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Implementation of a generalized actuator disk wind turbine model into the weather research and forecasting model for large-eddy simulation applications
1. M. Magnusson and A. S. Smedman, “Influence of atmospheric stability on wind turbine wakes,” Wind Eng. 18, 139–151 (1994).
2. R. J. Barthelmie, S. C. Pryor, S. T. Frandsen, K. S. Hansen, J. G. Schepers, K. Rados, W. Schlez, A. Neubert, L. E. Jensen, and S. Neckelmann, “Quantifying the impact of wind turbine wakes on power output at offshore wind farms,” J. Atmos. Ocean. Technol. 27(8), 1302–1317 (2010).
3. S. Wharton and J. K. Lundquist, “Atmospheric stability impacts on power curves of wind turbines: An analysis of a west coast North American wind farm,” Environ. Res. Lett. 7, 014005 (2012).
4. N. D. Kelley, “An initial overview of turbulence conditions seen at higher elevations over the Western Great Plains,” in Proceedings of Global Windpower 2004 Conference (Chicago, 2004).
5. N. Kelley, M. Shirazi, D. Jager, S. Wilde, J. Adams, M. Buhl, P. Sullivan, and E. Patton, “Lamar low-level jet program—interim report,” Report No. NREL/TP-500-34593, National Renewable Energy Laboratory, Golden, CO, 2004.
6. C. Sim, S. Basu, and L. Manuel, “The influence of stable boundary layer flows on wind turbine fatigue loads,” in Proceedings of the Aerospace Sciences Meeting AIAA (Orlando, 2009).
7. W. J. Shaw, J. K. Lundquist, and S. J. Schreck, “Workshop on research needs for wind resource characterization,” Bull. Am. Meteor. Soc. 90, 535–538 (2009).
8. B. Sanderse, S. P. van der Pijl, and B. Koren, “Review of computational fluid dynamics for wind turbine wake aerodynamics,” Wind Energy 14, 799–819 (2011).
9. F. J. Zajaczkowski, S. E. Haupt, and K. J. Schmehl, “A preliminary study of assimilating numerical weather prediction data into computational fluid dynamics models for wind prediction,” J. Wind Eng. Ind. Aerodyn. 99, 320–329 (2011).
10. R. B. Stull, An Introduction to Boundary Layer Meteorology (Kluwer Academic Publisher, Dordrecht, 1988).
11. W. Blumen, R. M. Banta, S. P. Burns, D. C. Fritts, R. Newsom, G. S. Poulos, and J. Sun, “Turbulence statistics of a Kelvin–Helmholtz billow event observed in the night-time boundary layer during the cooperative atmosphere–surface exchange study field program,” Dyn. Atmos. Oceans 34(2–4), 189–204 (2001).
12. J. Sun et al., “Atmospheric disturbances that generate intermittent turbulence in nocturnal boundary layers,” Boundary-Layer Meteorol. 110, 255–279 (2004).
13. N. D. Kelley, B. J. Jonkman, G. N. Scott, J. T. Bialasiewicz, and L. S. Redmond, “The impact of coherent turbulence on wind turbine aeroelastic response and its simulation,” in Proceedings of Wind Power 2005 Conference (Denver, 2005).
16. I. N. Smalikho, V. A. Banakh, Y. L. Pichugina, W. A. Brewer, R. M. Banta, J. K. Lundquist, and N. D. Kelley, “Lidar investigation of atmosphere effect on a wind turbine wake,” J. Atmos. Ocean. Technol. 30, 2554 (2013).
17. M. L. Aitken, R. M. Banta, Y. L. Pichugina, and J. K. Lundquist, “Quantifying wind turbine wake characteristics from scanning remote sensor data,” J. Atmos. Ocean. Technol. (in press).
18. M. J. Churchfield, S. Lee, J. Michalakes, and P. J. Moriarty, “A numerical study of the effects of atmospheric and wake turbulence on wind turbine dynamics,” J. Turbulence 13(14), N14 (2012).
19. J. Meyers and C. Meneveau, “Optimal turbine spacing in fully developed wind farm boundary layers,” Wind Energy 15, 305–317 (2012).
23. D. Rajewski, E. S. Takle, J. K. Lundquist, S. Oncley, J. H. Prueger, T. W. Horst, M. E. Rhodes, R. Pfeiffer, J. L. Hatfield, K. K. Spoth, and R. K. Doorenbos, “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. M. J. Churchfield, S. Lee, P. J. Moriarty, L. A. Martínez, S. Leonardi, G. Vijayakumar, and J. G. Brasseur, “A large-eddy simulation of wind-plant aerodynamics,” in Proceedings of the Aerospace Sciences Meeting AIAA (Nashville, 2012).
25. S. Lee, M. Churchfield, P. Moriiarty, J. Jonkman, and J. Michalakes, “Atmospheric and wake turbulence impacts on wind turbine fatigue loading,” in Proceedings of the Aerospace Sciences Meeting AIAA (Nashville, 2012).
26. P. P. Fleming, J.-W. Gebraad, S. Wingerden, S. Lee, M. Churchfield, A. Scholbrock, J. Michalakes, K. Johnson, and P. Moriarty, “The SOWFA super-controller: A high fidelity tool for evaluating wind plant control approaches,” in Proceedings of the European Wind Energy Association (Vienna, 2013).
27. W. C. Skamarock et al., “A description of the advanced research WRF version 3,” Report No. NCAR/TN-4751STR, National Center for Atmospheric Research, Boulder, CO, 2008.
28. C.-H. Moeng, J. Dudhia, J. B. Klemp, and P. P. Sullivan, “Examining two-way nesting for large eddy simulation of the PBL using the WRF model,” Mon. Wea. Rev. 135, 2295–2311 (2007).
29. J. D. Mirocha, J. K. Lundquist, and B. Kosović, “Implementation of a nonlinear subfilter turbulence stress model for large-eddy simulation in the Advanced Research WRF model,” Mon. Wea. Rev. 138, 4212–4228 (2010).
30. J. D. Mirocha, G. Kirkil, E. Bou-Zeid, F. K. Chow, and B. Kosović, “Transition and equilibration of neutral atmospheric boundary layer flow in one-way nested large eddy simulations using the weather research and forecasting model,” Mon. Wea. Rev. 141, 918–940 (2013).
31. G. Kirkil, J. D. Mirocha, F. K. Chow, and E. Bou-Zeid, “Implementation and evaluation of dynamic subfilter-scale stress models for large-eddy simulation using WRF,” Mon. Wea. Rev. 140, 266–284 (2012).
32. K. A. Lundquist, F. K. Chow, and J. K. Lundquist, “An immersed boundary method for the weather research and forecasting model,” Mon. Wea. Rev. 138, 796–817 (2010).
33. K. A. Lundquist, F. K. Chow, and J. K. Lundquist, “An immersed boundary method enabling large-eddy simulations of urban terrain in the WRF model,” Mon. Wea. Rev. 140, 3936–3955 (2012).
34. A. C. Fitch, J. B. Olson, J. K. Lundquist, J. Dudhia, A. K. Gupta, J. Michalakes, and I. Barstad, “Local and mesoscale impacts of wind farms as parameterized in a mesoscale NWP model,” Mon. Wea. Rev. 140(9), 3017–3038 (2012).
36. D. K. Lilly, “The representation of small-scale turbulence in numerical experiment,” in Proceedings of the IBM Scientific Computing Symposium on Environmental Sciences (New York, 1967), pp. 195–210.
38. J. W. Deardorff, “Three dimensional numerical study of turbulence in an entraining mixed layer,” Boundary-Layer Meteorol. 7, 199–226 (1974).
39. R. Mikkelsen, “Actuator disk methods applied to wind turbines,” Ph.D. dissertation (Technical University of Denmark, Copenhagen, 2003).
40. F. Porté-Agel, Y.-T. Wu, H. Lu, and R. J. Conzemius, “Large-eddy simulation of atmospheric boundary layer flow through wind turbines and wind farms,” J. Wind Eng. Ind. Aerodyn. 99, 154–168 (2011).
41. H. Glauert, “1963: Airplane propellers,” in Aerodynamic Theory, edited by W. F. Durand (Springer, New York, 1935), Vol. IV.
42. H. A. Madsen, “A CFD analysis of the actuator disc flow compared to momentum theory results,” in Proceedings of the 10th IEA Symposium on the Aerodynamics of Wind Turbines (Edinburgh, 1996), pp. 109–124.
44. J. N. Sørensen and R. Mikkelsen, “On the validity of the blade element momentum method,” in Proceedings of the European Wind Energy Conference Exhibition (Copenhagen, 2001), pp. 362–366.
45. J. Meyers and C. Meneveau, “Flow visualization using momentum and energy transport tubes and applications to turbulent flow in wind farms,” J. Fluid Mech. 715, 335–358 (2013).
46. C. Talbot, E. Bou-Zeid, and J. A. Smith, “Nested mesoscale-large eddy simulations with WRF: Performance in real test cases,” J. Hydrometeorol. 13(5), 1421–1441 (2012).
47. J. D. Mirocha, B. Kosović, and G. Kirkil, “Resolved turbulence characteristics in large-eddy simulations nested within mesoscale simulations using the weather research and forecasting model,” Mon. Wea. Rev. (in press).
48. M. Courtney, R. Wagner, and P. Lindelow, “Testing and comparison of lidars for profile and turbulence measurements in wind energy,” IOP Conf. Ser.: Earth Environ. Sci. 1, 012021 (2008).
49. M. L. Aitken, M. E. Rhodes, and J. K. Lundquist, “Performance of a wind-profiling lidar in the region of wind turbine rotor disks,” J. Atmos. Ocean. Technol. 29, 347–355 (2012).
52. W. Johnson and N. Kelley, “Design specifications for the development of the initial validation Software (Version 3.0) for processing of NWTC 80-meter meteorological tower data,” Report No. NREL/TP-500-27104, National Renewable Energy Laboratory, Golden, CO, 2000.
54. A. S. Monin and A. M. Obukhov, “Basic laws of turbulent mixing in the surface layer of the atmosphere,” Tr. Akad. Nauk SSSR Geofiz. Inst. 24, 163–187 (1959); English translation by John Miller, 1959.
55. S. P. Arya, Introduction to Micrometeorology, 2nd ed. (Academic Press, San Diego, 2001).
56. F. Bingöl, J. Mann, and G. C. Larsen, “Light detection and ranging measurements of wake dynamics part I: One-dimensional scanning,” Wind Energy 13(1), 51–61 (2010).
58. T. Burton, N. Jenkins, D. Sharpe, and E. Bossanyi, Wind Energy Handbook, 2nd ed. (Wiley, Chicester, 2011).
59. D. Wood, Small Wind Turbines: Analysis, Design and Application (Springer-Verlag, London, 2011).
60. R. C. Aster, B. Borchers, and C. H. Thurber, Parameter Estimation and Inverse Problems, 2nd ed. (Elsevier, 2013).
61. B. J. Rye and R. M. Hardesty, “Discrete spectral peak estimation in incoherent backscatter heterodyne lidar. I: Spectral accumulation and the Cramer-Rao lower bound,” IEEE Trans. Geosci. Remote Sens. 31, 16–27 (1993).
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A generalized actuator disk (GAD) wind turbine parameterization designed for large-eddy simulation (LES) applications was implemented into the Weather Research and Forecasting (WRF) model. WRF-LES with the GAD model enables numerical investigation of the effects of an operating wind turbine on and interactions with a broad range of atmospheric boundary layer phenomena. Numerical simulations using WRF-LES with the GAD model were compared with measurements obtained from the Turbine Wake and Inflow Characterization Study (TWICS-2011), the goal of which was to measure both the inflow to and wake from a 2.3-MW wind turbine. Data from a meteorological tower and two light-detection and ranging (lidar) systems, one vertically profiling and another operated over a variety of scanning modes, were utilized to obtain forcing for the simulations, and to evaluate characteristics of the simulated wakes. Simulations produced wakes with physically consistent rotation and velocity deficits. Two surface heat flux values of 20 W m−2 and 100 W m−2 were used to examine the sensitivity of the simulated wakes to convective instability. Simulations using the smaller heat flux values showed good agreement with wake deficits observed during TWICS-2011, whereas those using the larger value showed enhanced spreading and more-rapid attenuation. This study demonstrates the utility of actuator models implemented within atmospheric LES to address a range of atmospheric science and engineering applications. Validated implementation of the GAD in a numerical weather prediction code such as WRF will enable a wide range of studies related to the interaction of wind turbines with the atmosphere and surface.
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