a result of flow inertia. The time scales of the wake response to different pitch rates are quantified. Next, changes in wind direction with different time scales are explored. Under short time scales, the wake deflection is in the opposite direction of that observed under quasi-steady conditions. Finally, yaw changes are implemented at different rates, and the maximum inverse wake deflection and time scale are quantified, showing a clear dependence on yaw rate. To gain further physical understanding of the mechanism behind the inverse wake deflection, the streamwise vorticity in different parts of the wake is quantified. The results of this study provide guidance for the design of advanced wake flow control algorithms. The lag in wake response observed for both blade pitch and yaw changes shows that proposed dynamic control strategies must implement turbine operational changes with a time scale of the order of the rotor time scale or slower.

flow-structure interactions↗
DOE OSTI2019

Tonopah Test Range Air Monitoring: CY2018 Meteorological, Radiological, and Wind Transported Particulate Observations

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 primary objective of the monitoring effort is 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 to provide information for designing long-term monitoring of the sites. The monitoring program in 2018 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. The stations—which are similar in design to the Community Environmental Monitoring Program (CEMP) stations operating at locations surrounding the Nevada National Security Site and TTR—include meteorological instruments and continuous-flow, low-volume air samplers. Three pressurized ionization chambers for measuring gamma energy are in use at Stations 400, 401, and 403. Saltation sensors and saltation traps are deployed at the stations adjacent to the Clean Slate sites. Detailed meteorological data are recorded on data loggers, with periodic uploads via a satellite system to the Western Regional Climate Center (WRCC) at Desert Research Institute (DRI), to monitor instrument and site conditions. Air filter samples are collected every two weeks and the deposits in the saltation traps are collected when a sufficient sample mass for analysis has accumulated (generally a six- to eight-month interval). Monitoring at TTR in calendar year (CY) 2018 provided surveillance of both ambient conditions, and ground disturbance associated with remediation activities at Clean Slate II and III. The first half of CY2018 data from Stations 404 and 405 at Clean Slate II were collected during remediation activities at that site, and data collected during the final third of the year at Stations 401 and 403 at Clean Slate III coincided with remediation work at the site. Ambient conditions prevailed at Station 400 throughout the year. Dust transport by suspension and saltation is strongly dependent on wind speed. Concentrations of PM 10 (particulate matter of aerodynamic diameter ≤10 micrometers [μm], a size fraction of small particles that are suspended in the air and can be easily inhaled) remain low until winds exceed approximately 32 kilometers per hour (km/hr) (20 miles per hour [mph]). Saltation particle counts also increase above the same general threshold wind speed. Wind speeds in excess of 32 km/hr (20 mph) occur less than three percent of the time at the sites. Ground disturbing remediation activities were carried out at Clean Slate II from October 31, 2017, until July 26, 2018. Station 404 is located closest to the remediation area and is downwind of the area when winds are from the south. By late November, analysis of dust from the air sampler at Station 404 registered increases in plutonium-239+240 ( 239+240 Pu) concentrations. Peak plutonium concentrations, accompanied by elevated gross alpha concentrations and the detection of americium-241 ( 241 Am) by gamma spectroscopy, occurred between March and May 2018. During spring and early summer of 2018, elevated radionuclide concentrations were also measured in dust collected from the air sampler at Station 405, which is more than twice the distance from the remediation activity as Station 404. A minor but sustained increase in gross alpha concentration of suspended dust collected at Stations 400, 401, and 403 was also observed starting in May 2018 and continuing into August. Stations 401 and 403 are approximately 4.8 to 6.4 km (three to four miles) from Clean Slate II and Station 400 is approximately 13 km (eight miles) from that site. The radionuclide content of material that saltated into the passive Big Spring Number Eight (BSNE) traps during the Clean Slate II remediation period increased in both the traps facing toward and away from the fenced contamination area at Station 404, with the largest increases in concentration (almost five times the previous 239+240 Pu concentration) observed in the traps facing the fenced area. The radionuclide concentration in material in the trap facing away from the fence more than doubled from that measured in 2017. A similar increase was observed for the saltation trap facing away from the fenced area at Station 405, although no increase was observed for material captured in the trap facing the fenced area. Particles ranging in size from 50 to 500 µm generally move by saltation processes and are not expected to significantly contribute to dose for someone breathing in the standard respiratory zone for adult humans (1.5 to 1.7 m [4.9 to 5.6 ft] above ground level) because they are too large to be entrained in the air as suspended dust. Remediation activities at Clean Slate III began on September 5, 2018, and continued until a brief hiatus started on December 20, 2018. Although Station 401 is much closer to the remediation area than Station 403, the first increase in gross alpha and 239+240 Pu concentrations was noted in dust from filters collected at Station 403 in early November 2018. The highest radionuclide concentrations of 2018 at the Clean Slate III stations were recorded in air samples taken from Stations 401 and 403 in late December 2018 and these concentrations were comparable between the two station locations. Gamma rate data measured by the PICs was slightly higher than the annual average at Stations 401 and 403 after remediation activities began, but no single measurement was outside of the range observed in 2018 prior to remediation activities. No increase in radionuclide concentration was observed for the Clean Slate III remediation time period on air filters collected from the other stations. Radiological monitoring at Station 400—which is located at the ROC, and therefore the closest regularly staffed work location to the Clean Slate sites—recorded a higher mean gross alpha activity from dust collected on air sampler filters than in previous years. The individual biweekly air filters yielded measurements of gross alpha that were at or below the mean plus one standard deviation measured in CY2017, except for the period from April 25, 2018, through September 12, 2018. Nonetheless, gamma spectroscopy of all air filters from Station 400 identified only naturally occurring radionuclides, with no detection of americium-241 ( 241 Am) (which is used as an indicator of plutonium-241[ 241 Pu]). Alpha spectroscopy of selected air filters collected at Station 400 did not detect plutonium-238 ( 238 Pu) or 239+240 Pu above the analytical detection limit. Gross beta and gamma exposure rate data were comparable to data from regional CEMP stations. No saltation traps are deployed at Station 400. Considering the ambient monitoring periods during CY2018, the monitoring stations adjacent to the Clean Slate sites recorded above-background 238 Pu and 239+240 Pu concentrations (SNL, 1998; Turner et al., 2003) in samples collected by passive saltation traps and on selected air filters. These observations were consistent with previous years and indicated that radionuclide movement occurred under ambient conditions by processes of both saltation and suspension. The presence of radionuclides in all saltation traps, both upwind and downwind of the fenced contamination areas, was consistent with previous radiological survey results (EG&G, 1979) that show lower concentration radiologically contaminated material dispersed outside the fenced area. Although 239+240 Pu concentrations in the air filter samples were sometimes orders of magnitude higher than background, the concentrations were below risk-based action levels defined for an industrial area worker scenario (U.S. DOE, 2014). The interception of airborne dust with above-background radionuclide concentrations demonstrates that dispersal processes are continuing. Strong winds are the driver for both suspension and saltation of dust and soil. The recorded wind speed and PM10 during remediation activities at Clean Slate II and III identified discrete wind events lasting approximately five to eight hours each, which accounted for the greatest PM 10 concentrations at the monitoring stations. These events captured the effect of winds from the northwest and from the south. The observed differences in dust loading and radionuclide concentrations for the monitoring stations when they were either upwind or downwind of the contamination area identify the remediation area as a source of plutonium contaminated dust. The increase in concentration of radionuclides in airborne dust during the remediation activities, compared with measurements under previous ambient conditions (mean 239+240 Pu concentrations during remediation activities are approximately nine to 40 times higher compared to pre-remediation concentrations, depending on monitoring station), highlights the impact of land disturbance on wind-borne contaminant transport from the Clean Slate test areas. The meteorological and particulate matter monitoring indicated that conditions for wind-borne contaminant movement exist at the Clean Slate sites, and that transport of radionuclide-contaminated soil by both suspension and saltation occurred under ambient conditions and accelerated when the sites were disturbed. The 239+240 Pu alpha spectroscopy results indicated that airborne concentrations during remediation activities ranged from 2 × 10-16 to 380 × 10-16 µCi/ml. These results can be compared to the derived air concentration limit for 239 Pu of 6 × 10-11 µCi/ml for occupational workers contained in Code of Federal Regulations 10 CFR 835, Occupational Radiation Protection. Even though the air sampling results document an increase in concentration during remediation activities, the observed concentrations are less than 0.06 percent of the occupational limit. The Corrective Action Unit (CAU) closure strategy uses a risk-based approach, whereby acceptable contaminant concentrations are determined as a function of anticipated human exposure.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Large-eddy simulation of an atmospheric bore and associated gravity wave effects on wind farm performance in the southern Great Plains

Gravity waves are a common occurrence in the atmosphere, with a variety of generation mechanisms. Their impact on wind farms has only recently gained attention, with most studies focused on wind farm-induced gravity waves. In this study, the interaction between a wind farm and gravity waves generated by an atmospheric bore event is assessed using multiscale large-eddy simulations. The atmospheric bore is created by a thunderstorm downdraft from a nocturnal mesoscale convective system (MCS). The associated gravity waves impact the wind resource and power production at a nearby wind farm during the American Wake Experiment (AWAKEN) in the US southern Great Plains. A two-domain nested setup (Δx=300 and 20 m) is used in the Weather Research and Forecasting (WRF) model, forced with data from the High-Resolution Rapid Refresh model, to capture both the formation of the bore and its interaction with individual wind turbines. The MCS is resolved on the large outer domain, where the structure of the bore and the associated gravity waves are found to be especially sensitive to parameterized microphysics processes. On the finer inner domain, gravity wave interactions with individual wind turbines are resolved; wake dynamics are captured using a generalized actuator disk parameterization in WRF. The gravity waves are found to have a strong effect on the atmosphere above the wind farm; however, the effect of the waves is more nuanced closer to the surface where there is additional turbulence, both ambient and wake-generated. Notably, the gravity waves modulate the mesoscale environment by weakening and dissipating the preexisting low-level jet, which reduces hub-height wind speed and hence the simulated power output, which is confirmed by the observed supervisory control and data acquisition (SCADA) power data. Additionally, the gravity waves induce local wind direction variations correlated with fluctuations in pressure, which lead to fluctuations in the simulated power output as various turbines within the farm are subjected to waking from nearby turbines.

17 WIND ENERGY↗
DOE OSTI2021

Assessment of Updraft Modeling Bias Using Computational Fluid Dynamics

Golden Eagle (Aquila chrysaetos) habitats may overlap with wind energy development in some regions of the US. Eagles, and similar soaring bird species, are therefore at risk of collision with wind turbines when flying through wind farms. Recently developed behavioral modeling approaches can predict the presence of eagles near turbines within the rotor-swept layer but require reliable prediction of atmospheric flowfield conditions. In particular, the vertical component of the wind speed dictates a soaring bird's ability to maintain or gain altitude, since they rely on updrafts to subsidize their flight. In this work, we investigate the atmospheric conditions around a wind farm in complex terrain and compare methods for atmospheric characterization. We use computational fluid dynamics (specifically, large-eddy simulations, or LES) to simulate the atmospheric boundary layer over a region encompassing multiple wind farms with high temporal and spatial resolution (seconds and 10's of meters, respectively). We compare traditional non-simulation-based methods of determining the orographic updraft potential based on wind direction, terrain slope and aspect, with the flowfields from LES that include both orographic updrafts alone and combined thermal and orographic updrafts. Preliminary analysis suggests that although the model captures the horizontal pattern of vertical updrafts, their magnitude can be improved with information about the surface heat flux, which is usually correlated with time of the day. Within our study region, we found that the low-fidelity model may over- or underestimate updraft potential by up to 400% at 80 m AGL, depending on local orographic features. This can result in an inaccurate representation of eagle presence and, consequently, risk. Another important finding is that flowfield time-averaging can hide important details about the flight environment, including how thermally generated flow structures within the atmospheric boundary layer (e.g., convective rolls and/or cells) may be important drivers of eagle flight.

atmospheric turbulence↗
NASA NTRS1999

The Mars Pathfinder Mission and Science Results

Mars Pathfinder, the first low-cost, quick Discovery class mission to be completed, successfully landed on the surface of Mars on July 4, 1997, deployed and navigated a small rover, and collected data from 3 science instruments and 10 technology experiments. The mission operated on Mars for 3 months and returned 2.3 Gbits of new data, including over 16,500 lander and 550 rover images, 16 chemical analyses of rocks and soil, and 8.5 million individual temperature, pressure and wind measurements. The rover traversed 100 m clockwise around the lander, exploring about 200 square meters of the surface. The mission captured the imagination of the public, and garnered front page headlines during the first week. A total of about 566 million internet "hits" were registered during the first month of the mission, with 47 million "hits" on July 8th alone, making the Pathfinder landing by far the largest internet event in history at the time. Pathfinder was the first mission to deploy a rover on Mars. It carried a chemical analysis instrument, to characterize the rocks and soils in a landing area over hundreds of square meters on Mars, which provided a calibration point or "ground truth" for orbital remote sensing observations. The combination of spectral imaging of the landing area by the lander camera, chemical analyses aboard the rover, and close-up imaging of colors, textures and fabrics with the rover cameras offered the potential of identifying rocks (petrology and mineralogy). With this payload, a landing site in Ares Vallis was selected because it appeared acceptably safe and offered the prospect of analyzing a variety of rock types expected to be deposited by catastrophic floods, which enabled addressing first-order scientific questions such as differentiation of the crust, the development of weathering products, and the nature of the early Martian environment and its subsequent evolution. The 3 instruments and rover allowed seven areas of scientific investigation: the geology and geomorphology of the surface, mineralogy and geochemistry of rocks and soils, physical properties of surface materials, magnetic properties of airborne dust, atmospheric science including aerosols, and rotational and orbital dynamics of Mars. Scientists were assembled into 7 Science Operations Groups that were responsible for requesting measurements by the 3 instruments, rover and engineering subsystems for carrying out their scientific investigations and for analyzing the data and reporting on their findings. The spacecraft was launched on December 4, 1996 and had a 7 month cruise to Mars, with four trajectory correction maneuvers. The vehicle entered the atmosphere directly following cruise stage separation. Parachute deployment, heatshield and lander separation, radar ground acquisition, airbag inflation and rocket ignition all occurred before landing at 2:58 AM true local solar time (9:56:55 AM PDT). The lander bounced at least 15 times up to 12 in high without airbag rupture, demonstrating the robustness of this landing system. Reconstruction of the final landing sequence indicates that the parachute/backshel1/1ander was tilted due to a northwest directed wind and wind shear, which resulted in the lander bouncing about I km to the northwest and initially downhill about 20 m from where the solid rockets fired. Two anomalously bright spots located in the lander scene are likely the heatshield, which continued in a ballistic trajectory about 2 km downrange (west southwest), and the backshell/parachute, which stayed nearer to where the rockets fired. Unconnected disturbed soil patches in the scene indicate that the final few bounces of the lander were from the east-southeast and were followed by a gentle roll to the west before coming to rest on the base petal. The location of the lander away from where the solid rockets fired and considerations of the exhaust products used to inflate the airbags and their fate, indicate that the Pathfinder landing system is one of the cleanest designed leaving the local area essentially contaminant free. The radio signal from the low-=gain antenna was received at 11:34 AM PDT indicating a successful landing.

Golombek, M. P.↗
DOE OSTI2025

Cleaned 5-Minute Resolution Air Quality and Meteorological Data from Nine TCEQ CAMS Sites in Houston, Texas (Nov 2021 – Oct 2022)

These data encompass 5-minute air monitoring and meteorological observations collected in the greater Houston, Texas metropolitan region, at nine (9) Continuous Ambient Monitoring Stations (CAMS) operated by the Texas Commission on Environmental Quality (TCEQ) between November 1, 2021 and October 31, 2022. The CAMS sites (CAMS 1, 8, 35, 45, 148, 403, 405, 410, and 1052) were chosen because their instrumentation includes measurements of PM2.5. These sites also provide continuous multi-parameter air-quality and meteorological measurements. Particulate matter (PM2.5, PM10) was sampled along with several trace gases, including ozone (O3), nitrogen oxides (NO, NO2, NOx), sulfur dioxide (SO2), and carbon monoxide (CO). The data set also contains standard surface meteorological parameters (temperature, humidity, pressure, wind speed, and wind direction). Several sites also include AutoGC-based measurements of volatile organic compounds (VOCs). Air monitoring instruments deployed at the selected sites comprise the following systems: BAM-1020 or TEOM (PM2.5), Thermo Scientific TEI 49i (O3), TEI 42i (NOx), and AutoGCs (VOCs). This data set is similar to the data included within the houairq5mX1.00 datastream, except for a few additional quality control steps. A systematic data cleaning and verification process was performed on the data set to ensure its quality and preparation for analysis. Removal of non-numeric status flags (e.g., [LIM], [QAS], [SPZ], [CAL], [PMA], [AQI], [SPN], [MAL]) was accomplished by employing rule-based string parsing to extract valid numerical values. Missing entries were set to -9999; however, invalid or anomalous values (e.g., 99999) were retained as originally reported by the TCEQ to preserve data provenance. The time sequence was verified for completeness, removal of duplicates, and uniformity at 5-minute intervals. Column labeling was standardized, and corresponding values were assessed for physical plausibility. All timestamps in the data set were reported in Coordinated Universal Time (UTC) as provided by the TCEQ. Further, the latitude and longitude coordinates were added for each CAMS site. A subset of the data (June 1–September 30, 2022) has been used in the following publication: Subba et al. 2025. “Implications of sea breeze circulations on boundary layer aerosols in the southern coastal Texas region.” EGUsphere 2025: 1–49, https://doi.org/10.5194/egusphere-2025-2659.

latitude↗
DOE OSTI2022

Curled-Skewed Wakes behind Yawed Wind Turbines Subject to Veered Inflow

This work presents a new engineering analytical model that predicts the effect of both the turbine yaw misalignment and the inflow wind veer on the wake flow distribution downwind of a wind turbine. To consider the veered inflow, two methods were examined. In the first method, the curled shape of the wake due to the yaw offset is initially modelled. The wake shape is then laterally skewed at each height due to the wind veer based on the assumption that the turbine wake is transported downstream by the incoming flow. The second method is a more realistic approach that accounts for the effect of wind veer on the wind velocity direction and the yaw angle seen by the wind turbine. This models the wake region in a local coordinate system defined based on the wind direction at each height. A coordinate transformation is then performed to represent the wake flow distribution in the global coordinate system attached to the ground. The results show that while the two methods provide similar outputs for small variations in the wind direction across the rotor, the difference becomes more evident with an increase in wind veer. High-fidelity simulations for a turbine subject to a neutral atmospheric boundary layer were employed to validate model predictions for different operating conditions.

17 WIND ENERGY↗
DOE OSTI2022

A Case Study of the Weather Research and Forecasting Model Applied to the Joint Urban 2003 Tracer Field Experiment. Part III: Boundary-Layer Parametrizations

Numerical weather prediction is often used to supply the mean wind and turbulence fields for atmospheric transport and dispersion plume models as they provide dense geographic coverage in comparison to typically sparse monitoring networks. Here, the Weather Research and Forecasting (WRF) model 4.0 was run over the month-long period of the Joint Urban 2003 field campaign conducted in Oklahoma City. We compare three different simulations in their ability to reproduce the observations, each using a different boundary-layer parametrization. Specifically, we examine the Mellor–Yamada–Janjic (MYJ), Yonsei University (YSU), and Mellor–Yamada–Nakanishi–Niino (MYNN) boundary-layer parametrizations. All three predict the wind speed well during the day but overpredict it at night. The MYNN parametrization is better than MYJ at predicting the daytime turbulence in the surface layer, but both underpredict the nocturnal turbulence. Additionally, the MYJ parametrization is best at predicting the reciprocal Obukhov length, while MYNN and YSU both significantly overpredict thermal stability. Reconstructing the reciprocal Obukhov length from other simulated parameters produces more accurate values for both parametrizations. All three models overpredict the boundary-layer height, particularly under convective conditions. The MYJ parametrization overestimates boundary-layer height the most, while YSU and MYNN have comparable performance with MYNN having an advantage in predicting the stable boundary-layer height. Several days were found where the WRF simulations predict significant deviations from the prevailing diurnal pattern in wind direction, which are not found in the observations.

54 ENVIRONMENTAL SCIENCES↗