Characterizing the Coherent Structures Within and Above Large Wind Farms
We study large-eddy simulations of coherent structures within and above different wind-farm configurations in a neutral atmospheric boundary layer (ABL) using proper orthogonal decomposition (POD) to improve understanding of the flow structures in both physical and spectral space. We find that the s...
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Veröffentlicht in: | Boundary-layer meteorology 2020, Vol.174 (1), p.61-80 |
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Sprache: | eng |
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Zusammenfassung: | We study large-eddy simulations of coherent structures within and above different wind-farm configurations in a neutral atmospheric boundary layer (ABL) using proper orthogonal decomposition (POD) to improve understanding of the flow structures in both physical and spectral space. We find that the spanwise extent of elongated streamwise counter-rotating rolls is constrained by the spanwise turbine spacings. The very large streamwise extent of the observed POD flow structures in physical space indicates that the interaction between the wind turbines and the ABL also causes large-scale flow organization. Using a spectral POD analysis to characterize the coherent structures at a certain frequency, we find that the flow dynamics for the frequency corresponding to the time a fluid parcel takes to traverse one streamwise turbine spacing is dominated by the wind-turbine wakes. The first POD mode at this frequency indicates that the wakes are spatially correlated. However, the flow dynamics at lower frequencies, corresponding to the longer time a fluid parcel takes to traverse the entire wind farm, are dominated by large flow structures originating from the ABL dynamics. We find that hundreds of POD modes are required to accurately describe the full three-dimensional flow profiles in large wind farms, while even more POD modes are required to accurately describe the Reynolds stresses that are important to describe the momentum exchange between the wind farm and the ABL. This indicates that wind-farm dynamics in the ABL are very complex. |
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ISSN: | 0006-8314 1573-1472 |
DOI: | 10.1007/s10546-019-00468-x |