Engineering topics

Lundquist, Julie K.

Publications and source records attributed to Lundquist, Julie K..

48 records · Page 3

DOE OSTI2020

Does the rotational direction of a wind turbine impact the wake in a stably stratified atmospheric boundary layer?

Stably stratified atmospheric boundary layers are often characterized by a veering wind profile, in which the wind direction changes clockwise with height in the Northern Hemisphere. Wind-turbine wakes respond to this veer in the incoming wind by stretching from a circular shape into an ellipsoid. We investigate the relationship between this stretching and the direction of the turbine rotation by means of large-eddy simulations. Clockwise rotating, counterclockwise rotating, and non-rotating actuator disc turbines are embedded in wind fields of a precursor simulation with no wind veer and in wind fields with a Northern Hemispheric Ekman spiral, resulting in six combinations of rotor rotation and inflow wind condition. The wake strength, extension, width, and deflection depend on the interaction of the meridional component of Ekman spiral with the rotational direction of the actuator disc, whereas the direction of the disc rotation only marginally modifies the wake if no veer is present. The differences result from the amplification or weakening/reversion of the spanwise and the vertical wind components due to the effect of the superposed disc rotation. They are also present in the streamwise wind component of the wake and in the total turbulence intensity. In the case of an counterclockwise rotating actuator disc, the spanwise and vertical wind components increase directly behind the rotor, resulting in the same rotational direction in the whole wake while its strength decreases downwind. In the case of a clockwise rotating actuator disc, however, the spanwise and vertical wind components of the near wake are weakened or even reversed in comparison to the inflow. This weakening/reversion results in a downwind increase in the strength of the flow rotation in the wake or even a different rotational direction in the near wake in comparison to the far wake. The physical mechanism responsible for this difference can be explained by a simple linear superposition of a veering inflow with a Rankine vortex.

17 WIND ENERGY↗

DOE OSTI2020

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

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

17 WIND ENERGY↗

DOE OSTI2020

Longitudinal coherence and short-term wind speed prediction based on a nacelle-mounted Doppler lidar

The spatial structure of turbulence in atmospheric boundary layer flows is highly relevant to wind energy. In particular, wind turbine control strategies based on inflow preview measurements require knowledge of the longitudinal evolution of turbulent flow as it approaches the rotor. These upstream measurements are usually obtained with nacelle-mounted wind lidars. In contrast to traditional in situ anemometry, lidars collect measurements within a probe volume which varies in size depending on the technology of the commercial system being used. Here, we address two issues related to the use of wind lidar to measure the incoming flow to a wind turbine: (i) whether existing longitudinal coherence models can be used to predict flow at the rotor, based on measurements performed at a distance away from the rotor; and (ii) what effect probe-volume averaging has on the inflow predictions. These two questions are critical to the design and implementation of robust wind turbine control strategies. To address these questions, we perform field measurements and large-eddy simulations to determine which incoming flow structures can be readily predicted with existing coherence models, and which require additional corrections to account for lidar volumetric averaging effects. Results reveal that the wind turbine induction zone has a negligible impact on the longitudinal coherence and first-order turbulence quantities, such as the standard deviation of velocity fluctuations. However, the phase of the signal, from which advection time periods of the turbulent structures are derived, is affected by the rotor blockage effect.

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DOE OSTI2020

Can machine learning improve the model representation of turbulent kinetic energy dissipation rate in the boundary layer for complex terrain?

Current turbulence parameterizations in numerical weather prediction models at the mesoscale assume a local equilibrium between production and dissipation of turbulence. As this assumption does not hold at fine horizontal resolutions, improved ways to represent turbulent kinetic energy (TKE) dissipation rate (ϵ) are needed. Here, we use a 6-week data set of turbulence measurements from 184 sonic anemometers in complex terrain at the Perdigão field campaign to suggest improved representations of dissipation rate. First, we demonstrate that the widely used Mellor, Yamada, Nakanishi, and Niino (MYNN) parameterization of TKE dissipation rate leads to a large inaccuracy and bias in the representation of ϵ. Next, we assess the potential of machine-learning techniques to predict TKE dissipation rate from a set of atmospheric and terrain-related features. We train and test several machine-learning algorithms using the data at Perdigão, and we find that the models eliminate the bias MYNN currently shows in representing ϵ, while also reducing the average error by up to almost 40 %. Of all the variables included in the algorithms, TKE is the variable responsible for most of the variability of ϵ, and a strong positive correlation exists between the two. These results suggest further consideration of machine-learning techniques to enhance parameterizations of turbulence in numerical weather prediction models.

58 GEOSCIENCES↗

DOE OSTI2020

How wind speed shear and directional veer affect the power production of a megawatt-scale operational wind turbine

Most megawatt-scale wind turbines align themselves into the wind as defined by the wind speed at or near the center of the rotor (hub height). However, both wind speed and wind direction can change with height across the area swept by the turbine blades. A turbine aligned to hub-height winds might experience suboptimal or superoptimal power production, depending on the changes in the vertical profile of wind, also known as shear. Using observed winds and power production over 6 months at a site in the high plains of North America, we quantify the sensitivity of a wind turbine's power production to wind speed shear and directional veer as well as atmospheric stability. We measure shear using metrics such as α (the log-law wind shear exponent), β bulk (a measure of bulk rotor-disk-layer veer), β total (a measure of total rotor-disk-layer veer), and rotor-equivalent wind speed (REWS; a measure of actual momentum encountered by the turbine by accounting for shear). We also consider the REWS with the inclusion of directional veer, REWS θ , although statistically significant differences in power production do not occur between REWS and REWS θ at our site. When REWS differs from the hub-height wind speed (as measured by either the lidar or a transfer function-corrected nacelle anemometer), the turbine power generation also differs from the mean power curve in a statistically significant way. This change in power can be more than 70 kW or up to 5 % of the rated power for a single 1.5 MW utility-scale turbine. Over a theoretical 100-turbine wind farm, these changes could lead to instantaneous power prediction gains or losses equivalent to the addition or loss of multiple utility-scale turbines. At this site, REWS is the most useful metric for segregating the turbine's power curve into high and low cases of power production when compared to the other shear or stability metrics. Therefore, REWS enables improved forecasts of power production.

17 WIND ENERGY↗

DOE OSTI2020

Evaluating the WFIP2 updates to the HRRR model using scanning Doppler lidar measurements in the complex terrain of the Columbia River Basin

The wind-energy (WE) industry relies on numerical weather prediction (NWP) forecast models as foundational or base models for many purposes, including wind-resource assessment and wind-power forecasting. During the Second Wind Forecast Improvement Project (WFIP2) in the Columbia River Basin of Oregon and Washington, a significant effort was made to improve NWP forecasts through focused model development, to include experimental refinements to the High Resolution Rapid Refresh (HRRR) model physics and horizontal grid spacing. In this study, the performance of an experimental version of HRRR that includes these refinements is tested against a control version, which corresponds to that of the operational HRRR run by National Oceanic and Atmospheric Administration/National Centers for Environmental Protection at the outset of WFIP2. Furthermore, the effects of horizontal grid resolution were also tested by comparing wind forecasts from the HRRR (with 3-km grid spacing) with those from a finer-resolution HRRR nest with 750-m grid spacing. Model forecasts are validated against accurate wind-profile measurements by three scanning, pulsed Doppler lidars at sites separated by a total distance of 71 km. Model skill and improvements in model skill, attributable to physics refinements and improved horizontal grid resolution, varied by season, by site, and during periods of atmospheric phenomena relevant to WE. In general, model errors were the largest below 150 m above ground level (AGL). Experimental HRRR refinements tended to reduce the mean absolute error (MAE) and other error metrics for many conditions, but degradation in skill (increased MAE) was noted below 150 m AGL at the two lowest-elevation sites at night. Finer resolution was found to produce the most significant reductions in the error metrics.

17 WIND ENERGY↗

DOE OSTI2020

Characterizing Thunderstorm Gust Fronts near Complex Terrain

Fire safety, aviation, wind energy, and structural-engineering operations are impacted by thunderstorm outflow boundaries or gust fronts (GFs) particularly when they occur in mountainous terrain. For example, during the 2013 Arizona Yarnell Hill Fire, 19 firefighters were killed as a result of sudden changes in fire behavior triggered by a passing GF. Knowledge of GF behavior in complex terrain also determines departure and landing operations at nearby airports, and GFs can induce exceptional structural loads on wind turbines. Furthermore, while most examinations of GF characteristics focus on well-organized convection in areas such as the Great Plains, here the investigation is broadened to explore GF characteristics that evolve near the complex terrain of the Colorado Rocky Mountains. Using in situ observations from meteorological towers, as well as data from wind-profiling lidars and a microwave radiometer, 24 GF events are assessed to quantify changes in wind, temperature, humidity, and turbulence in the lowest 300 m AGL as these GFs passed over the instruments. The changes in magnitude for all variables are on average weaker in the Colorado Front Range than those typically observed from organized, severe storms in flatter regions. Most events from this study experience an increase in wind speed from 1 to 8 m s -1 , relative humidity from 1% to 8%, and weak vertical motion from 0.3 to 3.6 m s -1 during GF passage while temperature drops by 0.2°–3°C and turbulent kinetic energy peaks at >4 m2 s -2 . Vertical profiles reveal that these changes vary little with height in the lowest 300 m.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Simulated wind farm wake sensitivity to configuration choices in the Weather Research and Forecasting model version 3.8.1

Wakes from wind farms can extend over 50 km downwind in stably stratified conditions. These wakes can undermine power production at downwind turbines, adversely impacting revenue. As such, wind farm wake impacts must be considered in wind resource assessments, especially in regions of dense wind farm development. The open-source Weather Research and Forecasting (WRF) numerical weather prediction model includes a wind farm parameterization to estimate wind farm wake effects, but model configuration choices can influence the resulting predictions of wind farm wakes. These choices include vertical resolution, horizontal resolution, and whether or not to include the addition of turbulent kinetic energy generated by the rotating wind turbines. Despite the sensitivity to model configuration, no clear guidance currently exists for these options. Here we compare simulated wind farm wakes produced by varying model configurations with meteorological observations near a land-based wind farm in flat terrain over several diurnal cycles. A WRF configuration comprised of horizontal resolutions of 3 km or 1 km paired with a vertical resolution of 10 m provides the most accurate representation of wind farm wake effects, such as the correct surface warming and elevated wind speed deficit. The inclusion of turbine-generated turbulence is also critical to produce accurate surface warming and should not be omitted.

17 WIND ENERGY↗

DOE OSTI2020

American WAKE experimeNt (AWAKEN)

The national laboratories, directed by the U.S. Department of Energy Wind Energy Technologies Office, will organize, design, and execute a landmark international wake observation and validation campaign known as the American WAKE experimeNt or AWAKEN. This document describes the vision and purpose for AWAKEN and describes some of the organizational activities that have taken place to date. The driving need for this experimental campaign is that wake interactions are among the least understood physical phenomena in wind plants today, leading to unexpected power and financial losses. New observation data gathered will be used to further validate wind plant models and lead to both improved layout and more optimal operation of wind farms with greater power production and improved reliability, ultimately leading to lower wind energy costs. This campaign will occur in the U.S. Midwest, where the largest concentration of wind farms in the U.S. is located and where significant future growth is most likely. The field campaign will provide a data set that is unique among wake studies, based on location, scope, and observational fidelity: most wake studies have largely focused on offshore wind farms, which are not currently critical to the U.S. wind energy supply, and with a limited number of observations. While AWAKEN is focused on land-based wind farms in flat terrain, many of the observations will help researchers understand fundamental wake behavior applicable in both offshore and complex terrain environments.

17 WIND ENERGY↗

DOE OSTI2020

American WAKE experimeNt (AWAKEN)

The American WAKE experimeNt (AWAKEN) is an international multi-institutional wind energy field campaign to better understand wake losses within operational wind farms. Wake interactions are among the least understood physical interactions in wind plants today, leading to unexpected power and profit losses. For example, Ørsted, the world’s largest offshore wind farm developer, recently announced a downward revision in energy estimates across their energy generation portfolio, primarily caused by underprediction of energy losses from wind farm blockage and wakes. In their announcement, they noted that the standard industry models used for their original energy estimates were inaccurate, and this was likely an industry-wide issue. To help further improve and validate wind plant models across scales from individual turbines as well as interfarm interactions between plants, new observations, such as those planned for AWAKEN, are critical. These model improvements will enable both improved layout and more optimal operation of wind farms with greater power production and improved reliability, ultimately leading to lower wind energy costs.

17 WIND ENERGY↗

DOE OSTI2020

An LES-based airborne Doppler lidar simulator and its application to wind profiling in inhomogeneous flow conditions

Wind profiling by Doppler lidar is common practice and highly useful in a wide range of applications. Airborne Doppler lidar can provide additional insights relative to ground-based systems by allowing for spatially distributed and targeted measurements. Providing a link between theory and measurement, a first large eddy simulation (LES)-based airborne Doppler lidar simulator (ADLS) has been developed. Simulated measurements are conducted based on LES wind fields, considering the coordinate and geometric transformations applicable to real-world measurements. The ADLS provides added value as the input truth used to create the measurements is known exactly, which is nearly impossible in real-world situations. Thus, valuable insight can be gained into measurement system characteristics as well as retrieval strategies. As an example application, airborne Doppler lidar wind profiling is investigated using the ADLS. For commonly used airborne velocity azimuth display (AVAD) techniques, flow homogeneity is assumed throughout the retrieval volume, a condition which is violated in turbulent boundary layer flow. Assuming an ideal measurement system, the ADLS allows to isolate and evaluate the error in wind profiling which occurs due to the violation of the flow homogeneity assumption. Overall, the ADLS demonstrates that wind profiling is possible in turbulent wind field conditions with reasonable errors (root mean squared error of 0.36 m s –1 for wind speed when using a commonly used system setup and retrieval strategy for the conditions investigated). Nevertheless, flow inhomogeneity, e.g., due to boundary layer turbulence, can cause an important contribution to wind profiling error and is non-negligible. Results suggest that airborne Doppler lidar wind profiling at low wind speeds (<5 m s –1 ) can be biased, if conducted in regions of inhomogeneous flow conditions.

17 WIND ENERGY↗

DOE OSTI2020

Turbulent kinetic energy over large offshore wind farms observed and simulated by the mesoscale model WRF (3.8.1)

Abstract. Wind farms affect local weather and microclimates;hence, parameterizations of their effects have been developed for numerical weather prediction models.While most wind farm parameterizations (WFPs) include drag effects of wind farms, models differ on whether or not an additionalturbulent kinetic energy (TKE) source should be included in theseparameterizations to simulate the impact of wind farms on the boundary layer.Therefore, we use aircraft measurements above large offshore wind farms in stable conditions to evaluateWFP choices. Of the three case studies we examine, we find the simulated ambient backgroundflow to agree with observations of temperature stratification and winds. This agreement allows us toexplore the sensitivity of simulated wind farm effects with respect to modeling choicessuch as whether or not to include a TKE source, horizontal resolution, vertical resolutionand advection of TKE. For a stably stratified marine atmospheric boundary layer (MABL), a TKE source and a horizontal resolutionon the order of 5 km or finer are necessary to represent the impact of offshore wind farms on the MABL.Additionally, TKE advection results in excessively reduced TKE over the wind farms, which in turn causesan underestimation of the wind speed deficit above the wind farm. Furthermore, using fine verticalresolution increases the agreement of the simulated wind speed with satellite observations of surface wind speed.

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