An official website of the United States government
Abstract A field experiment was conducted to investigate the effects of the thrust force induced by utility-scale wind turbines on the incoming wind field. Five wind profiling LiDARs and a scanning Doppler pulsed wind LiDAR were deployed in the proximity of a row of four wind turbines located over relatively flat terrain, both before and after the construction of the wind farm. The analysis of the LiDAR data collected during the pre-construction phase enables quantifying the wind map of the site, which is then leveraged to correct the post-construction LiDAR data and isolate rotor-induced effects on the incoming wind field. The analysis of the profiling LiDAR data allows for the identification of the induction zone upstream of the turbine rotors, with an increasing velocity deficit moving from the top tip towards the bottom tip of the rotor. The largest wind speed reduction (about 5%) is observed for convective conditions and incoming hub-height wind speed between cut-in and rated wind speeds. The scanning LiDAR data indicate the presence of speedup regions within the gaps between adjacent turbine rotors. Speedup increases with reducing the transverse distance between the rotors, atmospheric instability (maximum 15%), while a longer streamwise extent of the speedup region is observed under stable atmospheric conditions.
more »
« less
Data for: "Lizards in the wind: The impact of wind on the thermoregulation of the common wall lizard"
The following are the data and code that supplied the figures, results, and tables from "Lizards in the wind: The impact of wind on the thermoregulation of the common wall lizard" by Spears et. al., 2024. Published in the Journal of Thermal Biology.
more »
« less
- Award ID(s):
- 2217826
- PAR ID:
- 10512901
- Publisher / Repository:
- Mendeley Data
- Date Published:
- Subject(s) / Keyword(s):
- Evolutionary Biology Animal Behavior Body Temperature Regulation Hydration Markov Chain Modeling Thermoregulation in Animals
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Synopsis Wind can significantly influence heat and water exchange between organisms and their environment, yet microclimatic variation in wind is often overlooked in models forecasting the effects of environmental change on organismal performance. Accounting for the effects of wind may become even more critical given the anticipated changes in wind speed across the planet as climates continue to warm. In this study, we first assessed how wind speed varies across the planet and how wind speed may change under climate warming at macroclimatic scales. We also used microclimatic data to assess how wind speed changes temporally throughout the day and year as well as the relationship between wind speed, temperature, and standard deviation in each environmental variable using data from weather stations in North America. Finally, we used a suite of biophysical simulations to understand how wind speed (and its interactions with other environmental variables and organismal traits) affects the temperatures and rates of water loss that plants and animals experience at a microclimatic scale. We found substantial latitudinal variation in wind speed and the change in wind speed under climate change, demonstrating that temperate regions are predicted to experience simultaneous warming and reductions in wind speed. From the microclimatic data, we also found that wind speed is positively associated with temperature and temperature variability, indicating that the effects of wind speed may become more challenging to predict under future warming scenarios. The biophysical simulations demonstrated that convective and evaporative cooling from wind interacts strongly with organismal traits (such as body size, solar absorptance, and conductance) and the heating effects of solar radiation to shape heat and water fluxes in terrestrial plants and animals. In many cases, the effect of wind (or its interaction with other variables) was comparable to the effects of air temperature or solar radiation. Understanding these effects will be important for predicting the ecological impacts of climate change and for explaining clinal variation in traits that have evolved across a range of thermal environments.more » « less
-
null (Ed.)Abstract Floating offshore wind turbines hold great potential for future solutions to the growing demand for renewable energy production. Thereafter, the prediction of the offshore wind power generation became critical in locating and designing wind farms and turbines. The purpose of this research is to improve the prediction of the offshore wind power generation by the prediction of local wind speed using a Deep Learning technique. In this paper, the future local wind speed is predicted based on the historical weather data collected from National Oceanic and Atmospheric Administration. Then, the prediction of the wind power generation is performed using the traditional methods using the future wind speed data predicted using Deep Learning. The network layers are designed using both Long Short-Term Memory (LSTM) and Bi-directional LSTM (BLSTM), known to be effective on capturing long-term time-dependency. The selected networks are fine-tuned, trained using a part of the weather data, and tested using the other part of the data. To evaluate the performance of the networks, a parameter study has been performed to find the relationships among: length of the training data, prediction accuracy, and length of the future prediction that is reliable given desired prediction accuracy and the training size.more » « less
-
null (Ed.)Floating offshore wind turbines hold great potential for future solutions to the growing demand for renewable energy production. Thereafter, the prediction of the offshore wind power generation became critical in locating and designing wind farms and turbines. The purpose of this research is to improve the prediction of the offshore wind power generation by the prediction of local wind speed using a Deep Learning technique. In this paper, the future local wind speed is predicted based on the historical weather data collected from National Oceanic and Atmospheric Administration. Then, the prediction of the wind power generation is performed using the traditional methods using the future wind speed data predicted using Deep Learning. The network layers are designed using both Long Short-Term Memory (LSTM) and Bi-directional LSTM (BLSTM), known to be effective on capturing long-term time-dependency. The selected networks are fine-tuned, trained using a part of the weather data, and tested using the other part of the data. To evaluate the performance of the networks, a parameter study has been performed to find the relationships among: length of the training data, prediction accuracy, and length of the future prediction that is reliable given desired prediction accuracy and the training size.more » « less
-
ABSTRACT Seismic data contains a continuous record of wind influenced by different factors across the frequency spectrum. To assess the influences of wind on ground motion, we use colocated wind and seismic data from 110 stations in the Alaska component of the EarthScope Transportable Array. We compare seismic probability power spectral densities and wind speed and direction during 2018 to develop a quantitative measure of the seismic sensitivity to wind. We observe a pronounced increase in seismic energy as a function of wind speed for almost all stations. At frequencies below the microseism band, our observations agree with previous authors in finding that sensor emplacement and ground materials are important, and that much of the wind influence likely comes from associated changes in barometric pressure. Wind has the least influence in the microseism band, but that is only because its contribution to noise is much smaller than the ubiquitous microseism background. At frequencies above the microseism band, we find that wind sensitivity is correlated with land cover type, increasing with vegetation height. This sensitivity varies seasonally, which we attribute to snow insulation, the burial of vegetation and objects around the station, and potentially the role of frozen ground. Wind direction also manifests in seismic data, which we attribute to turbulent air on the lee side of station huts coupling with the ground and the seismometer borehole cap. We find some dependence on bedrock type, with a greater seismic response in unconsolidated sediment. These results provide guidance on site selection and construction, and make it possible to forecast seismic network performance under different wind conditions. When we examine the factors at work in a warming climate, we find reason to anticipate increasing seismic noise from wind in the Arctic over the decades to come.more » « less
