
1. 南京航空航天大学 江苏省风力机设计高技术研究重点实验室,南京,210016
2. 南京理工大学 新能源学院,江阴,214443
Published:2022
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王同光, 田琳琳, 钟伟, 等. 风能利用中的空气动力学研究进展Ⅱ:入流和尾流特性[J]. 空气动力学学报, 2022, 40(4): 22−50.
WANG Tongguang, TIAN Linlin, ZHONG Wei, et al. Aerodynamic research progress in wind energy Ⅱ: Inflow and wake characteristics[J]. 2022, 40(4): 22-50.
王同光, 田琳琳, 钟伟, 等. 风能利用中的空气动力学研究进展Ⅱ:入流和尾流特性[J]. 空气动力学学报, 2022, 40(4): 22−50. DOI: 10.7638/kqdlxxb-2021.0390
WANG Tongguang, TIAN Linlin, ZHONG Wei, et al. Aerodynamic research progress in wind energy Ⅱ: Inflow and wake characteristics[J]. 2022, 40(4): 22-50. DOI: 10.7638/kqdlxxb-2021.0390.
空气动力学是风能工程面临的首要和关键问题之一,决定着风工程的经济性、稳定性和安全性。针对风能技术发展的迫切需求,结合近年来风能设备大型化、规模化、海洋化、智能化和数字化的发展趋势,对风能利用中的空气动力学问题进行了探讨,本篇为其中第二部分:入流和尾流特性。一方面,选取大气边界层、风力机尾流、陆上/海上/复杂地形风电场混合尾流及其之间的相关干扰等典型气动问题为论述对象。另一方面,从外场测量、风洞实验、理论分析、数值模拟、工程建模和人工智能等多种研究途径着手,梳理其中涉及的关键空气动力学问题、特殊物理现象及取得的重要研究进展,分析所涉及的流动分布特性、演变规律与关键流动机理。此外,结合我国气候、地理条件、国情探讨风电发展面临的空气动力学难题并尝试给出解决策略。最后,对未来的研究方向进行展望。以期为风电的行业规划、技术发展和工程实施提供重要参考。
Aerodynamics is the primary and key issue faced by wind energy engineering
which determines its economy
stability and safety. Based on the development requirement and trend of large-scale
clustering
oceanic
intelligence and digital for wind engineering
the present review is mainly focused on the aerodynamics issues faced by wind energy
such as the atmospheric boundary layer (ABL)
wind turbine wake flow
onshore/offshore/complex-terrain wind farm flow and its interactions. As the second review of a successive work
special interests here are put on the atmospheric inflow and wake characteristics of wind turbines. For these two aspects
this paper summarizes recent research efforts in field measurement
wind tunnel experiment
theoretical analysis
numerical simulation
engineering modelling and artificial intelligence predictions
and also provides some views on the corresponding flow distribution characteristics
evolution laws and key mechanisms. Some discussions and suggestions are made on the aerodynamic challenges faced by wind power development under our country’s specific atmospheric/geographical conditions. Finally
a non-exhaustive perspective on the future of wind energy engineering research is presented. It is expected to provide an important reference for wind power industry planning
technological development and project implementation.
YUE M N, DING Q W, LI C, et al. Current research status of dynamic response of offshore wind turbine under the effects of wind, wave and current and ship collision[J]. Journal of Engineering for Thermal Energy and Power, 2020, 35(12): 1-10. (in Chinese)
LI Z W, HE D X. Reviews of fluid dynamics researches in wind energy engineering[J]. Advances in Mechanics, 2013, 43(5): 472-525. (in Chinese)
王同光. 风力机空气动力学面临的挑战[C]//第五届全国风能技术应用年会论文集. 中国空气动力学会, 2008: 59-68.
ZHU C Y. Three-dimensional rotational and dynamic stall of horizontal axis wind turbine blades[D]. Nanjing University of Aeronautics and Astronautics, 2020(in Chinese).
CHATELAIN P, BRICTEUX L, BACKAERT S, et al. Vortex particle-mesh methods with immersed lifting lines applied to the Large Eddy Simulation of wind turbine wakes[C]//Wake Conference, Gotland, 2011.
PORTÉ-AGEL F, BASTANKHAH M, SHAMSODDIN S. Wind-turbine and wind-farm flows: A review[J]. Boundary-Layer Meteorology, 2020, 174(1): 1-59. DOI:10.1007/s10546-019-00473-0
VEERS P, DYKES K, LANTZ E, et al. Grand challenges in the science of wind energy[J]. Science, 2019, 366(6464). DOI:10.1126/science.aau2027
WANG R, ZHANG Q, YUE P, et al. Summary and prospects of numerical simulation research of the atmospheric boundary layer[J]. Advances in Earth Science, 2020, 35(4): 331-349. (in Chinese)
ZHANG S Y, HU F. Review on study of atmospheric boundary layer and wind power generation interaction[J]. Plateau Meteorology, 2017, 36(4): 1127-1137. (in Chinese)
WANG Q. Study on mesoscale numerical simulation of wake effect of wind farm its impact on atmospheric environment[D]. Hangzhou: Zhejiang University, 2020(in Chinese).
NIELSON J, BHAGANAGAR K. Using LES to understand wake evolution during the diurnal cycle[C]//Proceedings of ASME 2015 International Mechanical Engineering Congress and Exposition, Houston, Texas, USA, 2016. doi: 10.1115/IMECE2015-52045
EL-ASKARY W A, SAKR I M, ABDELSALAM A M, et al. Modeling of wind turbine wakes under thermally-stratified atmospheric boundary layer[J]. Journal of Wind Engineering and Industrial Aerodynamics, 2017, 160: 1-15. DOI:10.1016/j.jweia.2016.11.001
PEÑA A, GRYNING S E, HASAGER C B. Comparing mixing-length models of the diabatic wind profile over homogeneous terrain[J]. Theoretical and Applied Climatology, 2010, 100(3-4): 325-335. DOI:10.1007/s00704-009-0196-8
INTERNATIONAL ELECTROTECHNICAL COMMISSION. Wind energy generation systems - Part 12-1: Power performance measurements of electricity producing wind turbines[S]. IEC 61400-12-1: 2017, 2017.
ZHANG X Y. Study on flow field characteristics and aerodynamic loads of horizontal axis wind turbine under complicated inflow conditions[D]. Lanzhou: Lanzhou University of Technology, 2019(in Chinese).
PEÑA A, RÉTHORÉ P E, RATHMANN O. Modeling large offshore wind farms under different atmospheric stability regimes with the Park wake model[J]. Renewable Energy, 2014, 70: 164-171. DOI:10.1016/j.renene.2014.02.019
DIMITROV N, NATARAJAN A, KELLY M. Model of wind shear conditional on turbulence and its impact on wind turbine loads[J]. Wind Energy, 2015, 18(11): 1917-1931. DOI:10.1002/we.1797
HOLTSLAG M C, BIERBOOMS W M, VAN BUSSEL G J W. Definition of the equivalent atmospheric stability for wind turbine fatigue load assessment[J]. Journal of Physics: Conference Series, 2014, 524: 012110. DOI:10.1088/1742-6596/524/1/012110
ARYA S P. Atmospheric boundary layer and its parameterization[R]//CERMAK J E, DAVENPORT A G, PLATE E J, VIEGAS D X, (eds). Wind Climate in Cities. NATO ASI Series (Series E: Applied Sciences), 1995. Springer, Dordrecht. doi: 10.1007/978-94-017-3686-2_3
LUNDQUIST J K, WILCZAK J M, ASHTON R, et al. Assessing state-of-the-art capabilities for probing the atmospheric boundary layer: the XPIA field campaign[J]. Bulletin of the American Meteorological Society, 2017, 98(2): 289-314. DOI:10.1175/bams-d-15-00151.1
FERRERES E, SOLER M R, TERRADELLAS E. Analysis of turbulent exchange and coherent structures in the stable atmospheric boundary layer based on tower observations[J]. Dynamics of Atmospheres and Oceans, 2013, 64: 62-78. DOI:10.1016/j.dynatmoce.2013.10.002
DEARDORFF J W. Numerical investigation of neutral and unstable planetary boundary layers[J]. Journal of the Atmospheric Sciences, 1972, 29(1): 91-115. DOI:10.1175/1520-0469(1972)0290091: nionau2.0.co;2
SAIKI E M, MOENG C H, SULLIVAN P P. Large-eddy simulation of the stably stratified planetary boundary layer[J]. Boundary-Layer Meteorology, 2000, 95(1): 1-30. DOI:10.1023/A: 1002428223156
ENGLBERGER A, DÖRNBRACK A. A numerically efficient parametrization of turbulent wind-turbine flows for different thermal stratifications[J]. Boundary-Layer Meteorology, 2018, 169(3): 505-536. DOI:10.1007/s10546-018-0377-z
BASU S, VINUESA J F, SWIFT A. Dynamic LES modeling of a diurnal cycle[J]. Journal of Applied Meteorology and Climatology, 2008, 47(4): 1156-1174. DOI:10.1175/2007jamc1677.1
DALL'OZZO CDRIC, CARISSIMO BERTRAND, MILLIEZ MAYA, et al. Large-eddy simulation of the diurnal cycle of the atmospheric boundary layer and influence of the radiative forcing during the Wangara experiment[C]// EGU General Assembly, 2013. Vienna, Austria. EGU2013-10021.
FEKIH A, MOHAMED A. Evaluation of the WRF model on simulating the vertical structure and diurnal cycle of the atmospheric boundary layer over Bordj Badji Mokhtar (southwestern Algeria)[J]. Journal of King Saud University - Science, 2019, 31(4): 602-611. DOI:10.1016/j.jksus.2017.12.004
TIAN L L, SONG Y L, ZHAO N, et al. Numerical investigations into the idealized diurnal cycle of atmospheric boundary layer and its impact on wind turbine's power performance[J]. Renewable Energy, 2020, 145: 419-427. DOI:10.1016/j.renene.2019.05.038
ENGLBERGER A, DÖRNBRACK A. Impact of the diurnal cycle of the atmospheric boundary layer on wind-turbine wakes: a numerical modelling study[J]. Boundary-Layer Meteorology, 2018, 166(3): 423-448. DOI:10.1007/s10546-017-0309-3
MEDICI D, IVANELL S, DAHLBERG J Å, et al. The upstream flow of a wind turbine: blockage effect[J]. Wind Energy, 2011, 14(5): 691-697. DOI:10.1002/we.451
SHERRY M, NEMES A, LO JACONO D, et al. The interaction of helical tip and root vortices in a wind turbine wake[J]. Physics of Fluids, 2013, 25(11): 117102. DOI:10.1063/1.4824734
IUNGO G V, VIOLA F, CAMARRI S, et al. Linear stability analysis of wind turbine wakes performed on wind tunnel measurements[J]. Journal of Fluid Mechanics, 2013, 737: 499-526. DOI:10.1017/jfm.2013.569
DONG X Q, LIU Z, WANG J W, et al. Experimental study on trajetory of tip vortex in wake of horizontal axis wind turbine wake[J]. Acta Energiae Solaris Sinica, 2019, 40(2): 326-332. (in Chinese)
ZHANG W, MARKFORT C D, PORTÉ-AGEL F. Near-wake flow structure downwind of a wind turbine in a turbulent boundary layer[J]. Experiments in Fluids, 2012, 52(5): 1219-1235. DOI:10.1007/s00348-011-1250-8
LIGNAROLO L E M, RAGNI D, SCARANO F, et al. Tip-vortex instability and turbulent mixing in wind-turbine wakes[J]. Journal of Fluid Mechanics, 2015, 781: 467-493. DOI:10.1017/jfm.2015.470
ABRAHAM A, DASARI T, HONG J R. Effect of turbine nacelle and tower on the near wake of a utility-scale wind turbine[J]. Journal of Wind Engineering and Industrial Aerodynamics, 2019, 193: 103981. DOI:10.1016/j.jweia.2019.103981
ZHONG W, WANG T G. Numerical analysis of the wind turbine blade-tip vortex[J]. Journal of Nanjing University of Aeronautics Astronautics, 2011, 43(5): 640-644. (in Chinese)
HUANG X, ALAVI MOGHADAM S M, MEYSONNAT P S, et al. Numerical analysis of the effect of flaps on the tip vortex of a wind turbine blade[J]. International Journal of Heat and Fluid Flow, 2019, 77: 336-351. DOI:10.1016/j.ijheatfluidflow.2019.05.004
SØRENSEN J N, MIKKELSEN R, SARMAST S, et al. Determination of wind turbine near-wake length based on stability analysis[J]. Journal of Physics: Conference Series, 2014, 524: 012155. DOI:10.1088/1742-6596/524/1/012155
SARMAST S, DADFAR R, MIKKELSEN R F, et al. Mutual inductance instability of the tip vortices behind a wind turbine[J]. Journal of Fluid Mechanics, 2014, 755: 705-731. DOI:10.1017/jfm.2014.326
郜志腾, 王同光, 李晔. 耦合浸入边界和致动线方法的风力机尾流计算[C]//第十一全国流体力学学术会议, 广东, 深圳, 2020.
BASTANKHAH M, PORTÉ-AGEL F. Experimental and theoretical study of wind turbine wakes in yawed conditions[J]. Journal of Fluid Mechanics, 2016, 806: 506-541. DOI:10.1017/jfm.2016.595
SØRENSEN J N, MIKKELSEN R F, HENNINGSON D S, et al. Simulation of wind turbine wakes using the actuator line technique[J]. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 2015, 373(2035): 20140071. DOI:10.1098/rsta.2014.0071
KEANE A, AGUIRRE P E O, FERCHLAND H, et al. An analytical model for a full wind turbine wake[J]. Journal of Physics: Conference Series, 2016, 753: 032039. DOI:10.1088/1742-6596/753/3/032039
HULSMAN P, MARTÍNEZ-TOSSAS L A, HAMILTON N, et al. Modelling and assessing the near-wake representation and turbulence behaviour of control-oriented wake models[J]. Journal of Physics: Conference Series, 2020, 1618: 022056. DOI:10.1088/1742-6596/1618/2/022056
SUN H Y, YANG H X. Study on an innovative three-dimensional wind turbine wake model[J]. Applied Energy, 2018, 226: 483-493. DOI:10.1016/j.apenergy.2018.06.027
ARCHER C L, VASEL-BE-HAGH A, YAN C, et al. Review and evaluation of wake loss models for wind energy applications[J]. Applied Energy, 2018, 226: 1187-1207. DOI:10.1016/j.apenergy.2018.05.085
GUALTIERI G. Comparative analysis and improvement of grid-based wind farm layout optimization[J]. Energy Conversion and Management, 2020, 208: 112593. DOI:10.1016/j.enconman.2020.112593
NIAYIFAR A, PORTÉ-AGEL F. Analytical modeling of wind farms: a new approach for power prediction[J]. Energies, 2016, 9(9): 741. DOI: 10.3390/en9090741
SUN H Y, GAO X X, YANG H X. A review of full-scale wind-field measurements of the wind-turbine wake effect and a measurement of the wake-interaction effect[J]. Renewable and Sustainable Energy Reviews, 2020, 132: 110042. DOI:10.1016/j.rser.2020.110042
CHAMORRO L P, PORTÉ-AGEL F. A wind-tunnel investigation of wind-turbine wakes: boundary-layer turbulence effects[J]. Boundary-Layer Meteorology, 2009, 132(1): 129-149. DOI:10.1007/s10546-009-9380-8
HULSMAN P, WOSNIK M, PETROVIĆ V, et al. Turbine wake deflection measurement in a wind tunnel with a lidar WindScanner[J]. Journal of Physics: Conference Series, 2020, 1452: 012007. DOI:10.1088/1742-6596/1452/1/012007
BASTANKHAH M, PORTÉ-AGEL F. Wind tunnel study of the wind turbine interaction with a boundary-layer flow: Upwind region, turbine performance, and wake region[J]. Physics of Fluids, 2017, 29(6): 065105. DOI:10.1063/1.4984078
NIELSON J, BHAGANAGAR K. Using field data–based large eddy simulation to understand role of atmospheric stability on energy production of wind turbines[J]. Wind Engineering, 2019, 43(6): 625-638. DOI:10.1177/0309524x18824540
ZHANG W, MARKFORT C D, PORTÉ-AGEL F. Wind-turbine wakes in a convective boundary layer: a wind-tunnel study[J]. Boundary-Layer Meteorology, 2013, 146(2): 161-179. DOI:10.1007/s10546-012-9751-4
STEVENS R J A M, MENEVEAU C. Flow structure and turbulence in wind farms[J]. Annual Review of Fluid Mechanics, 2017, 49(1): 311-339. DOI:10.1146/annurev-fluid-010816-060206
MO J O, CHOUDHRY A, ARJOMANDI M, et al. Large eddy simulation of the wind turbine wake characteristics in the numerical wind tunnel model[J]. Journal of Wind Engineering and Industrial Aerodynamics, 2013, 112: 11-24. DOI:10.1016/j.jweia.2012.09.002
DAAOU NEDJARI H, GUERRI O, SAIGHI M. Full rotor modelling and generalized actuator disc for wind turbine wake investigation[J]. Energy Reports, 2020, 6: 232-255. DOI:10.1016/j.egyr.2019.10.041
QIAN Y R, WANG T G, YUAN Y P, et al. Comparative study on wind turbine wakes using a modified partially-averaged Navier-Stokes method and large eddy simulation[J]. Energy, 2020, 206: 118147. DOI:10.1016/j.energy.2020.118147
YAN C, QU F, ZHAO Y T, et al. Review of development and challenges for physical modeling and numerical scheme of CFD in aeronautics and astronautics[J]. Acta Aerodynamica Sinica, 2020, 38(5): 829-857. (in Chinese)
BLOCKEN B. 50 years of Computational Wind Engineering: Past, present and future[J]. Journal of Wind Engineering Industrial Aerodynamics, 2014, 129: 69-102. DOI: 10.1016/j.jweia.2014.03.008
TIAN L L, SONG Y L, ZHAO N, et al. Effects of turbulence modelling in AD/RANS simulations of single wind tidal turbine wakes and double wake interactions[J]. Energy, 2020, 208: 118440. DOI:10.1016/j.energy.2020.118440
ANTONINI E G A, ROMERO D A, AMON C H. Analysis and modifications of turbulence models for wind turbine wake simulations in atmospheric boundary layers[J]. Journal of Solar Energy Engineering, 2018, 140(3): 031007. DOI:10.1115/1.4039377
VAN DER LAAN M P, SØRENSEN N N, RÉTHORÉ P E, et al. An improved k- ϵ model applied to a wind turbine wake in atmospheric turbulence[J]. Wind Energy, 2015, 18(5): 889-907. DOI:10.1002/we.1736
BOURAS I, MA L, INGHAM D, et al. An improved k -ω turbulence model for the simulations of the wind turbine wakes in a neutral atmospheric boundary layer flow[J]. Journal of Wind Engineering and Industrial Aerodynamics, 2018, 179: 358-368. DOI:10.1016/j.jweia.2018.06.013
ROCHA P A C, ROCHA H H B, CARNEIRO F O M, et al. K-ω SST (shear stress transport) turbulence model calibration: a case study on a small scale horizontal axis wind turbine[J]. Energy, 2014, 65: 412-418. DOI:10.1016/j.energy.2013.11.050
NGUYEN V T, GUILLOU S S, THIÉBOT J, et al. Modelling turbulence with an Actuator Disk representing a tidal turbine[J]. Renewable Energy, 2016, 97: 625-635. DOI:10.1016/j.renene.2016.06.014
TIAN, SONG, ZHAO, et al. AD/RANS simulations of wind turbine wake flow employing the RSM turbulence model: impact of isotropic and anisotropic inflow conditions[J]. Energies, 2019, 12(21): 4026. DOI:10.3390/en12214026
CRESPO A, HERNA´NDEZ J. Turbulence characteristics in wind-turbine wakes[J]. Journal of Wind Engineering and Industrial Aerodynamics, 1996, 61(1): 71-85. DOI:10.1016/0167-6105(95)00033-X
BARLAS E, BUCKINGHAM S, BEECK J. Roughness effects on wind-turbine wake dynamics in a boundary-layer wind tunnel[J]. Boundary-Layer Meteorology, 2016, 158(1): 27-42. DOI:10.1007/s10546-015-0083-z
AITKEN M L, BANTA R M, PICHUGINA Y L, et al. Quantifying wind turbine wake characteristics from scanning remote sensor data[J]. Journal of Atmospheric and Oceanic Technology, 2014, 31(4): 765-787. DOI:10.1175/jtech-d-13-00104.1
AHMADI M H B, YANG Z Y. The evolution of turbulence characteristics in the wake of a horizontal axis tidal stream turbine[J]. Renewable Energy, 2020, 151: 1008-1015. DOI:10.1016/j.renene.2019.11.092
XIE S B, ARCHER C. Self-similarity and turbulence characteristics of wind turbine wakes via large-eddy simulation[J]. Wind Energy, 2015, 18(10): 1815-1838. DOI:10.1002/we.1792
YANG X L, SOTIROPOULOS F. A review on the meandering of wind turbine wakes[J]. Energies, 2019, 12(24): 4725. DOI:10.3390/en12244725
LARSEN G C, MADSEN H A, THOMSEN K, et al. Wake meandering: a pragmatic approach[J]. Wind Energy, 2008, 11(4): 377-395. DOI:10.1002/we.267
ESPAÑA 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[J]. Journal of Wind Engineering and Industrial Aerodynamics, 2012, 101: 24-33. DOI:10.1016/j.jweia.2011.10.011
LARSEN G C, MADSEN H A, BINGL F, et al. Dynamic wake meandering modeling[R]. Roskilde, Denmark: Ris National Laboratory, 2007. Ris-R-1607(EN).
KECK R E, UNDHEIM O. A pragmatic approach to wind farm simulations using the dynamic wake meandering model[J]. Wind Energy, 2015, 18(9): 1671-1682. DOI:10.1002/we.1783
ZHANG J C, ZHAO X W. Quantification of parameter uncertainty in wind farm wake modeling[J]. Energy, 2020, 196: 117065. DOI:10.1016/j.energy.2020.117065
CHAMORRO L P, HILL C, MORTON S, et al. On the interaction between a turbulent open channel flow and an axial-flow turbine[J]. Journal of Fluid Mechanics, 2013, 716: 658-670. DOI:10.1017/jfm.2012.571
FOTI D, YANG X L, SOTIROPOULOS F. Similarity of wake meandering for different wind turbine designs for different scales[J]. Journal of Fluid Mechanics, 2018, 842: 5-25. DOI:10.1017/jfm.2018.9
YANG X L, SOTIROPOULOS F. A new class of actuator surface models for wind turbines[J]. Wind Energy, 2018, 21(5): 285-302. DOI:10.1002/we.2162
FOTI D, YANG X L, SHEN L, et al. Effect of wind turbine nacelle on turbine wake dynamics in large wind farms[J]. Journal of Fluid Mechanics, 2019, 869: 1-26. DOI:10.1017/jfm.2019.206
SCHOTTLER J, MÜHLE F, BARTL J, et al. Comparative study on the wake deflection behind yawed wind turbine models[J]. Journal of Physics: Conference Series, 2017, 854: 012032. DOI:10.1088/1742-6596/854/1/012032
BASTANKHAH M, PORTÉ-AGEL F. Wind farm power optimization via yaw angle control: a wind tunnel study[J]. Journal of Renewable and Sustainable Energy, 2019, 11(2): 023301. DOI:10.1063/1.5077038
KRAGH K A, HANSEN M H. Load alleviation of wind turbines by yaw misalignment[J]. Wind Energy, 2014, 17(7): 971-982. DOI:10.1002/we.1612
MEDICI D, ALFREDSSON P H. Measurements on a wind turbine wake: 3D effects and bluff body vortex shedding[J]. Wind Energy, 2006, 9(3): 219-236. DOI:10.1002/we.156
SHAPIRO C R, GAYME D F, MENEVEAU C. Modelling yawed wind turbine wakes: a lifting line approach[J]. Journal of Fluid Mechanics, 2018, 841: R1. DOI:10.1017/jfm.2018.75
QIAN G W, ISHIHARA T. A new analytical wake model for yawed wind turbines[J]. Energies, 2018, 11(3): 665. DOI:10.3390/en11030665
BISWAS A, SARKAR S, GUPTA R. Application of artificial neural network for performance evaluation of vertical axis wind turbine rotor[J]. International Journal of Ambient Energy, 2016, 37(2): 209-218. DOI:10.1080/01430750.2014.915889
IUNGO G V, SANTONI-ORTIZ C, ABKAR M, et al. Data-driven Reduced Order Model for prediction of wind turbine wakes[J]. Journal of Physics: Conference Series, 2015, 625: 012009. DOI:10.1088/1742-6596/625/1/012009
TI Z L, DENG X W, YANG H X. Wake modeling of wind turbines using machine learning[J]. Applied Energy, 2020, 257: 114025. DOI:10.1016/j.apenergy.2019.114025
ALI N, CAL R B. Data-driven modeling of the wake behind a wind turbine array[J]. Journal of Renewable and Sustainable Energy, 2020, 12(3): 033304. DOI:10.1063/5.0004393
ZHANG J C, ZHAO X W. A novel dynamic wind farm wake model based on deep learning[J]. Applied Energy, 2020, 277: 115552. DOI:10.1016/j.apenergy.2020.115552
BLEEG J, PURCELL M, RUISI R, et al. Wind farm blockage and the consequences of neglecting its impact on energy production[J]. Energies, 2018, 11(6): 1609. DOI:10.3390/en11061609
CALAF M, MENEVEAU C, MEYERS J. Large eddy simulation study of fully developed wind-turbine array boundary layers[J]. Physics of Fluids, 2010, 22(1): 015110. DOI:10.1063/1.3291077
WU K, PORTÉ-AGEL F. Flow adjustment inside and around large finite-size wind farms[J]. Energies, 2017, 10(12): 2164. DOI:10.3390/en10122164
BARTHELMIE R J, FOLKERTS L, ORMEL F T, et al. Offshore wind turbine wakes measured by sodar[J]. Journal of Atmospheric and Oceanic Technology, 2003, 20(4): 466-477. DOI:10.1175/1520-0426(2003)20466: owtwmb2.0.co;2
BARTHELMIE R J, HANSEN K, FRANDSEN S T, et al. Modelling and measuring flow and wind turbine wakes in large wind farms offshore[J]. Wind Energy, 2009, 12(5): 431-444. DOI:10.1002/we.348
FLEMING P, ANNONI J, SHAH J J, et al. Field test of wake steering at an offshore wind farm[J]. Wind Energy Science, 2017, 2(1): 229-239. DOI:10.5194/wes-2-229-2017
AVILA M, GARGALLO-PEIRÓ A, FOLCH A. A CFD framework for offshore and onshore wind farm simulation[J]. Journal of Physics: Conference Series, 2017, 854: 012002. DOI:10.1088/1742-6596/854/1/012002
TABIB M, RASHEED A, KVAMSDAL T. LES and RANS simulation of onshore Bessaker wind farm: analysing terrain and wake effects on wind farm performance[J]. Journal of Physics: Conference Series, 2015, 625: 012032. DOI:10.1088/1742-6596/625/1/012032
PORTÉ-AGEL F, WU Y T, LU H, et al. Large-eddy simulation of atmospheric boundary layer flow through wind turbines and wind farms[J]. Journal of Wind Engineering and Industrial Aerodynamics, 2011, 99(4): 154-168. DOI:10.1016/j.jweia.2011.01.011
PORTÉ-AGEL F, WU Y T, CHEN C H. A numerical study of the effects of wind direction on turbine wakes and power losses in a large wind farm[J]. Energies, 2013, 6(10): 5297-5313. DOI:10.3390/en6105297
CALAF M, PARLANGE M B, MENEVEAU C. Large eddy simulation study of scalar transport in fully developed wind-turbine array boundary layers[J]. Physics of Fluids, 2011, 23(12): 126603. DOI:10.1063/1.3663376
MARTÍNEZ-TOSSAS L A, CHURCHFIELD M J, YILMAZ A E, et al. Comparison of four large-eddy simulation research codes and effects of model coefficient and inflow turbulence in actuator-line-based wind turbine modeling[J]. Journal of Renewable and Sustainable Energy, 2018, 10(3): 033301. DOI:10.1063/1.5004710
GOIT J P, MEYERS J. Optimal control of energy extraction in wind-farm boundary layers[J]. Journal of Fluid Mechanics, 2015, 768: 5-50. DOI:10.1017/jfm.2015.70
FRÜH W G, CREECH A C W, MAGUIRE A E. Turbulence characteristics in offshore wind farms from LES simulations of lillgrund wind farm[J]. Energy Procedia, 2014, 59: 182-189. DOI:10.1016/j.egypro.2014.10.365
CREECH A C W, FRÜH W G. Modeling wind turbine wakes for wind farms[M]//Alternative Energy and Shale Gas Encyclopedia. Hoboken, NJ, USA: John Wiley Sons, Inc. , 2016: 28-51. doi: 10.1002/9781119066354.ch4
STIEREN A, GADDE S N, STEVENS R J A M. Modeling dynamic wind direction changes in large eddy simulations of wind farms[J]. Renewable Energy, 2021, 170: 1342-1352. DOI:10.1016/j.renene.2021.02.018
BAIDYA ROY S, PACALA S W, WALKO R L. Can large wind farms affect local meteorology? [J]. Journal of Geophysical Research: Atmospheres, 2004, 109: D19101. DOI: 10.1029/2004JD004763
ABKAR M, PORTÉ-AGEL F. A new wind-farm parameterization for large-scale atmospheric models[J]. Journal of Renewable and Sustainable Energy, 2015, 7(1): 013121. DOI:10.1063/1.4907600
YUAN R Y, JI W J, LUO K, et al. Coupled wind farm parameterization with a mesoscale model for simulations of an onshore wind farm[J]. Applied Energy, 2017, 206: 113-125. DOI:10.1016/j.apenergy.2017.08.018
LEE J C Y, LUNDQUIST J K. Evaluation of the wind farm parameterization in the Weather Research and Forecasting model (version 3.8. 1) with meteorological and turbine power data[J]. Geoscientific Model Development, 2017, 10(11): 4229-4244. DOI:10.5194/gmd-10-4229-2017
WANG S, LIU S H, CHEN J Z, et al. Case studies: simulation on characteristics of wind turbine wake effect in a lake-side wind farm with WRF-fitch[J]. Acta Scientiarum Naturalium Universitatis Pekinensis, 2018, 54(3): 605-615. (in Chinese)
MANGARA R J, GUO Z H, LI S L. Performance of the wind farm parameterization scheme coupled with the weather research and forecasting model under multiple resolution regimes for simulating an onshore wind farm[J]. Advances in Atmospheric Sciences, 2019, 36(2): 119-132. DOI:10.1007/s00376-018-8028-3
STEVENS R J A M, GAYME D F, MENEVEAU C. Generalized coupled wake boundary layer model: applications and comparisons with field and LES data for two wind farms[J]. Wind Energy, 2016, 19(11): 2023-2040. DOI:10.1002/we.1966
NIAYIFAR A, PORTÉ-AGEL F. A new analytical model for wind farm power prediction[J]. Journal of Physics: Conference Series, 2015, 625: 012039. DOI:10.1088/1742-6596/625/1/012039
REDDY S R. Wind Farm Layout Optimization (WindFLO): an advanced framework for fast wind farm analysis and optimization[J]. Applied Energy, 2020, 269: 115090. DOI:10.1016/j.apenergy.2020.115090
PEÑA A, RATHMANN O. Atmospheric stability-dependent infinite wind-farm models and the wake-decay coefficient[J]. Wind Energy, 2014, 17(8): 1269-1285. DOI:10.1002/we.1632
FRANDSEN S, THØGERSEN M L. Integrated fatigue loading for wind turbines in wind farms by combining ambient turbulence and wakes[J]. Wind Engineering, 1999, 23(6): 327-339.
ZHANG H, GE M W, LIU Y Q, et al. A new coupled model for the equivalent roughness heights of wind farms[J]. Renewable Energy, 2021, 171: 34-46. DOI:10.1016/j.renene.2021.02.076
KHOSRAVI A, MACHADO L, NUNES R O. Time-series prediction of wind speed using machine learning algorithms: a case study Osorio wind farm, Brazil[J]. Applied Energy, 2018, 224: 550-566. DOI:10.1016/j.apenergy.2018.05.043
OPTIS M, PERR-SAUER J. The importance of atmospheric turbulence and stability in machine-learning models of wind farm power production[J]. Renewable and Sustainable Energy Reviews, 2019, 112: 27-41. DOI:10.1016/j.rser.2019.05.031
SHEN W Z. Short-term wind power prediction of wind farm based on machine learning[D]. Shanghai: Shanghai Jiaotong University, 2019 (in Chinese).
MIAO Y Z. Wind farm cluster frequency regulation control strategy based on prediction information[D]. Shenyang: Shenyang University of Technology, 2019. (in Chinese)
PARK J W, AN B S, LEE Y S, et al. Wind farm layout optimization using genetic algorithm and its application to Daegwallyeong wind farm[J]. JMST Advances, 2019, 1(4): 249-257. DOI:10.1007/s42791-019-00026-z
TAO S Y, XU Q S, FEIJÓO A, et al. Wind farm layout optimization with a three-dimensional Gaussian wake model[J]. Renewable Energy, 2020, 159: 553-569. DOI:10.1016/j.renene.2020.06.003
FENG J, SHEN W Z. Design optimization of offshore wind farms with multiple types of wind turbines[J]. Applied Energy, 2017, 205: 1283-1297. DOI:10.1016/j.apenergy.2017.08.107
MOSETTI G, POLONI C, DIVIACCO B. Optimization of wind turbine positioning in large windfarms by means of a genetic algorithm[J]. Journal of Wind Engineering and Industrial Aerodynamics, 1994, 51(1): 105-116. DOI:10.1016/0167-6105(94)90080-9
HOU P, ZHU J S, MA K C, et al. A review of offshore wind farm layout optimization and electrical system design methods[J]. Journal of Modern Power Systems and Clean Energy, 2019, 7(5): 975-986. DOI:10.1007/s40565-019-0550-5
SHAKOOR R, HASSAN M Y, RAHEEM A, et al. Wake effect modeling: a review of wind farm layout optimization using Jensen׳s model[J]. Renewable and Sustainable Energy Reviews, 2016, 58: 1048-1059. DOI:10.1016/j.rser.2015.12.229
GUO N Z, ZHANG M M, LI B, et al. Influence of atmospheric stability on wind farm layout optimization based on an improved Gaussian wake model[J]. Journal of Wind Engineering and Industrial Aerodynamics, 2021, 211: 104548. DOI:10.1016/j.jweia.2021.104548
ABDOLLAHZADEH H, ATASHGAR K, ABBASI M. Multi-objective opportunistic maintenance optimization of a wind farm considering limited number of maintenance groups[J]. Renewable Energy, 2016, 88: 247-261. DOI:10.1016/j.renene.2015.11.022
CAO J F, ZHU W J, SHEN W Z, et al. Wind farm layout optimization with special attention on noise radiation[J]. Journal of Physics: Conference Series, 2020, 1618: 042022. DOI:10.1088/1742-6596/1618/4/042022
CHEN Y, LI H, JIN K, et al. Wind farm layout optimization using genetic algorithm with different hub height wind turbines[J]. Energy Conversion and Management, 2013, 70: 56-65. DOI:10.1016/j.enconman.2013.02.007
VASEL-BE-HAGH A, ARCHER C L. Wind farm hub height optimization[J]. Applied Energy, 2017, 195: 905-921. DOI:10.1016/j.apenergy.2017.03.089
REDDY S R. A machine learning approach for modeling irregular regions with multiple owners in wind farm layout design[J]. Energy, 2021, 220: 119691. DOI:10.1016/j.energy.2020.119691
TIAN L L, ZHAO N, ZHONG W, et al. Placement optimization of wind farm based on niche genetic algorithm[J]. Journal of Nanjing University of Aeronautics Astronautics, 2011, 43(5): 650-654. (in Chinese)
SONG M X, HE Z Y, CHEN K, et al. Application of wind farm micro-siting software[J]. Journal of Engineering Thermophysics, 2013, 34(7): 1299-1302. (in Chinese)
LIU Y Q, SHAO Z Z, YAN L W, et al. Optimization research of wind farm micro-sitting based on improved binary firefly algorithm[J]. Renewable Energy Resources, 2019, 37(1): 112-118. (in Chinese)
SHAO Z Z. Wake fast simulation method and its application in wind farm[D]. Beijing: North China Electric Power University (Beijing), 2019(in Chinese).
XU C, YANG J C, LI C Q, et al. Optimization of wind farm layout in complex terrain[J]. Proceedings of the CSEE, 2013, 33(31): 58-64, 7. (in Chinese)
TIAN L L. Numerical simulation of wind turbine wakes and the study of wind farm layout optimization[D]. Nanjing: Nanjing University of Aeronautics and Astronautics, 2014(in Chinese).
LI L Y, REN X Q, YANG Y L, et al. Analysis and recommendations for onshore wind power policies in China[J]. Renewable and Sustainable Energy Reviews, 2018, 82: 156-167. DOI:10.1016/j.rser.2017.06.114
MIROCHA J D, KOSOVIC B, AITKEN M L, et al. Implementation of a generalized actuator disk wind turbine model into the weather research and forecasting model for large-eddy simulation applications[J]. Journal of Renewable and Sustainable Energy, 2014, 6(1): 013104. DOI:10.1063/1.4861061
KIM D Y, KIM Y H, KIM B S. Changes in wind turbine power characteristics and annual energy production due to atmospheric stability, turbulence intensity, and wind shear[J]. Energy, 2021, 214: 119051. DOI:10.1016/j.energy.2020.119051
ABKAR M, SHARIFI A, PORTÉ-AGEL F. Wake flow in a wind farm during a diurnal cycle[J]. Journal of Turbulence, 2016, 17(4): 420-441. DOI:10.1080/14685248.2015.1127379
SHARMA V, CALAF M, LEHNING M, et al. Time-adaptive wind turbine model for an LES framework[J]. Wind Energy, 2016, 19(5): 939-952. DOI:10.1002/we.1877
SHARMA V, PARLANGE M B, CALAF M. Perturbations to the spatial and temporal characteristics of the diurnally-varying atmospheric boundary layer due to an extensive wind farm[J]. Boundary-Layer Meteorology, 2017, 162(2): 255-282. DOI:10.1007/s10546-016-0195-0
HU W C. Research on wind farm micro-siting and short-term wind speed prediction over mountainous terrain[D]. Beijing: Beijing Jiaotong University, 2019(in Chinese).
ISHIHARA T, HIBI K, OIKAWA S. A wind tunnel study of turbulent flow over a three-dimensional steep hill[J]. Journal of Wind Engineering and Industrial Aerodynamics, 1999, 83(1-3): 95-107. DOI:10.1016/S0167-6105(99)00064-1
CONAN B. Wind resource assessment in complex terrain by wind tunnel modelling[D]. Institut von Karman, Universit´E D’Orl´Eans, 2012. https://www.researchgate.net/publication/277677404_Wind_resource_assessment_in_complex_terrain_by_wind_tunnel_modelling
SHEN G H, WENG W T, WANG Y W, et al. A study on three-dimensional wind field characteristics of a complex hill[J]. Journal of Vibration and Shock, 2020, 39(4): 75-80. (in Chinese)
ALFREDSSON P H, SEGALINI A. Introduction Wind farms in complex terrains: an introduction[J]. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 2017, 375(2091): 20160096. DOI:10.1098/rsta.2016.0096
KIM H G, PATEL V C, LEE C M. Numerical simulation of wind flow over hilly terrain[J]. Journal of Wind Engineering and Industrial Aerodynamics, 2000, 87(1): 45-60. DOI:10.1016/S0167-6105(00)00014-3
RKENES KJERSTI. Investigation of terrain effects with respect to wind farm siting[D]. Trondheim: Norwegian University of Science and Technology, 2009.
HU W C, YANG Q S, CHEN H P, et al. Wind field characteristics over hilly and complex terrain in turbulent boundary layers[J]. Energy, 2021, 224: 120070. DOI:10.1016/j.energy.2021.120070
POLITIS E S, PROSPATHOPOULOS J, CABEZON D, et al. Modeling wake effects in large wind farms in complex terrain: the problem, the methods and the issues[J]. Wind Energy, 2012, 15(1): 161-182. DOI:10.1002/we.481
SEGALINI A. Linearized simulation of flow over wind farms and complex terrains[J]. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 2017, 375(2091): 20160099. DOI:10.1098/rsta.2016.0099
YANG X L, PAKULA M, SOTIROPOULOS F. Large-eddy simulation of a utility-scale wind farm in complex terrain[J]. Applied Energy, 2018, 229: 767-777. DOI:10.1016/j.apenergy.2018.08.049
SHAMSODDIN S, PORTÉ-AGEL F. Wind turbine wakes over hills[J]. Journal of Fluid Mechanics, 2018, 855: 671-702. DOI:10.1017/jfm.2018.653
HYVRINEN A. Wind turbines over a hilly terrain: performance and wake evolution[R]. Mekanik: Royal Institute of Technology, 2018.
HE X F, ZHOU R W, ZHU R. A study on wind resources in complex terrain simulated by the combination of MM5 and CFD software[J]. Resources Science, 2010, 32(4): 650-655. (in Chinese)
BODINI N, LUNDQUIST J K, OPTIS M. Can machine learning improve the model representation of turbulent kinetic energy dissipation rate in the boundary layer for complex terrain? [J]. Geoscientific Model Development, 2020, 13(9): 4271-4285. DOI:10.5194/gmd-13-4271-2020
TANG X Y, ZHAO S M, FAN B, et al. Micro-scale wind resource assessment in complex terrain based on CFD coupled measurement from multiple masts[J]. Applied Energy, 2019, 238: 806-815. DOI:10.1016/j.apenergy.2019.01.129
RADÜNZ W C, SAKAGAMI Y, HAAS R, et al. Influence of atmospheric stability on wind farm performance in complex terrain[J]. Applied Energy, 2021, 282: 116149. DOI:10.1016/j.apenergy.2020.116149
FENG Y, HE Y, ZHU Q H, et al. Temporal and spatial characteristics of offshore wind resources[J]. Journal of Tsinghua University (Science and Technology), 2016, 56(5): 522-529. (in Chinese)
BASTANKHAH M, WELCH B L, MARTÍNEZ-TOSSAS L A, et al. Analytical solution for the cumulative wake of wind turbines in wind farms[J]. Journal of Fluid Mechanics, 2021, 911: A53. DOI:10.1017/jfm.2020.1037
WANG J, ZHOU C, CAI Y F, et al. Active wake control of offshore wind farm considering fatigue equilibrium[J]. Renewable Energy Resources, 2021, 39(2): 208-214. (in Chinese)
AKHTAR N, GEYER B, ROCKEL B, et al. Accelerating deployment of offshore wind energy alter wind climate and reduce future power generation potentials[J]. Scientific Reports, 2021, 11: 11826. DOI:10.1038/s41598-021-91283-3
SIEDERSLEBEN S K, LUNDQUIST J K, PLATIS A, et al. Micrometeorological impacts of offshore wind farms as seen in observations and simulations[J]. Environmental Research Letters, 2018, 13(12): 124012. DOI:10.1088/1748-9326/aaea0b
SIEDERSLEBEN S K, PLATIS A, LUNDQUIST J K, et al. Turbulent kinetic energy over large offshore wind farms observed and simulated by the mesoscale model WRF (3.8. 1)[J]. Geoscientific Model Development, 2020, 13(1): 249-268. DOI:10.5194/gmd-13-249-2020
SYED A H, JAVED A, ASIM FEROZ R M, et al. Partial repowering analysis of a wind farm by turbine hub height variation to mitigate neighboring wind farm wake interference using mesoscale simulations[J]. Applied Energy, 2020, 268: 115050. DOI:10.1016/j.apenergy.2020.115050
TEMEL O, BRICTEUX L, VAN BEECK J. Coupled WRF-OpenFOAM study of wind flow over complex terrain[J]. Journal of Wind Engineering and Industrial Aerodynamics, 2018, 174: 152-169. DOI:10.1016/j.jweia.2018.01.002
BARCONS J, AVILA M, FOLCH A. A wind field downscaling strategy based on domain segmentation and transfer functions[J]. Wind Energy, 2018, 21(6): 409-425. DOI:10.1002/we.2169
CLASS A, VIELLIEBER M, MOUSSIOPOULOS N, et al. Two way coupled micro/meso scale method for wind farms[J]. PAMM, 2014, 14(1): 585-586. DOI:10.1002/pamm.201410280
KEITH D W, DECAROLIS J F, DENKENBERGER D C, et al. The influence of large-scale wind power on global climate[J]. PNAS, 2004, 101(46): 16115-16120. DOI:10.1073/pnas.0406930101
FITCH A C, LUNDQUIST J K, OLSON J B. Mesoscale influences of wind farms throughout a diurnal cycle[J]. Monthly Weather Review, 2013, 141(7): 2173-2198. DOI:10.1175/mwr-d-12-00185.1
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