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Investigating wind turbine impacts on near-wake flow using profiling lidar data and large-eddy simulations with an actuator disk model
1. Aitken, M. L. , Banta, R. M. , Pichugina, Y. , and Lundquist, J. K. , “ Quantifying wind turbine wake characteristics from scanning remote sensor data,” J. Atmos. Oceanic Technol. 31, 765–787 (2014a).
2. Aitken, M. L. , Kosović, B. , Mirocha, J. D. , and Lundquist, J. K. , “ Large eddy simulation of wind turbine wake dynamics in the stable boundary layer using the weather research and forecasting model,” J. Renewable Sustainable Energy 6, 033137 (2014b).
3. Aitken, M. L. and Lundquist, J. K. , “ Utility-scale wind turbine wake characterization using nacelle-based long-range scanning lidar,” J. Atmos. Oceanic Technol. 31, 1529–1539 (2014).
4. Aitken, M. L. , Rhodes, M. E. , and Lundquist, J. K. , “ Performance of a wind-profiling lidar in the region of wind turbine rotor disks,” J. Atmos. Oceanic Technol. 29, 347–355 (2012).
5. Arya, S. P. , Introduction to Micrometeorology, 2nd ed. ( Academic Press, San Diego, 2001).
6. Barthelmie, R. J. , Folkerts, L. , Ormel, T. , Sanderhoff, P. , Eecen, P. J. , Stobbe, O. , and Nielsen, N. M. , “ Offshore wind turbine wakes measured by sodar,” J. Atmos. Oceanic Technol. 20, 466–477 (2003).
7. Basu, S. , Holtslag, A. A. M. , Van de Wiel, B. J. H. , Moene, A. F. , and Steeneveld, G.-J. , “ An inconvenient ‘truth’ about using sensible heat flux as a surface boundary condition in models under stably stratified regimes,” Acta Geophys. 56, 88–99 (2008).
8. Churchfield, M. , Draxl, C. , and Mirocha, J. D. , “ A one-way meso-microscale coupling strategy for realistic wind plant aerodynamics large-eddy simulation,” J. Renewable Sustainable Energy (submitted).
9. Churchfield, M. J. , Lee, S. , Michalakes, J. , and Moriarty, P. J. , “ A numerical study of the effects of atmospheric and wake turbulence on wind turbine dynamics,” J. Turbul. 13(14), N14 (2012).
11. Hansen, K. S. , Barthelmie, R. J. , Jensen, L. E. , and Sommer, A. , “ The impact of turbulence intensity and atmospheric stability on power deficits due to wind turbine wakes at Horns Rev wind farm,” Wind Energy 15, 183–196 (2012).
12. Hirth, B. M. , Schroeder, J. L. , Gunter, S. , and Guynes, J. , “ Measuring a utility scale turbine wake using the TTUKa mobile research radars,” J. Atmos. Oceanic Technol. 29, 765–771 (2012).
13. Högström, U. , Asimakopoulos, D. N. , Kambezidis, H. , Helmis, C. G. , and Smedman, A. , “ A field study of the wake behind a 2 MW wind turbine,” Atmos. Environ. 22, 803–820 (1988).
13. Holton, J. R. , An Introduction to Dynamic Meteorology, Third Edition ( Academic Press, 1992).
14. Lilly, D. K. , “ The representation of small-scale turbulence in numerical experiment,” Proceedings of the IBM Scientific Computing Symposium on Environmental Sciences ( IBM, New York, 1967), pp. 195–210.
15. Lundquist, J. K. , Churchfield, M. , Lee, S. , and Clifton, A. , “ Quantifying error of lidar and sodar Doppler beam swinging measurements of wind turbine wakes using computational fluid dynamics,” Atmos. Meas. Tech. 8, 907–920 (2015).
16. Magnusson, M. and Smedman, A. , “ Influence of atmospheric stability on wind turbine wakes,” Wind Eng. 18(3), 139–152 (1994).
18. Mehta, D. , van Zuijlen, A. H. , Koren, B. , Holierhoek, J. G. , and Bijl, H. , “ Large eddy simulation of wind farm aerodynamics: A review,” J. Wind Eng. Ind. Aerodyn. 133, 1–17 (2014).
19. Mirocha, J. D. , Kosović, B. , Aitken, M. L. , and Lundquist, J. K. , “ Implementation of a generalized actuator disk wind turbine model into the weather research and forecasting model for large-eddy simulation applications,” J. Renewable Sustainable Energy 6, 013104 (2014).
20. Mirocha, J. D. , Lundquist, J. K. , and Kosović, B. , “ Implementation of nonlinear subfilter turbulence stress models for large-eddy simulations in the advanced research WRF model,” Mon. Weather Rev. 138, 4212–4228 (2010).
21. Monin, A. S. and Obukhov, A. M. , “ Basic laws of turbulent mixing in the surface layer of the atmosphere,” Tr. -Akad. Nauk SSSR Geofiz. Inst. 24, 163–187 (1954), English translation by John Miller, 1959.
22. Nygaard, N. G. , “ Lidar wake measurements in an onshore wind farm,” in VindKraftNet: Remote Sensing Workshop, Roskilde, Denmark, 2011.
23. Rajewski, D. A. , Takle, E. S. , Lundquist, J. K. , Oncley, S. , Prueger, J. H. , Horst, T. W. , Rhodes, M. E. , Pfeiffer, R. , Hatfield, J. L. , Spoth, K. K. , and Doorenbos, R. K. , “ Crop wind energy experiment (CWEX): Observations of surface-layer, boundary layer, and mesoscale interactions with a wind farm,” Bull. Am. Meteorol. Soc. 94, 655–672 (2013).
24. Rajewski, D. A. , Takle, E. S. , Lundquist, J. K. , Prueger, J. H. , Pfeiffer, R. L. , Hatfield, J. L. , Spoth, K. K. , and Doorenbos, R. K. , “ Changes in fluxes of heat, H2O, and CO2 caused by a large wind farm,” Agric. For. Meteorol. 194, 175–187 (2014).
25. Rhodes, M. E. and Lundquist, J. K. , “ The effect of wind turbine wakes on summertime midwest atmospheric wind profiles,” Boundary-Layer Meteorol. 149, 85–103 (2013).
26. Sanderse, B. , van der Pijl, S. P. , and Koren, B. , “ Review of computational fluid dynamics for wind turbine wake aerodynamics,” Wind Energy 14, 799–819 (2011).
27. Sathe, A. , Mann, J. , Gottschall, J. , and Courtney, M. , “ Can wind lidars measure turbulence?,” J. Atmos. Oceanic Technol. 28(7), 853–868 (2011).
30. Skamarock, W. C. et al., “ A description of the advanced research WRF version 3,” Report No. NCAR/TN-4751STR, National Center for Atmospheric Research, Boulder, CO, 2008.
31. Smalikho, I. N. , Banakh, V. A. , Pichugina, Y. L. , Brewer, W. A. , Banta, R. M. , Lundquist, J. K. , and Kelley, N. D. , “ Lidar investigation of atmosphere effect on a wind turbine wake,” J. Atmos. Oceanic Technol. 30, 2554–2570 (2013).
33. Troldborg, N. , Larsen, G. C. , Madsen, H. A. , Hansen, K. S. , Sørensen, J. N. , and Mikkelsen, R. , “ Numerical simulations of wake interaction between two wind turbines at various inflow conditions,” Wind Energy 14, 859–876 (2011).
33. Vanderwende, B. , Lundquist, J. K. , Rhodes, M. E. , Takle, G. S. , and Purdy, S. I. , “ Observing and simulating the summertime low-level jet in central Iowa,” Mon. Weather Rev. 143, 2319–2336 (2015).
35. Walton, R. A. , Takle, E. S. , and Gallus, W. A. , Jr., “ Characteristics of 50–200-m winds and temperatures derived from an Iowa Tall Tower Network,” J. Appl. Meteorol. Climatol. 53, 2387–2393 (2014).
36. Wu, Y. T. and Porté-Agel, F. , “ Large-eddy simulation of wind-turbine wakes: Evaluation of turbine parametrisations,” Boundary-Layer Meteorol. 138, 345–366 (2011).
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Wind turbine impacts on the atmospheric flow are investigated using data from the Crop Wind Energy Experiment (CWEX-11) and large-eddy simulations (LESs) utilizing a generalized actuator disk (GAD) wind turbine
model. CWEX-11 employed velocity-azimuth display (VAD) data from two Doppler lidar systems to sample vertical profiles of flow parameters across the rotor depth both upstream and in the wake of an operating 1.5 MW wind turbine.
Lidar and surface observations obtained during four days of July 2011 are analyzed to characterize the turbine impacts on wind speed and flow variability, and to examine the sensitivity of these changes to atmospheric stability. Significant velocity deficits (
) are observed at the downstream location during both convective and stable portions of four diurnal cycles, with large, sustained deficits occurring during stable conditions. Variances of the streamwise velocity component,
, likewise show large increases downstream during both stable and unstable conditions, with stable conditions supporting sustained small increases of
, while convective conditions featured both larger magnitudes and increased variability, due to the large coherent structures in the background flow. Two representative case studies, one stable and one convective, are simulated using LES with a GAD model at 6 m resolution to evaluate the compatibility of the simulation framework with validation using vertically profiling lidar data in the near wake region. Virtual lidars were employed to sample the simulated flow field in a manner consistent with the VAD technique. Simulations reasonably reproduced aggregated wake
characteristics, albeit with smaller magnitudes than observed, while
values in the wake are more significantly underestimated. The results illuminate the limitations of using a GAD in combination with coarse model resolution in the simulation of near wake physics, and validation thereof using VAD data.
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