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Volume 53 Issue 12
Dec.  2021
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Yang Xiaolei. Review of research on the simulation method and flow mechanism of a single horizontal-axis wind turbine wake. Chinese Journal of Theoretical and Applied Mechanics, 2021, 53(12): 3169-3178. DOI: 10.6052/0459-1879-21-493
Citation: Yang Xiaolei. Review of research on the simulation method and flow mechanism of a single horizontal-axis wind turbine wake. Chinese Journal of Theoretical and Applied Mechanics, 2021, 53(12): 3169-3178. DOI: 10.6052/0459-1879-21-493
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REVIEW OF RESEARCH ON THE SIMULATION METHOD AND FLOW MECHANISM OF A SINGLE HORIZONTAL-AXIS WIND TURBINE WAKE

  • LNM, Institute of Mechanics, Chinese Academy of Sciences, Beijing 100190, China

  • College of Engineering Sciences, Chinese Academy of Sciences, Beijing 100049, China

  • Received Date: September 04, 2021
  • Accepted Date: November 03, 2021
  • Available Online: November 03, 2021
    Graphical Abstract
    • Abstract
  • In order to achieve the "3060" target of carbon peak and carbon neutrality, wind power will play an important role in our nation's energy system. The wake of a wind turbine is a key factor that affects the performance and levelized cost of wind power. It needs to be fully considered in the layout and control design of the wind turbine. This article first introduces the computational methods of wind turbine wakes, including analytical models, low-order models, large-eddy simulation, and methods for generating inflow turbulence. Analytical models and low-order models can compute wind turbine wakes fast, but they rely on model parameters and cannot or cannot accurately predict the turbulence characteristics of wind turbine wakes. Large-eddy simulation with parameterized models for wind turbines can accurately predict turbulence characteristics such as wake meandering. It is a powerful tool for investigating wake mechanism and can provide data and theoretical support for development of fast prediction models. Next, the article introduces the tip vortices, hub vortex and wake meandering and discusses their mechanism. For turbulent inflows, tip vortices mainly exist in the near wake. Meandering is the main feature of the far wake, which affects the characteristics of inflow for downstream wind turbines. There are two mechanisms for wake meandering: large-scale eddies of the incoming flow and shear layer instability. Numerical and observation results show that the two mechanisms coexist. The nacelle and the hub vortex have an important influence on wake meandering. Using the actuation surface model of the blade and the nacelle can accurately predict wake meandering. Research has shown that the turbulence characteristics for different designs of wind turbines are similar, which provides a theoretical basis for the development of a fast prediction model for wake turbulence. Current research efforts have been focused on wind turbine wakes on flat terrain. The mechanism of the atmospheric turbulence and wind turbine wake in complex terrain and marine environments are complex, which cannot be accurately predicted using the existing engineering models and needs further in-depth research.
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  • [1]
    Li J, He D. Strategies of Sustainable Development in China’s Wind Power Industry. Beijing: Science Press, Springer, 2020: 1-396
    [2]
    国家发改委能源研究所. 2020年中国可再生能源展望报告. http://www.chinapower.com.cn/xw/zyxw/20210129/49044.html.2021-1-29

    The Energy Research Institute of the National Development and Reform Commission of China. China renewable energy outlook 2020. http://www.chinapower.com.cn/xw/zyxw/20210129/49044.ht-ml.2021-1-29 (in Chinese)
    [3]
    Veers P, Dykes K, Lantz E, et al. Grand challenges in the science of wind energy. Science, 2019, 366(6464): eaau2027 doi: 10.1126/science.aau2027
    [4]
    Barthelmie RJ, Hansen K, Frandsen S, et al. Modelling and measuring flow and wind turbine wakes in large wind farms offshore. Wind Energy, 2009, 12(5): 431-444 doi: 10.1002/we.348
    [5]
    El−Asha S, Zhan L, Iungo GV. Quantification of power losses due to wind turbine wake interactions through SCADA, meteorological and wind LiDAR data. Wind Energy, 2017, 20(11): 1823-1839 doi: 10.1002/we.2123
    [6]
    Burton T, Jenkins N, Sharpe D, et al. Wind Energy Handbook. West Sussex: John Wiley & Sons, 2011: 1-742
    [7]
    Nielsen JJ, Sørensen JD. On risk-based operation and maintenance of offshore wind turbine components. Reliability Engineering & System Safety, 2011, 96(1): 218-229
    [8]
    Orlanski I. A rational subdivision of scales for atmospheric processes. Bulletin of the American Meteorological Society, 1975, 56(5): 527-530 doi: 10.1175/1520-0477-56.5.527
    [9]
    Yang X, Sotiropoulos F. A new class of actuator surface models for wind turbines. Wind Energy, 2018, 21(5): 285-302 doi: 10.1002/we.2162
    [10]
    Jensen N. A note on wind generator interaction. Risø National Laboratory, 1983, Risø-M-2411
    [11]
    Frandsen S. On the wind speed reduction in the center of large clusters of wind turbines. Journal of Wind Engineering and Industrial Aerodynamics, 1992, 39(1-3): 251-265 doi: 10.1016/0167-6105(92)90551-K
    [12]
    Bastankhah M, Porté-Agel F. A new analytical model for wind-turbine wakes. Renewable Energy, 2014, 70: 116-123 doi: 10.1016/j.renene.2014.01.002
    [13]
    Ge M, Wu Y, Lin Y, et al. A two-dimensional model based on the expansion of physical wake boundary for wind-turbine wakes. Applied Energy, 2019, 233: 975-984
    [14]
    Ge M, Wu Y, Lin Y, et al. A two-dimensional Jensen model with a Gaussian-shaped velocity deficit. Renewable Energy, 2019, 141: 46-56 doi: 10.1016/j.renene.2019.03.127
    [15]
    Calaf M, Meneveau C, Meyers J. Large eddy simulation study of fully developed wind-turbine array boundary layers. Physics of Fluids, 2010, 22(1): 015110 doi: 10.1063/1.3291077
    [16]
    Meneveau C. The top-down model of wind farm boundary layers and its applications. J. Turbul., 2012, 13: N7 doi: 10.1080/14685248.2012.663092
    [17]
    Yang X, Kang S, Sotiropoulos F. Computational study and modeling of turbine spacing effects in infinite aligned wind farms. Physics of Fluids, 2012, 24(11): 115107 doi: 10.1063/1.4767727
    [18]
    Frandsen S, Barthmie R, Pryor S, et al. Analytical modelling of wind speed deficit in large offshore wind farms. Wind Energy, 2006, 9: 39-53 doi: 10.1002/we.189
    [19]
    Rothmann O, Frandsen S, Barthmie RJ, et al. Wake modelling for intermediate and large wind farms// European Wind Energy Association Conference and Exhibition, Milan, Italy, 2007
    [20]
    Yang X, Sotiropoulos F. Analytical model for predicting the performance of arbitrary size and layout wind farms. Wind Energy, 2016, 19(7): 1239-1248 doi: 10.1002/we.1894
    [21]
    Stevens RJ, Gayme DF, Meneveau C. Coupled wake boundary layer model of wind-farms. Journal of Renewable and Sustainable Energy, 2015, 7(2): 023115 doi: 10.1063/1.4915287
    [22]
    Zhang H, Ge M, Liu Y, et al. A new coupled model for the equivalent roughness heights of wind farms. Renewable Energy, 2021, 171: 34-46 doi: 10.1016/j.renene.2021.02.076
    [23]
    Ainslie JF. Calculating the flowfield in the wake of wind turbines. Journal of Wind Engineering and Industrial Aerodynamics, 1988, 27(1-3): 213-224 doi: 10.1016/0167-6105(88)90037-2
    [24]
    Schlichting H, Gersten K. Boundary-layer Theory. Berlin Heidelberg: Springer, 2016: 1-805
    [25]
    Larsen GC, Aagaard MH, Bingöl F. Dynamic wake meandering modeling. Risø National Laboratory, 2007, Risø-R-1607
    [26]
    Madsen HA, Larsen GC, Larsen TJ, et al. Calibration and validation of the dynamic wake meandering model for implementation in an aeroelastic code. Journal of Solar Energy Engineering, 2010, 132: 041014 doi: 10.1115/1.4002555
    [27]
    He G, Jin G, Yang Y. Space-time correlations and dynamic coupling in turbulent flows. Annual Review of Fluid Mechanics, 2017, 49: 51-70 doi: 10.1146/annurev-fluid-010816-060309
    [28]
    Brunton SL, Noack BR, Koumoutsakos P. Machine learning for fluid mechanics. Annual Review of Fluid Mechanics, 2020, 52: 477-508 doi: 10.1146/annurev-fluid-010719-060214
    [29]
    Yang X. Towards the development of a wake meandering model based on neural networks. Journal of Physics: Conference Series, 2020, 1618(6): 062026
    [30]
    Li Y, Çalışal SM. A discrete vortex method for simulating a stand-alone tidal-current turbine: Modeling and validation. Journal of offshore Mechanics and Arctic Engineering, 2010, 132(3): 031102 doi: 10.1115/1.4000499
    [31]
    Länger ‐ Möller A. Impact of wall roughness and turbulence level on the performance of a horizontal axis wind turbine with the U‐RANS solver THETA. Wind Energy, 2019, 22(4): 523-537 doi: 10.1002/we.2304
    [32]
    Cao H, Zhang M, Zhang Y, et al. A general model for trailing edge serrations simulation on wind turbine airfoils. Theoretical and Applied Mechanics Letters, 2021, 11(4): 100284 doi: 10.1016/j.taml.2021.100284
    [33]
    Yang X, Sotiropoulos F, Conzenmius RJ, et al. Large‐eddy simulation of turbulent flow past wind turbines/farms: the virtual wind simulator (VWiS). Wind Energy, 2015, 18(12): 2025-2045 doi: 10.1002/we.1802
    [34]
    Foti D. Coherent vorticity dynamics and dissipation in a utility-scale wind turbine wake with uniform inflow. Theoretical and Applied Mechanics Letters, 2021, 11(5): 100292
    [35]
    Smagorinsky J. General circulation experiments with the primitive equations: I. The basic experiment. Monthly Weather Review, 1963, 91(3): 99-164 doi: 10.1175/1520-0493(1963)091<0099:GCEWTP>2.3.CO;2
    [36]
    Deardorff JW. A numerical study of three-dimensional turbulent channel flow at large Reynolds numbers. Journal of Fluid Mechanics, 1970, 41(2): 453-480 doi: 10.1017/S0022112070000691
    [37]
    Germano M, Piomelli U, Moin P, et al. A dynamic subgrid‐scale eddy viscosity model. Physics of Fluids A: Fluid Dynamics, 1991, 3(7): 1760-1765 doi: 10.1063/1.857955
    [38]
    Wu YT, Porté-Agel F. Large-eddy simulation of wind-turbine wakes: Evaluation of turbine parametrisations. Boundary-Layer Meteorology, 2011, 138(3): 345-366 doi: 10.1007/s10546-010-9569-x
    [39]
    Yang X, Sotiropoulos F. On the predictive capabilities of LES-actuator disk model in simulating turbulence past wind turbines and farms. 1st American Control Conference, Washington, DC, United States, 2013: 2878-2883
    [40]
    Li Z, Yang X. Evaluation of actuator disk model relative to actuator surface model for predicting utility-scale wind turbine wakes. Energies, 2020, 13(14): 3574 doi: 10.3390/en13143574
    [41]
    Sorensen JN, Shen WZ. Numerical modeling of wind turbine wakes. Journal of Fluids Engineering, 2002, 124(2): 393-399 doi: 10.1115/1.1471361
    [42]
    Shen WZ, Zhang JH, Sørensen JN. The actuator surface model: a new Navier–Stokes based model for rotor computations. Journal of Solar Energy Engineering, 2009, 131(1): 011002 doi: 10.1115/1.3027502
    [43]
    Liao F, Yang X, Zhang S, et al. A simulation-based actuator surface parameterization for large-eddy simulation of propeller wakes. Ocean Engineering, 2020, 199: 107023 doi: 10.1016/j.oceaneng.2020.107023
    [44]
    Yang X, Zhang X, Li Z, et al. A smoothing technique for discrete delta functions with application to immersed boundary method in moving boundary simulations. Journal of Computational Physics, 2009, 228(20): 7821-7836 doi: 10.1016/j.jcp.2009.07.023
    [45]
    Santoni C, Carrasguillo K, Arenas-Navarro I, et al. Effect of tower and nacelle on the flow past a wind turbine. Wind Energy, 2017, 20: 1927-1939 doi: 10.1002/we.2130
    [46]
    刘海锋, 朱彬荣, 张宏杰等. 钢管格构式和圆筒式塔架对风力机尾流扰动特性对比研究. 太阳能学报, 2019, 40(7): 2036-2044 (Liu Haifeng, Zhu Binrong, Zhang Hongjie, et al. Comparative study on disturbance properties of tubular latticed and pipe tower on wake of wind turbines. Acta Energiae Solaris Sinica, 2019, 40(7): 2036-2044 (in Chinese)
    [47]
    Abraham A, Dasari T, Hong J. Effect of turbine nacelle and tower on the near wake of a utility-scale wind turbine. Journal of Wind Engineering and Industrial Aerodynamics, 2019, 193: 103981 doi: 10.1016/j.jweia.2019.103981
    [48]
    Mann J. The spatial structure of neutral atmospheric surface-layer turbulence. Journal of Fluid Mechanics, 1994, 273: 141-168 doi: 10.1017/S0022112094001886
    [49]
    Mann J. Wind field simulation. Probabilistic Engineering Mechanics, 1998, 13(4): 269-282 doi: 10.1016/S0266-8920(97)00036-2
    [50]
    Foti D, Yang X, Campagnolo F, et al. On the use of spires for generating inflow conditions with energetic coherent structures in large eddy simulation. Journal of Turbulence, 2017, 18(7): 611-633 doi: 10.1080/14685248.2017.1316852
    [51]
    Munters W, Meneveau C, Meyers J. Turbulent inflow precursor method with time-varying direction for large-eddy simulations and applications to wind farms. Boundary-Layer Meteorology, 2016, 159(2): 305-328 doi: 10.1007/s10546-016-0127-z
    [52]
    Yang X, Pakula M, Sotiropoulos F. Large-eddy simulation of a utility-scale wind farm in complex terrain. Applied Energy, 2018, 229: 767-777 doi: 10.1016/j.apenergy.2018.08.049
    [53]
    Yang X, Milliren C, Kistner M, et al. High-fidelity simulations and field measurements for characterizing wind fields in a utility-scale wind farm. Applied Energy, 2021, 281: 116115 doi: 10.1016/j.apenergy.2020.116115
    [54]
    Porté-Agel F, Wu YT, Chen, CH. A numerical study of the effects of wind direction on turbine wakes and power losses in a large wind farm. Energies, 2013, 6: 5297-5313 doi: 10.3390/en6105297
    [55]
    Gaumond M, Réthoré PE, Ott S, et al. Evaluation of the wind direction uncertainty and its impact on wake modeling at the Horns Rev offshore wind farm. Wind Energy, 2014, 17: 1169-1178 doi: 10.1002/we.1625
    [56]
    Xie S, Archer C. Self-similarity and turbulence characteristics of wind turbine wakes via large-eddy simulation. Wind Energy, 2015, 18(10): 1815-1838 doi: 10.1002/we.1792
    [57]
    钱耀如, 王同光, 张震宇. 基于大涡模拟方法的风力机气动性能和尾流干扰研究. 中国科学:物理学 力学 天文学, 2016, 46(12): 124704 (Qian Y, Wang T, Zhang. Large-eddy simulation of aerodynamic performance and wake characteristics of two inline wind turbines. Scientia Sinica Physica,Mechanica &Astronomica, 2016, 46(12): 124704 (in Chinese)
    [58]
    胡丹梅, 杨官奎, 霍能萌等. 动态来流对风力机性能的影响. 可再生能源, 2016, 34(7): 1058-1066 (Hu Danmei, Yang Guankui, Huo Nengmeng, et al. Effect of dynamic inflow on the performance of wind turbine. Renewable Energy Resources, 2016, 34(7): 1058-1066 (in Chinese)
    [59]
    韩玉霞, 汪建文, 孙博等. 湍流强度对水平轴风力机尾迹速度恢复影响机理的实验研究. 太阳能学报, 2019, 40(3): 649-655 (Han Yuxia, Wang Jianwen, Sun Bo, et al. Experimental study on influence mechanism of turbulent intensity on velocity recovery of horizontal axis wind turbine wake. Acta Energiae Solaris Sinica, 2019, 40(3): 649-655 (in Chinese)
    [60]
    Lignarolo LEM, Ragni D, Scarano F, et al. Tip-vortex instability and turbulent mixing in wind-turbine wakes. Journal of Fluid Mechanics, 2015, 781: 467-493 doi: 10.1017/jfm.2015.470
    [61]
    Ivanell S, Mikkelsen R, Sorensen JN, et al. Stability analysis of the tip vortices of a wind turbine. Wind Energy, 2010, 13(8): 705-715 doi: 10.1002/we.391
    [62]
    Chamorro LP, Porté-Agel F. A wind-tunnel investigation of wind-turbine wakes: boundary-layer turbulence effects. Boundary-Layer Meteorology, 2009, 132(1): 129-149 doi: 10.1007/s10546-009-9380-8
    [63]
    Chamorro LP, Porté-Agel F. Effects of thermal stability and incoming boundary-layer flow characteristics on wind-turbine wakes: a wind-tunnel study. Boundary-Layer Meteorology, 2010, 136: 515-533
    [64]
    Zhang W, Markfort CD, Porté-Agel F. Wind-turbine wakes in a convective boundary layer: A wind-tunnel study. Boundary-Layer Meteorology, 2013, 146(2): 161-179 doi: 10.1007/s10546-012-9751-4
    [65]
    Yang X, Hong J, Barone M, et al. Coherent dynamics in the rotor tip shear layer of utility-scale wind turbines. Journal of Fluid Mechanics, 2016, 804: 90-115 doi: 10.1017/jfm.2016.503
    [66]
    Hong J, Toloui M, Chamorro LP, et al. Natural snowfall reveals large-scale flow structures in the wake of a 2.5-MW wind turbine. Nature Communications, 2014, 5(1): 1-9
    [67]
    Iungo GV, Viola F, Camarri S, et al. Linear stability analysis of wind turbine wakes performed on wind tunnel measurements. Journal of Fluid Mechanics, 2013, 737: 499-526 doi: 10.1017/jfm.2013.569
    [68]
    Viola F, Iungo GV, Camarri S, et al. Prediction of the hub vortex instability in a wind turbine wake: stability analysis with eddy-viscosity models calibrated on wind tunnel data. Journal of Fluid Mechanics, 2014, 750: R1 doi: 10.1017/jfm.2014.263
    [69]
    Kang S, Yang X, Sotiropoulos F. On the onset of wake meandering for an axial flow turbine in a turbulent open channel flow. Journal of Fluid Mechanics, 2014, 744: 376-403 doi: 10.1017/jfm.2014.82
    [70]
    Trujillo JJ. , Bingöl F, Larsen GC, et al Light detection and ranging measurements of wake dynamics. Part II:two‐dimensional scanning. Wind Energy, 2011, 14(1): 61-75
    [71]
    Espana G, Aubrun S, Loyer S, et al. Spatial study of the wake meandering using modelled wind turbines in a wind tunnel. Wind Energy, 2011, 14(7): 923-937 doi: 10.1002/we.515
    [72]
    Espana G, Aubrun S, Loyer S, et al. Wind tunnel study of the wake meandering downstream of a modelled wind turbine as an effect of large scale turbulent eddies. Journal of Wind Engineering and Industrial Aerodynamics, 2012, 101: 24-33 doi: 10.1016/j.jweia.2011.10.011
    [73]
    Howard KB, Singh A, Sotiropoulos F, et al. On the statistics of wind turbine wake meandering: An experimental investigation. Physics of Fluids, 2015, 27(7): 075103 doi: 10.1063/1.4923334
    [74]
    Foti D, Yang X, Guala M, et al. Wake meandering statistics of a model wind turbine: Insights gained by large eddy simulations. Physical Review Fluids, 2016, 1(4): 044407 doi: 10.1103/PhysRevFluids.1.044407
    [75]
    Foti D, Yang X, Shen L, et al. Effect of wind turbine nacelle on turbine wake dynamics in large wind farms. Journal of Fluid Mechanics, 2019, 869: 1-26 doi: 10.1017/jfm.2019.206
    [76]
    Foti D, Yang X, Sotiropoulos F. Similarity of wake meandering for different wind turbine designs for different scales. Journal of Fluid Mechanics, 2018, 842: 5-25 doi: 10.1017/jfm.2018.9
    [77]
    Medici D, Alfredsson PH. Measurements on a wind turbine wake: 3D effects and bluff body vortex shedding. Wind Energy, 2006, 9(3): 219-236 doi: 10.1002/we.156
    [78]
    Medici D, Alfredsson PH. Measurements behind model wind turbines: further evidence of wake meandering. Wind Energy, 2008, 11(2): 211-217 doi: 10.1002/we.247
    [79]
    Heisel M, Hong J, Guala M. The spectral signature of wind turbine wake meandering: A wind tunnel and field‐scale study. Wind Energy, 2018, 21(9): 715-731 doi: 10.1002/we.2189
    [80]
    Yang X, Sotiropoulos F. Wake characteristics of a utility-scale wind turbine under coherent inflow structures and different operating conditions. Physical Review Fluids, 2019, 4(2): 024604 doi: 10.1103/PhysRevFluids.4.024604
    [81]
    Foti D, Yang X, Campagnolo F, et al. Wake meandering of a model wind turbine operating in two different regimes. Physical Review Fluids, 2018, 3(5): 054607 doi: 10.1103/PhysRevFluids.3.054607
    [82]
    Yang X, Foti D, Kelley C, et al. Wake statistics of different-scale wind turbines under turbulent boundary layer inflow. Energies, 2020, 13(11): 3004 doi: 10.3390/en13113004
    [83]
    Li Z, Yang X. Large-eddy simulation on the similarity between wakes of wind turbines with different yaw angles. Journal of Fluid Mechanics, 2021, 921: A11 doi: 10.1017/jfm.2021.495
    [84]
    Feist C, Sotiropoulos F, Guala M. A quasi-coupled wind wave experimental framework for testing offshore wind turbine floating systems. Theoretical and Applied Mechanics Letters, 2021, 11(5): 100294
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