90 citations as recorded by crossref.

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4. Multi-fidelity Bayesian Optimisation of Wind Farm Wake Steering using Wake Models and Large Eddy Simulations A. Mole & S. Laizet 10.1007/s10494-024-00629-0
5. A multi-fidelity framework for power prediction of wind farm under yaw misalignment Y. Tu et al. 10.1016/j.apenergy.2024.124600
6. Positive effect of wind farm control on energy production and revenue in European markets F. Wasilczuk et al. 10.1016/j.energy.2025.136159
7. Collective wind farm operation based on a predictive model increases utility-scale energy production M. Howland et al. 10.1038/s41560-022-01085-8
8. Toward ultra-efficient high-fidelity predictions of wind turbine wakes: Augmenting the accuracy of engineering models with machine learning C. Santoni et al. 10.1063/5.0213321
9. Comparison of the Gaussian Wind Farm Model with Historical Data of Three Offshore Wind Farms B. Doekemeijer et al. 10.3390/en15061964
10. A Probabilistic Learning Approach Applied to the Optimization of Wake Steering in Wind Farms J. Almeida & F. Rochinha 10.1115/1.4054501
11. Wind plant wake losses: Disconnect between turbine actuation and control of plant wakes with engineering wake models R. Scott et al. 10.1063/5.0207013
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13. The value of wake steering wind farm flow control in US energy markets E. Simley et al. 10.5194/wes-9-219-2024
14. Adaptive Multitask Neural Network for High-Fidelity Wake Flow Modeling of Wind Farms D. Zhang et al. 10.3390/en18112897
15. Characterizing Wake Behavior of Adaptive Aerodynamic Structures Using Reduced-Order Models K. Sadeghilari et al. 10.3390/en18143648
16. Bi-level multi-objective optimization framework for wake escape in floating offshore wind farm C. Huang et al. 10.1016/j.apenergy.2024.124712
17. A nonlinear wake model of a wind turbine considering the yaw wake steering Y. Li et al. 10.1007/s00343-023-3040-6
18. Improving Floating Offshore Wind Farm Flow Control With Scalable Model-Based Deep Reinforcement Learning M. Mei et al. 10.1109/TASE.2025.3585016
19. Optimizing yaw angles for improved power generation in offshore wind farms: A statistical approach I. Formoso 10.1016/j.oceaneng.2024.119830
20. The Effect of Using Different Wake Models on Wind Farm Layout Optimization: A Comparative Study P. Yang & H. Najafi 10.1115/1.4052775
21. Artificial intelligence-aided wind plant optimization for nationwide evaluation of land use and economic benefits of wake steering D. Harrison-Atlas et al. 10.1038/s41560-024-01516-8
22. Monte-Carlo simulations based hub height optimization using FLORIS for two interacting onshore wind farms G. Kütükçü & O. Uzol 10.1063/5.0107244
23. Overview of recent observations and simulations from the American WAKE experimeNt (AWAKEN) field campaign P. Moriarty et al. 10.1088/1742-6596/2505/1/012049
24. Advancements in Wind Farm Control: Modelling and Multi-Objective Optimization Through Yaw-Based Wake Steering T. Lucas Frutuoso et al. 10.3390/en18092247
25. Machine learning enables national assessment of wind plant controls with implications for land use D. Harrison‐Atlas et al. 10.1002/we.2689
26. Fatigue analysis of wind turbine and load reduction through wind-farm-level yaw control Y. Wang & Z. Liu 10.1016/j.energy.2025.136266
27. Comparison of wind-farm control strategies under realistic offshore wind conditions: wake quantities of interest K. Brown et al. 10.5194/wes-10-1737-2025
28. Large-eddy simulation of a wind-turbine array subjected to active yaw control M. Lin & F. Porté-Agel 10.5194/wes-7-2215-2022
29. Can wind turbine farms increase settlement of particulate matters during dust events? M. Mataji et al. 10.1063/5.0129481
30. Data–Driven Wake Steering Control for a Simulated Wind Farm Model S. Simani et al. 10.31875/2409-9694.2023.10.02
31. Ada2MF: Dual-adaptive multi-fidelity neural network approach and its application in wind turbine wake prediction L. Zhan et al. 10.1016/j.engappai.2024.109061
32. Numerical modelling of offshore wind-farm cluster wakes P. Ouro et al. 10.1016/j.rser.2025.115526
33. Fast yaw optimization for wind plant wake steering using Boolean yaw angles A. Stanley et al. 10.5194/wes-7-741-2022
34. 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. 10.3390/en17030753
35. A three-dimensional, analytical wind turbine wake model: Flow acceleration, empirical correlations, and continuity Z. Sadek et al. 10.1016/j.renene.2023.03.129
36. Integer programming for optimal yaw control of wind farms F. Bestehorn et al. 10.5194/wes-10-1637-2025
37. Floating Wind Farm Layout Optimization Considering Moorings and Seabed Variations M. Hall et al. 10.1088/1742-6596/2767/6/062038
38. Time-domain fatigue damage assessment for wind turbine tower bolts under yaw optimization control at offshore wind farm T. Tao et al. 10.1016/j.oceaneng.2024.117706
39. Dynamic performance of a passively self-adjusting floating wind farm layout to increase the annual energy production M. Mahfouz et al. 10.5194/wes-9-1595-2024
40. On the power and control of a misaligned rotor – beyond the cosine law S. Tamaro et al. 10.5194/wes-9-1547-2024
41. Measurement-driven large-eddy simulations of a diurnal cycle during a wake-steering field campaign E. Quon 10.5194/wes-9-495-2024
42. Wind Farm Design with 15 MW Floating Offshore Wind Turbines in Typhoon Regions K. Ma et al. 10.3390/jmse13040687
43. Effectiveness of cooperative yaw control based on reinforcement learning for in-line multiple wind turbines L. Wang et al. 10.1016/j.conengprac.2024.106124
44. Multicluster Distributed Optimization Strategy for Turbine Wake Environment Z. Yu et al. 10.1002/aisy.202400884
45. Investigation of wake steering control effects on the dynamic responses of 15 MW semi-submersible floating wind farms T. Zhang et al. 10.1016/j.renene.2025.123704
46. Validation of an interpretable data-driven wake model using lidar measurements from a field wake steering experiment B. Sengers et al. 10.5194/wes-8-747-2023
47. On the importance of wind predictions in wake steering optimization E. Kadoche et al. 10.5194/wes-9-1577-2024
48. A comparison between scanning LiDAR and scada-based hyperparameter tuning of analytical wake models P. Daems et al. 10.1088/1742-6596/3025/1/012002
49. Development and validation of a hybrid data-driven model-based wake steering controller and its application at a utility-scale wind plant P. Bachant et al. 10.5194/wes-9-2235-2024
50. Aerodynamic characterization of two tandem wind turbines under yaw misalignment control using actuator line model Y. Tu et al. 10.1016/j.oceaneng.2023.114992
51. Further calibration and validation of FLORIS with wind tunnel data F. Campagnolo et al. 10.1088/1742-6596/2265/2/022019
52. LES-based validation of a dynamic wind farm flow model under unsteady inflow and yaw misalignment J. Bohrer et al. 10.1088/1742-6596/2767/3/032041
53. An analytical modeling study on yaw-based wake redirection control for large-scale offshore wind farm annual energy power improvement J. Tan et al. 10.1063/5.0207111
54. A vortex sheet based analytical model of the curled wake behind yawed wind turbines M. Bastankhah et al. 10.1017/jfm.2021.1010
55. Dynamic wind farm flow control using free-vortex wake models M. van den Broek et al. 10.5194/wes-9-721-2024
56. Scalable SCADA-Based Calibration for Analytical Wake Models Across an Offshore Cluster D. Binsbergen et al. 10.1088/1742-6596/2745/1/012014
57. Distributed Fixed-Time Fatigue Minimization Control For Waked Wind Farms M. Firouzbahrami et al. 10.1109/TCST.2024.3362518
58. A three-dimensional dynamic wake prediction framework for multiple turbine operating states based on diffusion model M. Song et al. 10.1016/j.energy.2025.137084
59. Brief communication: An elliptical parameterisation of the wind direction rose E. Hart 10.5194/wes-10-1821-2025
60. Validation of induction/steering reserve-boosting active power control by a wind tunnel experiment with dynamic wind direction changes S. Tamaro et al. 10.1088/1742-6596/2767/9/092067
61. Deep reinforcement learning-based adaptive yaw control for wind farms in fluctuating winds Q. Dong et al. 10.1063/5.0267200
62. Wind Farm Power Maximisation via Wake Steering: A Gaussian Process‐Based Yaw‐Dependent Parameter Tuning Approach F. Gori et al. 10.1002/we.2953
63. Flow in a large wind field with multiple actuators in the presence of constant vorticity S. Basu et al. 10.1063/5.0104902
64. Addressing deep array effects and impacts to wake steering with the cumulative-curl wake model C. Bay et al. 10.5194/wes-8-401-2023
65. Combined wake control of aligned wind turbines for power optimization based on a 3D wake model considering secondary wake steering Y. Liu et al. 10.1016/j.energy.2024.132900
66. A physically interpretable data-driven surrogate model for wake steering B. Sengers et al. 10.5194/wes-7-1455-2022
67. Sensitivity analysis of wake steering optimisation for wind farm power maximisation F. Gori et al. 10.5194/wes-8-1425-2023
68. Active Wake Steering Control Data-Driven Design for a Wind Farm Benchmark S. Simani et al. 10.1016/j.ifacol.2023.10.1504
69. Hyperparameter tuning framework for calibrating analytical wake models using SCADA data of an offshore wind farm D. van Binsbergen et al. 10.5194/wes-9-1507-2024
70. Comparative Analysis of Wind Farm Simulators for Wind Farm Control M. Kim et al. 10.3390/en16093676
71. Data-driven wake model parameter estimation to analyze effects of wake superposition M. LoCascio et al. 10.1063/5.0163896
72. Data-driven optimisation of wind farm layout and wake steering with large-eddy simulations N. Bempedelis et al. 10.5194/wes-9-869-2024
73. 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. 10.1016/j.jweia.2023.105559
74. Stochastic black-box optimization using multi-fidelity score function estimator A. Agrawal et al. 10.1088/2632-2153/ad8e2b
75. The fluid mechanics of active flow control at very large scales C. Meneveau 10.1017/jfm.2024.846
76. Study of three wake control strategies for power maximization of offshore wind farms with different layouts B. Li et al. 10.1016/j.enconman.2022.116059
77. FarmConners wind farm flow control benchmark – Part 1: Blind test results T. Göçmen et al. 10.5194/wes-7-1791-2022
78. Effectively using multifidelity optimization for wind turbine design J. Jasa et al. 10.5194/wes-7-991-2022
79. Robust wind farm layout optimization M. Sinner & P. Fleming 10.1088/1742-6596/2767/3/032036
80. Experimental results of wake steering using fixed angles P. Fleming et al. 10.5194/wes-6-1521-2021
81. Evaluation of the potential for wake steering for U.S. land-based wind power plants D. Bensason et al. 10.1063/5.0039325
82. Design and analysis of a wake model for spatially heterogeneous flow A. Farrell et al. 10.5194/wes-6-737-2021
83. An adaptation of the super-Gaussian wake model for yawed wind turbines F. Blondel et al. 10.1088/1742-6596/1618/6/062031
84. Continued results from a field campaign of wake steering applied at a commercial wind farm – Part 2 P. Fleming et al. 10.5194/wes-5-945-2020
85. Influence of Wake Model Superposition and Secondary Steering on Model-Based Wake Steering Control with SCADA Data Assimilation M. Howland & J. Dabiri 10.3390/en14010052
86. Review of wake management techniques for wind turbines D. Houck 10.1002/we.2668
87. Optimal closed-loop wake steering – Part 1: Conventionally neutral atmospheric boundary layer conditions M. Howland et al. 10.5194/wes-5-1315-2020
88. Sensitivity and Uncertainty of the FLORIS Model Applied on the Lillgrund Wind Farm M. van Beek et al. 10.3390/en14051293
89. The curled wake model: a three-dimensional and extremely fast steady-state wake solver for wind plant flows L. Martínez-Tossas et al. 10.5194/wes-6-555-2021
90. Design and analysis of a wake steering controller with wind direction variability E. Simley et al. 10.5194/wes-5-451-2020