128 citations as recorded by crossref.

1. The curled wake model: a three-dimensional and extremely fast steady-state wake solver for wind plant flows L. Martínez-Tossas et al. https://doi.org/10.5194/wes-6-555-2021
2. An interpretable data-driven intelligent prediction framework for wind turbine wake turbulence intensity based on machine learning model Z. Li et al. https://doi.org/10.1016/j.engstruct.2026.122916
3. Investigating Turbulent Dynamics in the Nocturnal Boundary Layer: A Large Eddy Simulation Study of the South Fork Wind Farm A. Ayouche et al. https://doi.org/10.1088/1742-6596/3016/1/012048
4. Wake turbulence modeling in stratified atmospheric flows using a novel k−ℓ model K. Klemmer & M. Howland https://doi.org/10.1063/5.0249278
5. Wake redirection control for offshore wind farm power and fatigue multi-objective optimisation based on a wind turbine load indicator J. Sun et al. https://doi.org/10.1016/j.energy.2024.133893
6. The impact of coherent large-scale vortices generated by helix active wake control on the recovery process of wind turbine wakes J. Gutknecht et al. https://doi.org/10.1063/5.0278687
7. Experimental investigation and analytical modelling of active yaw control for wind farm power optimization H. Zong & F. Porté-Agel https://doi.org/10.1016/j.renene.2021.02.059
8. Overview of recent observations and simulations from the American WAKE experimeNt (AWAKEN) field campaign P. Moriarty et al. https://doi.org/10.1088/1742-6596/2505/1/012049
9. Initial results from a field campaign of wake steering applied at a commercial wind farm – Part 1 P. Fleming et al. https://doi.org/10.5194/wes-4-273-2019
10. Modelling Yawed Wind Turbine Wakes: Extension of a Gaussian-Based Wake Model D. Wei et al. https://doi.org/10.3390/en14154494
11. Comparison of the Gaussian Wind Farm Model with Historical Data of Three Offshore Wind Farms B. Doekemeijer et al. https://doi.org/10.3390/en15061964
12. Wind Farm Design with 15 MW Floating Offshore Wind Turbines in Typhoon Regions K. Ma et al. https://doi.org/10.3390/jmse13040687
13. Field experiment for open-loop yaw-based wake steering at a commercial onshore wind farm in Italy B. Doekemeijer et al. https://doi.org/10.5194/wes-6-159-2021
14. The characteristics of helically deflected wind turbine wakes H. Korb et al. https://doi.org/10.1017/jfm.2023.390
15. Characterizing Wake Behavior of Adaptive Aerodynamic Structures Using Reduced-Order Models K. Sadeghilari et al. https://doi.org/10.3390/en18143648
16. Wind turbine partial wake merging description and quantification R. Scott et al. https://doi.org/10.1002/we.2504
17. Aerodynamic force reduction of rectangular cylinder using deep reinforcement learning-controlled multiple jets L. Yan et al. https://doi.org/10.1063/5.0189009
18. Mechanisms of dynamic near-wake modulation of a utility-scale wind turbine A. Abraham et al. https://doi.org/10.1017/jfm.2021.737
19. Global optimization of wake steering for large-scale wind farms using generalized serial refinement method Y. Tu et al. https://doi.org/10.1016/j.apenergy.2025.127259
20. Monte-Carlo simulations based hub height optimization using FLORIS for two interacting onshore wind farms G. Kütükçü & O. Uzol https://doi.org/10.1063/5.0107244
21. Minimizing Levelized Cost of Energy and visual impact in Mediterranean offshore wind farms: A multi-objective optimization approach V. Barnabei et al. https://doi.org/10.1016/j.enconman.2025.120204
22. Extending the dynamic wake meandering model in HAWC2Farm: a comparison with field measurements at the Lillgrund wind farm J. Liew et al. https://doi.org/10.5194/wes-8-1387-2023
23. Three-dimensional stochastic dynamical modeling for wind farm flow estimation M. Lingad et al. https://doi.org/10.1088/1742-6596/2767/5/052065
24. A Probabilistic Learning Approach Applied to the Optimization of Wake Steering in Wind Farms J. Almeida & F. Rochinha https://doi.org/10.1115/1.4054501
25. A Wake Modeling Paradigm for Wind Farm Design and Control C. Shapiro et al. https://doi.org/10.3390/en12152956
26. A new method to characterize the curled wake shape under yaw misalignment B. Sengers et al. https://doi.org/10.1088/1742-6596/1618/6/062050
27. 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
28. A hybrid wake method for simulating yaw tandem wind turbine Y. Yuan et al. https://doi.org/10.1016/j.oceaneng.2024.119549
29. Investigating the influence of yaw control on wind farm dynamic characteristics for three conceptual floating offshore wind turbines T. Zhang et al. https://doi.org/10.1016/j.oceaneng.2025.122218
30. Yaw Optimisation for Wind Farm Production Maximisation Based on a Dynamic Wake Model Z. Deng et al. https://doi.org/10.3390/en16093932
31. Can wind turbine farms increase settlement of particulate matters during dust events? M. Mataji et al. https://doi.org/10.1063/5.0129481
32. A multi-fidelity model intercomparison for wake steering of a large turbine in a conventionally neutral atmospheric boundary layer J. Steiner et al. https://doi.org/10.5194/wes-11-1679-2026
33. Improving wind farm flow models by learning from operational data J. Schreiber et al. https://doi.org/10.5194/wes-5-647-2020
34. Extension and validation of an operational dynamic wake model to yawed configurations M. Lejeune et al. https://doi.org/10.1088/1742-6596/2265/2/022018
35. Accelerated wind farm yaw and layout optimisation with multi-fidelity deep transfer learning wake models S. Anagnostopoulos et al. https://doi.org/10.1016/j.renene.2023.119293
36. Turbulence and Control of Wind Farms C. Shapiro et al. https://doi.org/10.1146/annurev-control-070221-114032
37. Wind Farm Simulation and Layout Optimization in Complex Terrain J. Allen et al. https://doi.org/10.1088/1742-6596/1452/1/012066
38. Wind farm flow control: prospects and challenges J. Meyers et al. https://doi.org/10.5194/wes-7-2271-2022
39. Toward flow control: An assessment of the curled wake model in the FLORIS framework C. Bay et al. https://doi.org/10.1088/1742-6596/1618/2/022033
40. Comparison of modular analytical wake models to the Lillgrund wind plant N. Hamilton et al. https://doi.org/10.1063/5.0018695
41. Experimental results of wake steering using fixed angles P. Fleming et al. https://doi.org/10.5194/wes-6-1521-2021
42. 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
43. Dynamic wind farm flow control using free-vortex wake models M. van den Broek et al. https://doi.org/10.5194/wes-9-721-2024
44. Modular deep learning approach for wind farm power forecasting and wake loss prediction S. Ally et al. https://doi.org/10.5194/wes-10-779-2025
45. Optimizing wind farm control through wake steering using surrogate models based on high-fidelity simulations P. Hulsman et al. https://doi.org/10.5194/wes-5-309-2020
46. Feasibility Study of implementing Wake Steering in Floating Wind Farms: Power Gain, Load Estimation and Economic Analysis M. Mohammadi et al. https://doi.org/10.1002/we.70111
47. FLOW Estimation and Rose Superposition (FLOWERS): an integral approach to engineering wake models M. LoCascio et al. https://doi.org/10.5194/wes-7-1137-2022
48. A data-driven machine learning approach for yaw control applications of wind farms C. Santoni et al. https://doi.org/10.1016/j.taml.2023.100471
49. Curled-Skewed Wakes behind Yawed Wind Turbines Subject to Veered Inflow M. Mohammadi et al. https://doi.org/10.3390/en15239135
50. Stochastic Dynamical Modeling of Wind Farm Turbulence A. Bhatt et al. https://doi.org/10.3390/en16196908
51. 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
52. Fast physics-based extension of a double-Gaussian wake model to capture non-symmetric wake shapes J. Krause et al. https://doi.org/10.1088/1742-6596/3224/3/032076
53. Wakes of Wind Turbines in Yaw for Wind Farm Power Optimization A. Crespo https://doi.org/10.3390/en15186553
54. Design and analysis of a wake model for spatially heterogeneous flow A. Farrell et al. https://doi.org/10.5194/wes-6-737-2021
55. 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
56. Are steady-state wake models and lookup tables sufficient to design profitable wake steering strategies? A Large Eddy Simulation investigation M. Lejeune et al. https://doi.org/10.1088/1742-6596/2767/9/092075
57. Evolution and instability of the tip vortices behind a yawed wind turbine C. Li et al. https://doi.org/10.1017/jfm.2025.10389
58. 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
59. Wind farm active wake control via concurrent yaw and tip-speed ratio optimization A. Hosseini et al. https://doi.org/10.1016/j.apenergy.2024.124625
60. The fluid mechanics of active flow control at very large scales C. Meneveau https://doi.org/10.1017/jfm.2024.846
61. 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
62. Trailing-Edge Noise and Amplitude Modulation Under Yaw-Induced Partial Wake: A Curl–UVLM Analysis with Atmospheric Stability Effects H. Kim et al. https://doi.org/10.3390/en18195205
63. Multifidelity multiobjective optimization for wake-steering strategies J. Quick et al. https://doi.org/10.5194/wes-7-1941-2022
64. Numerical Study on the Yaw Control for Two Wind Turbines under Different Spacings Z. Xin et al. https://doi.org/10.3390/app12147098
65. Design and analysis of a wake steering controller with wind direction variability E. Simley et al. https://doi.org/10.5194/wes-5-451-2020
66. Analytical solutions for yawed wind-turbine wakes with application to wind-farm power optimization by active yaw control Z. Zhang et al. https://doi.org/10.1016/j.oceaneng.2024.117691
67. Short-term wind forecasting via surface pressure measurements: Stochastic modeling and sensor placement S. Abootorabi et al. https://doi.org/10.1063/5.0216465
68. Adaptation of Engineering Wake Models using Gaussian Process Regression and High-Fidelity Simulation Data L. Andersson et al. https://doi.org/10.1088/1742-6596/1618/2/022043
69. Parametric dependencies of the yawed wind‐turbine wake development E. Kleusberg et al. https://doi.org/10.1002/we.2395
70. A multi-fidelity framework for power prediction of wind farm under yaw misalignment Y. Tu et al. https://doi.org/10.1016/j.apenergy.2024.124600
71. Asymmetries and similarities of yawed rotor wakes X. Xiong et al. https://doi.org/10.1063/5.0106745
72. Power Maximization and Fatigue-Load Mitigation in a Wind-turbine Array by Active Yaw Control: an LES Study M. Lin & F. Porté-Agel https://doi.org/10.1088/1742-6596/1618/4/042036
73. 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
74. Wind Farm Modeling with Interpretable Physics-Informed Machine Learning M. Howland & J. Dabiri https://doi.org/10.3390/en12142716
75. SCADA-driven unsteady background flow model for wind farm flow and turbine wake simulations L. Beaudet et al. https://doi.org/10.1088/1742-6596/3224/3/032024
76. Real-time relocation of floating offshore wind turbine platforms for wind farm efficiency maximization: An assessment of feasibility and steady-state potential A. Kheirabadi & R. Nagamune https://doi.org/10.1016/j.oceaneng.2020.107445
77. Influence of atmospheric conditions on the power production of utility-scale wind turbines in yaw misalignment M. Howland et al. https://doi.org/10.1063/5.0023746
78. A new method for simulating multiple wind turbine wakes under yawed conditions D. Wei et al. https://doi.org/10.1016/j.oceaneng.2021.109832
79. Sensitivity analysis of wake steering optimisation for wind farm power maximisation F. Gori et al. https://doi.org/10.5194/wes-8-1425-2023
80. Wind plant wake losses: Disconnect between turbine actuation and control of plant wakes with engineering wake models R. Scott et al. https://doi.org/10.1063/5.0207013
81. Wind turbine wakes modeling and applications: Past, present, and future L. Wang et al. https://doi.org/10.1016/j.oceaneng.2024.118508
82. 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
83. Large-Eddy Simulation of Yawed Wind-Turbine Wakes: Comparisons with Wind Tunnel Measurements and Analytical Wake Models M. Lin & F. Porté-Agel https://doi.org/10.3390/en12234574
84. Generation and decay of counter-rotating vortices downstream of yawed wind turbines in the atmospheric boundary layer C. Shapiro et al. https://doi.org/10.1017/jfm.2020.717
85. Large-eddy simulation of a wind-turbine array subjected to active yaw control M. Lin & F. Porté-Agel https://doi.org/10.5194/wes-7-2215-2022
86. Adaptive Multitask Neural Network for High-Fidelity Wake Flow Modeling of Wind Farms D. Zhang et al. https://doi.org/10.3390/en18112897
87. The value of wake steering wind farm flow control in US energy markets E. Simley et al. https://doi.org/10.5194/wes-9-219-2024
88. Adjoint optimisation for wind farm flow control with a free-vortex wake model M. van den Broek et al. https://doi.org/10.1016/j.renene.2022.10.120
89. A quantitative review of wind farm control with the objective of wind farm power maximization A. Kheirabadi & R. Nagamune https://doi.org/10.1016/j.jweia.2019.06.015
90. An extended analytical wake model and applications to yawed wind turbines in atmospheric boundary layers with different levels of stratification and veer G. Narasimhan et al. https://doi.org/10.1063/5.0251305
91. Combined wake control of aligned wind turbines for power optimization based on a 3D wake model considering secondary wake steering Y. Liu et al. https://doi.org/10.1016/j.energy.2024.132900
92. Wind farm layout optimization using a novel machine learning approach M. Gholami Anjiraki et al. https://doi.org/10.1063/5.0326424
93. Wake steering of multirotor wind turbines G. Speakman et al. https://doi.org/10.1002/we.2633
94. Effects of wind veer on a yawed wind turbine wake in atmospheric boundary layer flow G. Narasimhan et al. https://doi.org/10.1103/PhysRevFluids.7.114609
95. Predicting the benefit of wake steering on the annual energy production of a wind farm using large eddy simulations and Gaussian process regression D. Hoek et al. https://doi.org/10.1088/1742-6596/1618/2/022024
96. Digital Twins for Wind Energy Conversion Systems: A Literature Review of Potential Modelling Techniques Focused on Model Fidelity and Computational Load J. De Kooning et al. https://doi.org/10.3390/pr9122224
97. Towards multi-fidelity deep learning of wind turbine wakes S. Pawar et al. https://doi.org/10.1016/j.renene.2022.10.013
98. Free-vortex models for wind turbine wakes under yaw misalignment – a validation study on far-wake effects M. van den Broek et al. https://doi.org/10.5194/wes-8-1909-2023
99. Momentum deficit and wake-added turbulence kinetic energy budgets in the stratified atmospheric boundary layer K. Klemmer & M. Howland https://doi.org/10.1103/PhysRevFluids.9.114607
100. Quantification of wake shape modulation and deflection for tilt and yaw misaligned wind turbines J. Bossuyt et al. https://doi.org/10.1017/jfm.2021.237
101. Large eddy simulations of curled wakes from tilted wind turbines H. Johlas et al. https://doi.org/10.1016/j.renene.2022.02.018
102. Data-driven wake model parameter estimation to analyze effects of wake superposition M. LoCascio et al. https://doi.org/10.1063/5.0163896
103. Wind farm blockage effects: comparison of different engineering models E. Branlard et al. https://doi.org/10.1088/1742-6596/1618/6/062036
104. Turbine power loss during yaw-misaligned free field tests at different atmospheric conditions P. Hulsman et al. https://doi.org/10.1088/1742-6596/2265/3/032074
105. Incoming flow measurements of a utility-scale wind turbine using super-large-scale particle image velocimetry C. Li et al. https://doi.org/10.1016/j.jweia.2019.104074
106. A point vortex transportation model for yawed wind turbine wakes H. Zong & F. Porté-Agel https://doi.org/10.1017/jfm.2020.123
107. Wake Recovery Enhancement with Helix Active Wake Control: Vortex Structures in a Porous Disk Wake Observed in PIV Experiments J. Gutknecht et al. https://doi.org/10.1088/1742-6596/3016/1/012030
108. Evolution of eddy viscosity in the wake of a wind turbine R. Scott et al. https://doi.org/10.5194/wes-8-449-2023
109. 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
110. The curled wake model: equivalence of shed vorticity models L. Martínez-Tossas & E. Branlard https://doi.org/10.1088/1742-6596/1452/1/012069
111. A physically interpretable data-driven surrogate model for wake steering B. Sengers et al. https://doi.org/10.5194/wes-7-1455-2022
112. Continued results from a field campaign of wake steering applied at a commercial wind farm – Part 2 P. Fleming et al. https://doi.org/10.5194/wes-5-945-2020
113. Effectively using multifidelity optimization for wind turbine design J. Jasa et al. https://doi.org/10.5194/wes-7-991-2022
114. Wake steering of wind turbine in the presence of a two-dimensional hill A. Mishra et al. https://doi.org/10.1063/5.0185842
115. Development of a curled wake of a yawed wind turbine under turbulent and sheared inflow P. Hulsman et al. https://doi.org/10.5194/wes-7-237-2022
116. A time‐varying formulation of the curled wake model within the FAST.Farm framework E. Branlard et al. https://doi.org/10.1002/we.2785
117. Active flow control of square cylinder adaptive to wind direction using deep reinforcement learning L. Yan et al. https://doi.org/10.1103/PhysRevFluids.9.094607
118. Control-oriented model for secondary effects of wake steering J. King et al. https://doi.org/10.5194/wes-6-701-2021
119. Wind farm power optimization through wake steering M. Howland et al. https://doi.org/10.1073/pnas.1903680116
120. Highlighting the impact of yaw control by parsing atmospheric conditions based on total variation N. Hamilton https://doi.org/10.1088/1742-6596/1452/1/012006
121. 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
122. 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
123. Assessment of yaw-control effects on wind turbine-wake interaction: A coupled unsteady vortex lattice method and curled wake model analysis W. Han et al. https://doi.org/10.1016/j.jweia.2023.105559
124. LES-based validation of a dynamic wind farm flow model under unsteady inflow and yaw misalignment J. Bohrer et al. https://doi.org/10.1088/1742-6596/2767/3/032041
125. Further calibration and validation of FLORIS with wind tunnel data F. Campagnolo et al. https://doi.org/10.1088/1742-6596/2265/2/022019
126. Assessing Engineering Wake Models Against Operational Data: Insights From the Lillgrund Wind Farm Wake Steering Campaign D. Siguenza‐Alvarado et al. https://doi.org/10.1002/we.70126
127. Evaluation of the potential for wake steering for U.S. land-based wind power plants D. Bensason et al. https://doi.org/10.1063/5.0039325
128. Results from a wake-steering experiment at a commercial wind plant: investigating the wind speed dependence of wake-steering performance E. Simley et al. https://doi.org/10.5194/wes-6-1427-2021