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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Continued results from a field campaign of wake steering applied at a commercial wind farm – Part 2

This paper presents the results of a field campaign investigating the performance of wake steering applied at a section of a commercial wind farm. It is the second phase of the study for which the first phase was reported in Fleming et al. (2019). The authors implemented wake steering on two turbine pairs, and compared results with the latest FLORIS (FLOw Redirection and Induction in Steady State) model of wake steering, showing good agreement in overall energy increase. Further, although not the original intention of the study, we also used the results to detect the secondary steering phenomenon. Results show an overall reduction in wake losses of approximately 6.6 % for the regions of operation, which corresponds to achieving roughly half of the static optimal result.

17 WIND ENERGY↗
DOE OSTI2023

Investigation of Wind Plant Wake Effects at the AWAKEN Field Campaign

Wind plant wakes characterized by lower momentum flow with increased turbulent kinetic energy (TKE) have been shown to propagate for several kilometers and interact with neighboring plants. To better understand these interactions, an Oklahoma site hosting five wind plants is heavily instrumented as part of the American Wake Experiment (AWAKEN) field campaign. Upstream, interior-plant, and downstream flows are probed using a suite of remote sensing instruments to quantify wake properties such as the magnitude and extent of momentum deficit and TKE increase. The effects of atmospheric inflow (wind speed, turbulence, stability, etc.) are investigated. Under certain conditions, the impact of an upstream plant on its downstream neighbor can be observed using the performance data recorded by the downstream plant. In addition to providing insight into plant wake behavior, the results of this study will be used as a benchmark case for wind plant simulations.

AWAKEN↗
DOE OSTI2020

Field Validation of Wake Steering Control with Wind Direction Variability

Wake steering is a wind farm control strategy wherein upstream turbines are misaligned with the wind direction to redirect their wakes away from downstream turbines, increasing overall wind plant power. Wake steering is often analyzed assuming steady mean wind directions across the wind farm. However, in practice, the wind direction varies considerably over time because of large-scale weather phenomena. Wind direction variability causes the increase in power production from wake-steering to be less than predicted by steady-state models, but more robust wake-steering strategies can be designed that account for variable wind conditions. This paper compares the achieved yaw offsets and power gains from two different 2-turbine wake-steering experiments at a commercial wind farm with model predictions using the FLOw Redirection and Induction in Steady State (FLORIS) control-oriented model, assuming both fixed and variable wind directions. The impact of wind direction variability is modeled by including wind direction and yaw uncertainty in the FLORIS calculations. The field results match the trends predicted, assuming wind direction variability. Specifically, the yaw offsets achieved in the intended control regions are lower than desired, resulting in less power gain, while a slight loss in power occurs for wind directions outside of the intended control region because of unintentional yaw misalignment. The agreement between the model and field results suggests that the wind direction variability model can be used to design wake-steering controllers that are more robust to variable wind conditions present in the field.

17 WIND ENERGY↗
DOE OSTI2020

Field Validation of Wake Steering Control with Wind Direction Variability: Preprint

Wake steering is a type of wind farm control strategy wherein upstream turbines are misaligned with the wind direction to redirect their wakes away from downstream turbines, increasing overall wind plant power. Wake steering is often analyzed assuming steady mean wind directions across the wind farm. However, in practice, the wind direction varies considerably over time because of large-scale weather phenomena. Wind direction variability causes wake steering controllers to increase power production less than predicted by steady state models, but more robust wake steering strategies can be designed that account for variable wind conditions. This paper compares the achieved yaw offsets and power gains from two 2-turbine wake steering experiments at a commercial wind farm with model predictions using the FLOw Redirection and Induction in Steady State (FLORIS) control-oriented model, assuming both fixed and variable wind directions. The impact of wind direction variability is modeled by including wind direction and yaw uncertainty in the FLORIS calculations. The field results match the trends predicted assuming wind direction variability. Specifically, the yaw offsets achieved in the intended control regions are lower than desired, resulting in less power gain, while a slight loss in power occurs for wind directions outside of the intended control region because of unintentional yaw misalignment. The agreement between the model and field results suggests that the wind direction variability model can be used to design wake steering controllers that are more robust to variable wind conditions present in the field.

17 WIND ENERGY↗
NASA NTRS1989

Application of the wide-field shadowgraph technique to rotor wake visualization

The wide field shadowgraph technique is reviewed along with its application to the visualization of rotor wakes. In particular, current experimental methods and data reduction requirements are discussed. Sample shadowgraphs are presented. These include shadowgraphs of model-scale helicopter main rotors and tilt rotors, and full scale tail rotors, both in hover and in forward flight.

Norman, Thomas R.↗
NASA NTRS1971

Magnetic field anomalies in the lunar wake

The interplanetary magnetic field is only slightly perturbed by the presence of the moon in the solar wind flow. A statistical study of umbral increases and of penumbral variations was conducted with respect to variation in the solar wind plasma value beta, the distance from the moon, and the selenographic longitude of the limb regions of the lunar surface in the solar wind flow. All lunar wake anomalies show a strong positive correlation with the plasma value beta, while only penumbral increases show a marked variation with distance from the moon. There is no clear correlation of penumbral anomaly occurrence with selenographic longitude of the exposed lunar limb in the solar wind flow.

Whang, Y. C.↗
NASA NTRS1972

Magnetic-field anomalies in the lunar wake.

The interplanetary magnetic field is only slightly perturbed by the presence of the moon in the solar-wind flow. A statistical study of the umbral increases and penumbral decreases and increases was conducted with variation of the solar-wind plasma beta value, the distance from the moon and the selenographic longitude of the limb regions of the lunar surface in the solar-wind flow. All lunar-wake anomalies show a strong positive correlation with the plasma beta value, whereas only penumbral increases show a marked variation with distance from the moon. There is no clear correlation of occurrence of penumbral anomaly with selenographic longitude of the exposed lunar limb in the solar-wind flow.

Whang, Y. C.↗
DOE OSTI2019

Initial Results From a Field Campaign of Wake Steering Applied at a Commercial Wind Farm: Part 1

Wake steering is a form of wind farm control in which turbines use yaw offsets to affect wakes in order to yield an increase in total energy production. In this first phase of a study of wake steering at a commercial wind farm, two turbines implement a schedule of offsets. Results exploring the observed performance of wake steering are presented, as well as some first lessons learned. For two closely spaced turbines, an approximate 13 % increase in energy was measured on the downstream turbine over a 10° sector. Additionally, the increase of energy for the combined upstream/downstream pair was found to be in line with prior predictions. Lastly, the influence of atmospheric stability over the results is explored.

17 WIND ENERGY↗
DOE OSTI2019

Initial results from a field campaign of wake steering applied at a commercial wind farm – Part 1

Wake steering is a form of wind farm control in which turbines use yaw offsets to affect wakes in order to yield an increase in total energy production. In this first phase of a study of wake steering at a commercial wind farm, two turbines implement a schedule of offsets. Results exploringthe observed performance of wake steering are presented and somefirst lessons learned. For two closely spaced turbines, an approximate 14% increase in energy was measured on the downstream turbine over a 10° sector, with a 4% increase in energy production of the combined upstream–downstream turbine pair. Lastly, the influence ofatmospheric stability over the results is explored.

17 WIND ENERGY↗
NASA NTRS1996

Far-Field Turbulent Vortex-Wake/Exhaust Plume Interaction for Subsonic and HSCT Airplanes

Computational study of the far-field turbulent vortex-wake/exhaust plume interaction for subsonic and high speed civil transport (HSCT) airplanes is carried out. The Reynolds-averaged Navier-Stokes (NS) equations are solved using the implicit, upwind, Roe-flux-differencing, finite-volume scheme. The two-equation shear stress transport model of Menter is implemented with the NS solver for turbulent-flow calculation. For the far-field study, the computations of vortex-wake interaction with the exhaust plume of a single engine of a Boeing 727 wing in a holding condition and two engines of an HSCT in a cruise condition are carried out using overlapping zonal method for several miles downstream. These results are obtained using the computer code FTNS3D. The results of the subsonic flow of this code are compared with those of a parabolized NS solver known as the UNIWAKE code.

Kandil, Osama A.↗
DOE OSTI2022

Data-driven wind turbine wake modeling via probabilistic machine learning

Wind farm design primarily depends on the variability of the wind turbine wake flows to the atmospheric wind conditions and the interaction between wakes. Physics-based models that capture the wake flow field with high-fidelity are computationally very expensive to perform layout optimization of wind farms, and, thus, data-driven reduced-order models can represent an efficient alternative for simulating wind farms. In this work, we use real-world light detection and ranging (LiDAR) measurements of wind-turbine wakes to construct predictive surrogate models using machine learning. Specifically, we first demonstrate the use of deep autoencoders to find a low-dimensional latent space that gives a computationally tractable approximation of the wake LiDAR measurements. Then, we learn the mapping between the parameter space and the (latent space) wake flow fields using a deep neural network. Additionally, we also demonstrate the use of a probabilistic machine learning technique, namely, Gaussian process modeling, to learn the parameter-space-latent-space mapping in addition to the epistemic and aleatoric uncertainty in the data. Finally, to cope with training large datasets, we demonstrate the use of variational Gaussian process models that provide a tractable alternative to the conventional Gaussian process models for large datasets. Furthermore, we introduce the use of active learning to adaptively build and improve a conventional Gaussian process model predictive capability. Overall, we find that our approach provides accurate approximations of the wind-turbine wake flow field that can be queried at an orders-of-magnitude cheaper cost than those generated with high-fidelity physics-based simulations.

Deep neural networks↗
NASA NTRS2015

Evaluation of Fast-Time Wake Models Using Denver 2006 Field Experiment Data

The National Aeronautics and Space Administration conducted a series of wake vortex field experiments at Denver in 2003, 2005, and 2006. This paper describes the lidar wake vortex measurements and associated meteorological data collected during the 2006 deployment, and includes results of recent reprocessing of the lidar data using a new wake vortex algorithm and estimates of the atmospheric turbulence using a new algorithm to estimate eddy dissipation rate from the lidar data. The configuration and set-up of the 2006 field experiment allowed out-of-ground effect vortices to be tracked in lateral transport further than any previous campaign and thereby provides an opportunity to study long-lived wake vortices in moderate to low crosswinds. An evaluation of NASA's fast-time wake vortex transport and decay models using the dataset shows similar performance as previous studies using other field data.

Ahmad, Nash’at N.↗