scholarly journals Review of "Global Trends of Large Wind Farm Performance based on High Fidelity Simulations"

2020 ◽  
2020 ◽  
Vol 5 (4) ◽  
pp. 1689-1703
Author(s):  
Søren Juhl Andersen ◽  
Simon-Philippe Breton ◽  
Björn Witha ◽  
Stefan Ivanell ◽  
Jens Nørkær Sørensen

Abstract. A total of 18 high-fidelity simulations of large wind farms have been performed by three different institutions using various inflow conditions and simulation setups. The setups differ in how the atmospheric turbulence, wind shear and wind turbine rotors are modeled, encompassing a wide range of commonly used modeling methods within the large eddy simulation (LES) framework. Various turbine spacings, atmospheric turbulence intensity levels and incoming wind velocities are considered. The work performed is part of the International Energy Agency (IEA) wind task Wakebench and is a continuation of previously published results on the subject. This work aims at providing a methodology for studying the general flow behavior in large wind farms in a systematic way. It seeks to investigate and further understand the global trends in wind farm performance, with a focus on variability. Parametric studies first map the effect of various parameters on large aligned wind farms, including wind turbine spacing, wind shear and atmospheric turbulence intensity. The results are then aggregated and compared to engineering models as well as LES results from other investigations to provide an overall picture of how much power can be extracted from large wind farms operating below the rated level. The simple engineering models, although they cannot capture the variability features, capture the general trends well. Response surfaces are constructed based on the large number of aggregated LES data corresponding to a wide range of large wind farm layouts. The response surfaces form a basis for mapping the inherently varying power characteristics inside very large wind farms, including how much the turbines are able to exploit the turbulent fluctuations within the wind farms and estimating the associated uncertainty, which is valuable information useful for risk mitigation.


2020 ◽  
Author(s):  
Søren Juhl Andersen ◽  
Simon-Philippe Breton ◽  
Björn Witha ◽  
Stefan Ivanell ◽  
Jens Nørkær Sørensen

Abstract. A total of 18 high fidelity simulations of large wind farms have been performed by three different institutions using various inflow conditions and simulation setups. The setups differ in how the atmospheric turbulence, wind shear and wind turbine rotors are modelled, encompassing a wide range of commonly used modelling methods within the LES framework. Various turbine spacings, atmospheric turbulence intensity levels and incoming wind velocities are considered. The work performed is part of the International Energy Agency(IEA) wind task Wakebench, and is a continuation of previously published results on the subject. This work aims at providing a methodology for studying the general flow behavior in large wind farms in a systematic way. It seeks to investigate and further understand the global trends of wind farm performance, with a focus on variability. Parametric studies first map the effect of various parameters on large aligned wind farms, including wind turbine spacing, wind shear and atmospheric turbulence intensity. The results are then aggregated and compared to engineering models as well as LES results from other investigations to provide an overall picture of how much power can be extracted from large wind farms operating below rated level. The simple engineering models, although they cannot capture the variability features, capture the general trends well. Response surfaces are constructed based on the large amount of aggregated LES data corresponding to a wide range of large wind farm layouts. The response surfaces form a basis for mapping the inherently varying power characteristics inside very large wind farm, including how much the turbines are able to exploit the turbulent fluctuations within the wind farms and estimating the associated uncertainty, which is valuable information useful for risk mitigation.


2021 ◽  
Vol 281 ◽  
pp. 116115
Author(s):  
Xiaolei Yang ◽  
Christopher Milliren ◽  
Matt Kistner ◽  
Christopher Hogg ◽  
Jeff Marr ◽  
...  

2021 ◽  
Author(s):  
Gregor Giebel ◽  
Tuhfe Göçmen ◽  
Jakob Mann ◽  
Anna Maria Sempreviva ◽  
Haakon Lund ◽  
...  

<p>TRAIN<sup>2</sup>WIND is a PhD TRAINing school analysing enTRAINment in offshore WIND farms with computer models and experiments. By its very nature, a wind turbine extracts energy from the wind, which is replenished from the wind field on the sides and above due to the ambient turbulence. However, offshore the turbulence is lower, and wind farms are typically larger than onshore, therefore the wind can only be replenished from above in a process called entrainment. Train<sup>2</sup>Wind will investigate the entrainment process using advanced high-resolution computer modelling and wind tunnel models together with measurements of the wind field above, in and downstream of large wind farms, using lidars, radars, satellites and Unmanned Aerial Systems.</p><p>Besides the natural science package, one humanities PhD student at the University of Copenhagen will investigate the collaboration between the researchers from a social science and collaboration tools perspective.</p><p>The main work is done during the education of 18 fellows, where 13 embark on a PhD, while the other ones are employed for one year. The students will work with high-fidelity numerical simulations, lidars, unmanned aerial systems, wind tunnels and satellite data in order to understand entrainment of new momentum in very large wind farms. This changes the atmospheric boundary layer over a very extended wind farm, which becomes a wind turbine array boundary layer. The resulting change in wind resource is the main object of interest. The main planned activity is an experimental campaign at a major cluster of wind farms, probably in the North Sea. Another activity revolves around vertical axis turbines and their significantly different wake pattern, a potential mitigation measure.</p><p>So far we recruited the fellows and started with the simulations and the development of the hardware. We intend to employ a vertical take-off and landing model plane with a wing span of about 2m, which would allow to start and land from a helicopter pad offshore, and after the vertical start enjoy the advantage of a winged plane and its much larger range and endurance. Another instrument is a hexacopter mounted with a sonic anemometer, which allows to sample in a single point much akin a conventional met mast, but at any given point in or above a large wind farm. Lidar usage and development is part of the project as well, with a floating lidar in Bergen University and long-range lidars at DTU.</p><p>There are three numerical codes used in Train<sup>2</sup>Wind: Ellipsys3D, a Large Eddy Simulation (LES) high-fidelity code from DTU, WIRE-LES, another LES code from EPFL, and the Weather Research and Forecasting model run at DTU.</p><p>The outcome of the project is more knowledge of the entrainment process, and a guidance on how close to position clusters of wind farms in order not to exhaust the wind resource. The talk will give an overview of the project, highlighting the challenges.</p>


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Kai Liu ◽  
Majid Allahyari ◽  
Jorge S. Salinas ◽  
Nadim Zgheib ◽  
S. Balachandar

AbstractHigh-fidelity simulations of coughs and sneezes that serve as virtual experiments are presented, and they offer an unprecedented opportunity to peer into the chaotic evolution of the resulting airborne droplet clouds. While larger droplets quickly fall-out of the cloud, smaller droplets evaporate rapidly. The non-volatiles remain airborne as droplet nuclei for a long time to be transported over long distances. The substantial variation observed between the different realizations has important social distancing implications, since probabilistic outlier-events do occur and may need to be taken into account when assessing the risk of contagion. Contrary to common expectations, we observe dry ambient conditions to increase by more than four times the number of airborne potentially virus-laden nuclei, as a result of reduced droplet fall-out through rapid evaporation. The simulation results are used to validate and calibrate a comprehensive multiphase theory, which is then used to predict the spread of airborne nuclei under a wide variety of ambient conditions.


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