High-fidelity simulations and field measurements for characterizing wind fields in a utility-scale wind farm

2021 ◽  
Vol 281 ◽  
pp. 116115
Author(s):  
Xiaolei Yang ◽  
Christopher Milliren ◽  
Matt Kistner ◽  
Christopher Hogg ◽  
Jeff Marr ◽  
...  
2021 ◽  
Author(s):  
Peter Andreas Brugger ◽  
Corey D. Markfort ◽  
Fernando Porté-Agel

Abstract. Wake meandering is a low-frequency oscillation of the entire wind turbine wake that can contribute to power and load fluctuations of downstream turbines in wind farms. Field measurements of two Doppler LiDARs mounted on the nacelle of a utility-scale wind turbine were used to investigate relationships between the inflow and the wake meandering as well as the effect of wake meandering on the temporally averaged wake. A correlation analysis showed a linear relationship between the instantaneous wake position and the lateral velocity that degraded with the evolution of the turbulent wind field during the time of downstream advection. A low-pass filter proportional to the advection time delay is recommended to remove small scales that become decorrelated even for distances within the typical spacing of wind turbine rows in a wind farm. The results also showed that the velocity at which wake meandering is transported downstream was slower than the inflow wind speed, but faster than the velocity at the wake center. This indicates that the modelling assumption of the wake as an passive scalar should be revised in the context of the downstream advection. Further, the strength of wake meandering increased linearly with the turbulence intensity of the lateral velocity and with the downstream distance. Wake meandering reduced the maximum velocity deficit of the temporally averaged wake and increased its width. Both effects scaled with the wake meandering strength. Lastly, we found that the fraction of the wake turbulence intensity that was caused by wake meandering decreased with downstream distance contrary to the wake meandering strength.


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.


2020 ◽  
Vol 12 (6) ◽  
pp. 2467 ◽  
Author(s):  
Fei Zhao ◽  
Yihan Gao ◽  
Tengyuan Wang ◽  
Jinsha Yuan ◽  
Xiaoxia Gao

To study the wake development characteristics of wind farms in complex terrains, two different types of Light Detection and Ranging (LiDAR) were used to conduct the field measurements in a mountain wind farm in Hebei Province, China. Under two different incoming wake conditions, the influence of wind shear, terrain and incoming wind characteristics on the development trend of wake was analyzed. The results showed that the existence of wind shear effect causes asymmetric distribution of wind speed in the wake region. The relief of the terrain behind the turbine indicated a subsidence of the wake centerline, which had a linear relationship with the topography altitudes. The wake recovery rates were calculated, which comprehensively validated the conclusion that the wake recovery rate is determined by both the incoming wind turbulence intensity in the wake and the magnitude of the wind speed.


2009 ◽  
Vol 46 (5) ◽  
pp. 903-922 ◽  
Author(s):  
Miguel R. Visbal ◽  
Raymond E. Gordnier ◽  
Marshall C. Galbraith

2019 ◽  
Vol 120 ◽  
pp. 103099 ◽  
Author(s):  
Akash Dhruv ◽  
Elias Balaras ◽  
Amir Riaz ◽  
Jungho Kim

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