95 citations as recorded by crossref.

1. Development of a novel analytical wake model behind HAWT by considering the nacelle effect R. Mirsane et al. https://doi.org/10.1016/j.enconman.2023.118031
2. Investigation of dynamic wake model of a floating offshore wind turbine under heave, surge and pitch motion L. Wenfeng et al. https://doi.org/10.1016/j.renene.2025.123665
3. Modeling of a Wind Turbine Wake Under Different Atmospheric Conditions Using a Multi-Fidelity Approach F. Euzenat et al. https://doi.org/10.1088/1742-6596/3224/3/032036
4. Evolution and instability of the tip vortices behind a yawed wind turbine C. Li et al. https://doi.org/10.1017/jfm.2025.10389
5. Wind Farm Layout Optimization Accounting for Uncertainty in Model Selection N. O’Neill et al. https://doi.org/10.1088/1742-6596/3016/1/012054
6. Wind farm layout optimization using a novel machine learning approach M. Gholami Anjiraki et al. https://doi.org/10.1063/5.0326424
7. Pseudo-2D RANS: A LiDAR-driven mid-fidelity model for simulations of wind farm flows S. Letizia & G. Iungo https://doi.org/10.1063/5.0076739
8. Influence of thrust coefficient on the wake of a wind turbine: A numerical and analytical study D. Vahidi & F. Porté-Agel https://doi.org/10.1016/j.renene.2024.122194
9. Wind turbine wakes modeling and applications: Past, present, and future L. Wang et al. https://doi.org/10.1016/j.oceaneng.2024.118508
10. Toward ultra-efficient high-fidelity predictions of wind turbine wakes: Augmenting the accuracy of engineering models with machine learning C. Santoni et al. https://doi.org/10.1063/5.0213321
11. Data-driven surrogate model for wind turbine damage equivalent load R. Haghi & C. Crawford https://doi.org/10.5194/wes-9-2039-2024
12. Wind Turbine Wakes Under Different Atmospheric Conditions: Bridging Internal Dynamics and Meandering with Taylor’s Diffusion Theory E. Noël & E. Jézéquel https://doi.org/10.1088/1742-6596/3016/1/012044
13. A physics-based model for wind turbine wake expansion in the atmospheric boundary layer D. Vahidi & F. Porté-Agel https://doi.org/10.1017/jfm.2022.443
14. Validation of a diffusion-based analytical wake model for offshore wind farms D. Araya et al. https://doi.org/10.1088/1742-6596/3224/3/032093
15. Numerical modelling of offshore wind-farm cluster wakes P. Ouro et al. https://doi.org/10.1016/j.rser.2025.115526
16. Addressing deep array effects and impacts to wake steering with the cumulative-curl wake model C. Bay et al. https://doi.org/10.5194/wes-8-401-2023
17. Validation of RANS-calibrated engineering models and ANN-based surrogate for wind farm flow simulation and layout optimization J. Schøler et al. https://doi.org/10.5194/wes-11-2845-2026
18. Analytical modelling of wind-turbine wake turbulence in neutral atmospheric boundary layers F. Blondel et al. https://doi.org/10.1017/jfm.2026.11685
19. A vortex sheet based analytical model of the curled wake behind yawed wind turbines M. Bastankhah et al. https://doi.org/10.1017/jfm.2021.1010
20. A novel three-dimensional dual-cosine wake model for wind turbine full wake predictions Y. Bao et al. https://doi.org/10.1016/j.renene.2025.125056
21. Large-Scale Wind Turbine’s Load Characteristics Excited by the Wind and Grid in Complex Terrain: A Review W. Li et al. https://doi.org/10.3390/su142417051
22. The multi-scale coupled model: a new framework capturing wind farm–atmosphere interaction and global blockage effects S. Stipa et al. https://doi.org/10.5194/wes-9-1123-2024
23. A super-Gaussian theoretical model to calculate the near wake velocity distribution of wind turbine G. Liu & Q. Yang https://doi.org/10.1016/j.renene.2026.126130
24. Validation of an interpretable data-driven wake model using lidar measurements from a field wake steering experiment B. Sengers et al. https://doi.org/10.5194/wes-8-747-2023
25. A new physically based model for cumulative wakes and application to wind farms Z. Zhang et al. https://doi.org/10.1016/j.enconman.2026.121593
26. Alignment-based layout optimization of a floating offshore wind farm J. Park et al. https://doi.org/10.1007/s12206-026-2101-0
27. Comparison of steady-state analytical wake models implemented in wind farm analysis software R. Mudafort et al. https://doi.org/10.1088/1742-6596/2767/5/052066
28. Flow in a large wind field with multiple actuators in the presence of constant vorticity S. Basu et al. https://doi.org/10.1063/5.0104902
29. Modeling of Blockage and Wake Effect: Comparison with Field data P. Maheshwari et al. https://doi.org/10.1088/1742-6596/2767/9/092021
30. Discussion on the spatial-temporal inhomogeneity characteristic of horizontal-axis wind turbine's wake and improvement of four typical wake models S. Zhang et al. https://doi.org/10.1016/j.jweia.2023.105368
31. Towards multi-fidelity deep learning of wind turbine wakes S. Pawar et al. https://doi.org/10.1016/j.renene.2022.10.013
32. Research on the dynamic characteristics of wind turbine gearboxes under the spatiotemporal inhomogeneous in the wake X. Zhu et al. https://doi.org/10.1016/j.measurement.2023.113704
33. Analytical solution for the cumulative wake of wind turbines in wind farms M. Bastankhah et al. https://doi.org/10.1017/jfm.2020.1037
34. Wind-farm wake recovery mechanisms in conventionally neutral boundary layers L. Lanzilao & J. Meyers https://doi.org/10.1017/jfm.2025.10320
35. A new analytical wake model with application to power prediction Z. Zhang & Q. Li https://doi.org/10.1016/j.oceaneng.2026.126981
36. Three-dimensional effects of the wake on wind turbine sound propagation using parabolic equation H. Bommidala et al. https://doi.org/10.1016/j.jsv.2025.119036
37. Evaluating the Role of Blockage Deficit Models in Robust Wind Farm Design M. Ngo & A. Forsting https://doi.org/10.1088/1742-6596/3224/3/032072
38. Analytical prediction of the start of a wind turbine far wake considering the balance between pressure and Reynolds stresses Z. Fei et al. https://doi.org/10.1088/1742-6596/3016/1/012033
39. A Comparative Analysis of Actuator-Based Turbine Structure Parametrizations for High-Fidelity Modeling of Utility-Scale Wind Turbines under Neutral Atmospheric Conditions C. Santoni et al. https://doi.org/10.3390/en17030753
40. Comparison of modular analytical wake models to the Lillgrund wind plant N. Hamilton et al. https://doi.org/10.1063/5.0018695
41. Large-eddy simulation of upwind-hill effects on wind-turbine wakes and power performance Z. Zhang et al. https://doi.org/10.1016/j.energy.2024.130823
42. Derivation and verification of three-dimensional wake model of multiple wind turbines based on super-Gaussian function S. Zhang et al. https://doi.org/10.1016/j.renene.2023.118968
43. A new 3D asymmetric double-Gaussian wake analytical model for horizontal-axis wind turbines Y. Liu et al. https://doi.org/10.1016/j.jweia.2024.105685
44. Bayesian uncertainty quantification framework for wake model calibration and validation with historical wind farm power data F. Aerts et al. https://doi.org/10.1002/we.2841
45. The Effect of Using Different Wake Models on Wind Farm Layout Optimization: A Comparative Study P. Yang & H. Najafi https://doi.org/10.1115/1.4052775
46. Can wind turbine farms increase settlement of particulate matters during dust events? M. Mataji et al. https://doi.org/10.1063/5.0129481
47. Brief communication: A momentum-conserving superposition method applied to the super-Gaussian wind turbine wake model F. Blondel https://doi.org/10.5194/wes-8-141-2023
48. Quantification of three-dimensional added turbulence intensity for the horizontal-axis wind turbine considering the wake anisotropy S. Zhang et al. https://doi.org/10.1016/j.energy.2024.130843
49. Quantification of 3D spatiotemporal inhomogeneity for wake characteristics with validations from field measurement and wind tunnel test X. Gao et al. https://doi.org/10.1016/j.energy.2022.124277
50. A novel double-Gaussian full wake model for wind turbines considering dependence on thrust coefficient and ambient turbulence intensity G. Qian & T. Ishihara https://doi.org/10.1016/j.apenergy.2025.125859
51. Quantifying and clustering the wake-induced perturbations within a wind farm for load analysis A. Lovera et al. https://doi.org/10.1088/1742-6596/2505/1/012011
52. FarmConners market showcase results: wind farm flow control considering electricity prices K. Kölle et al. https://doi.org/10.5194/wes-7-2181-2022
53. An advanced analytical model for wind-turbine wakes based on an elliptic super-Gaussian function and adaptive parameters selection strategy L. Mi et al. https://doi.org/10.1063/5.0294148
54. LiDAR and SCADA data processing for interacting wind turbine wakes with comparison to analytical wake models A. Hegazy et al. https://doi.org/10.1016/j.renene.2021.09.019
55. A Simple Model for Wake-Induced Aerodynamic Interaction of Wind Turbines E. Mahmoodi et al. https://doi.org/10.3390/en16155710
56. Further improvements to the double-Gaussian wake model C. Zengler et al. https://doi.org/10.1088/1742-6596/2767/9/092066
57. Time-Averaged Wind Turbine Wake Flow Field Prediction Using Autoencoder Convolutional Neural Networks Z. Zhang et al. https://doi.org/10.3390/en15010041
58. Characterization of wind turbine flow through nacelle-mounted lidars: a review S. Letizia et al. https://doi.org/10.3389/fmech.2023.1261017
59. Two three-dimensional super-Gaussian wake models for hilly terrain L. Dai et al. https://doi.org/10.1063/5.0174297
60. An advanced optimization framework for offshore wind farm layout design Z. Zhang et al. https://doi.org/10.1016/j.energy.2026.142333
61. Theoretical modelling of the three-dimensional wake of vertical axis turbines P. Ouro & M. Lazennec https://doi.org/10.1017/flo.2021.4
62. Direct integration of non-axisymmetric Gaussian wind-turbine wake including yaw and wind-veer effects K. Ali et al. https://doi.org/10.5194/wes-10-511-2025
63. An adaptation of the super-Gaussian wake model for yawed wind turbines F. Blondel et al. https://doi.org/10.1088/1742-6596/1618/6/062031
64. Development and validation of a three-dimensional wind-turbine wake model based on high-order Gaussian function H. Wei et al. https://doi.org/10.1016/j.oceaneng.2024.119133
65. Hyperparameter tuning framework for calibrating analytical wake models using SCADA data of an offshore wind farm D. van Binsbergen et al. https://doi.org/10.5194/wes-9-1507-2024
66. The area localized coupled model for analytical mean flow prediction in arbitrary wind farm geometries G. Starke et al. https://doi.org/10.1063/5.0042573
67. Analytical Descriptions of Swirling Wake Profiles W. Schutz & J. Naughton https://doi.org/10.1088/1742-6596/2505/1/012021
68. Two Three-Dimensional Super-Gaussian Wake Models for Wind Turbine Wakes Z. Luo et al. https://doi.org/10.1061/JLEED9.EYENG-5350
69. Ranking multi-fidelity model performances in reproducing internal and external wake impacts at neighbouring offshore wind farms S. Freitas et al. https://doi.org/10.1088/1742-6596/2767/9/092045
70. A diffusion-based wind turbine wake model K. Ali et al. https://doi.org/10.1017/jfm.2024.1077
71. Research on three-dimensional wake model of horizontal axis wind turbine based on Weibull function Y. Li et al. https://doi.org/10.1063/5.0205533
72. A novel three-dimensional yaw wake analytical model and experimental validation using particle image velocimetry P. Zhou et al. https://doi.org/10.1016/j.energy.2025.139885
73. A review of physical and numerical modeling techniques for horizontal-axis wind turbine wakes M. Amiri et al. https://doi.org/10.1016/j.rser.2024.114279
74. A review on modeling, simulation and experiment of dynamic wake effect of floating offshore wind turbines Z. He et al. https://doi.org/10.1016/j.apenergy.2025.127283
75. Curled-Skewed Wakes behind Yawed Wind Turbines Subject to Veered Inflow M. Mohammadi et al. https://doi.org/10.3390/en15239135
76. Wind Tunnel Study on the Tip Speed Ratio’s Impact on a Wind Turbine Wake Development I. Neunaber et al. https://doi.org/10.3390/en15228607
77. The impact of the atmospheric boundary layer on the asymmetric wake profile: A bivariate analysis M. Barasa et al. https://doi.org/10.1016/j.seta.2022.102563
78. Machine learning for wind farm wake modeling: Output-driven taxonomy, architectural inductive biases, and benchmarking Z. Dong et al. https://doi.org/10.1016/j.rser.2026.117359
79. Evaluation of a lattice Boltzmann-based wind-turbine actuator line model against a Navier-Stokes approach H. Schottenhamml et al. https://doi.org/10.1088/1742-6596/2265/2/022027
80. FarmConners wind farm flow control benchmark – Part 1: Blind test results T. Göçmen et al. https://doi.org/10.5194/wes-7-1791-2022
81. Derivation and Verification of Gaussian Terrain Wake Model Based on Wind Field Experiment W. Liu et al. https://doi.org/10.3390/pr10122731
82. Mathematical modeling of two-dimensional and three-dimensional turbulent wakes under pressure gradients Z. Zhang & Q. Li https://doi.org/10.1063/5.0266631
83. A bin-wise approach to wake steering optimization under fatigue load constraints I. Gharbia et al. https://doi.org/10.1088/1742-6596/3025/1/012017
84. 3D ellipsoid-Gaussian coupling model with application to wake forecasting in complex wind fields Y. Ye et al. https://doi.org/10.1016/j.enconman.2025.120827
85. Decentralized yaw optimization for maximizing wind farm production based on deep reinforcement learning Z. Deng et al. https://doi.org/10.1016/j.enconman.2023.117031
86. A physically interpretable data-driven surrogate model for wake steering B. Sengers et al. https://doi.org/10.5194/wes-7-1455-2022
87. Calibration of wake models for the block island offshore wind farm using SCADA measurements D. Fabrizio et al. https://doi.org/10.1016/j.jfluidstructs.2026.104655
88. The actuator farm model for large eddy simulation (LES) of wind-farm-induced atmospheric gravity waves and farm–farm interaction S. Stipa et al. https://doi.org/10.5194/wes-9-2301-2024
89. Yaw Optimisation for Wind Farm Production Maximisation Based on a Dynamic Wake Model Z. Deng et al. https://doi.org/10.3390/en16093932
90. Breakdown of the velocity and turbulence in the wake of a wind turbine – Part 2: Analytical modelling E. Jézéquel et al. https://doi.org/10.5194/wes-9-119-2024
91. Probabilistic Analysis of Small-Signal Stability of Power Systems Affected by Wind Power Generation Uncertainties Considering Wake Effects Z. Wang & S. Bu https://doi.org/10.1109/TPWRS.2024.3450909
92. Bidirectional wakes over complex terrain using SCADA data and wake models N. Sasanuma et al. https://doi.org/10.5194/wes-11-265-2026
93. Prediction of multiple-wake velocity and wind power using a cosine-shaped wake model Z. Zhang & P. Huang https://doi.org/10.1016/j.renene.2023.119418
94. A novel yawed wake model capturing full-wake heterogeneity with application to layout-yaw co-optimization in utility-scale wind farms S. Zhang et al. https://doi.org/10.1016/j.apenergy.2026.128540
95. The AWAKEN wind farm benchmark – Part 2: Modeling results N. Bodini et al. https://doi.org/10.5194/wes-11-3107-2026