84 citations as recorded by crossref.

1. Integrated floating wind farm layout design and mooring system optimization to increase annual energy production M. Mahfouz et al. 10.1088/1742-6596/2767/6/062020
2. Exploring the Power & Loads Paradigm: Tocha Farm Case Study T. Lucas Frutuoso et al. 10.1088/1742-6596/2767/9/092102
3. Wind turbine wakes modeling and applications: Past, present, and future L. Wang et al. 10.1016/j.oceaneng.2024.118508
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
12. Results from a wake-steering experiment at a commercial wind plant: investigating the wind speed dependence of wake-steering performance E. Simley et al. 10.5194/wes-6-1427-2021
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. Bi-level multi-objective optimization framework for wake escape in floating offshore wind farm C. Huang et al. 10.1016/j.apenergy.2024.124712
16. A nonlinear wake model of a wind turbine considering the yaw wake steering Y. Li et al. 10.1007/s00343-023-3040-6
17. Optimizing yaw angles for improved power generation in offshore wind farms: A statistical approach I. Formoso 10.1016/j.oceaneng.2024.119830
18. The Effect of Using Different Wake Models on Wind Farm Layout Optimization: A Comparative Study P. Yang & H. Najafi 10.1115/1.4052775
19. 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
20. Monte-Carlo simulations based hub height optimization using FLORIS for two interacting onshore wind farms G. Kütükçü & O. Uzol 10.1063/5.0107244
21. 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
22. Advancements in Wind Farm Control: Modelling and Multi-Objective Optimization Through Yaw-Based Wake Steering T. Lucas Frutuoso et al. 10.3390/en18092247
23. Machine learning enables national assessment of wind plant controls with implications for land use D. Harrison‐Atlas et al. 10.1002/we.2689
24. Fatigue analysis of wind turbine and load reduction through wind-farm-level yaw control Y. Wang & Z. Liu 10.1016/j.energy.2025.136266
25. Large-eddy simulation of a wind-turbine array subjected to active yaw control M. Lin & F. Porté-Agel 10.5194/wes-7-2215-2022
26. Can wind turbine farms increase settlement of particulate matters during dust events? M. Mataji et al. 10.1063/5.0129481
27. Data–Driven Wake Steering Control for a Simulated Wind Farm Model S. Simani et al. 10.31875/2409-9694.2023.10.02
28. 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
29. Numerical modelling of offshore wind-farm cluster wakes P. Ouro et al. 10.1016/j.rser.2025.115526
30. Fast yaw optimization for wind plant wake steering using Boolean yaw angles A. Stanley et al. 10.5194/wes-7-741-2022
31. 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
32. 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
33. Floating Wind Farm Layout Optimization Considering Moorings and Seabed Variations M. Hall et al. 10.1088/1742-6596/2767/6/062038
34. 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
35. 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
36. On the power and control of a misaligned rotor – beyond the cosine law S. Tamaro et al. 10.5194/wes-9-1547-2024
37. Measurement-driven large-eddy simulations of a diurnal cycle during a wake-steering field campaign E. Quon 10.5194/wes-9-495-2024
38. Wind Farm Design with 15 MW Floating Offshore Wind Turbines in Typhoon Regions K. Ma et al. 10.3390/jmse13040687
39. 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
40. Multicluster Distributed Optimization Strategy for Turbine Wake Environment Z. Yu et al. 10.1002/aisy.202400884
41. 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
42. 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
43. On the importance of wind predictions in wake steering optimization E. Kadoche et al. 10.5194/wes-9-1577-2024
44. 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
45. 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
46. 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
47. Further calibration and validation of FLORIS with wind tunnel data F. Campagnolo et al. 10.1088/1742-6596/2265/2/022019
48. 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
49. 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
50. A vortex sheet based analytical model of the curled wake behind yawed wind turbines M. Bastankhah et al. 10.1017/jfm.2021.1010
51. Dynamic wind farm flow control using free-vortex wake models M. van den Broek et al. 10.5194/wes-9-721-2024
52. Scalable SCADA-Based Calibration for Analytical Wake Models Across an Offshore Cluster D. Binsbergen et al. 10.1088/1742-6596/2745/1/012014
53. Distributed Fixed-Time Fatigue Minimization Control For Waked Wind Farms M. Firouzbahrami et al. 10.1109/TCST.2024.3362518
54. 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
55. Deep reinforcement learning-based adaptive yaw control for wind farms in fluctuating winds Q. Dong et al. 10.1063/5.0267200
56. Wind Farm Power Maximisation via Wake Steering: A Gaussian Process‐Based Yaw‐Dependent Parameter Tuning Approach F. Gori et al. 10.1002/we.2953
57. Flow in a large wind field with multiple actuators in the presence of constant vorticity S. Basu et al. 10.1063/5.0104902
58. 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
59. 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
60. A physically interpretable data-driven surrogate model for wake steering B. Sengers et al. 10.5194/wes-7-1455-2022
61. Sensitivity analysis of wake steering optimisation for wind farm power maximisation F. Gori et al. 10.5194/wes-8-1425-2023
62. Active Wake Steering Control Data-Driven Design for a Wind Farm Benchmark S. Simani et al. 10.1016/j.ifacol.2023.10.1504
63. 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
64. Comparative Analysis of Wind Farm Simulators for Wind Farm Control M. Kim et al. 10.3390/en16093676
65. Data-driven wake model parameter estimation to analyze effects of wake superposition M. LoCascio et al. 10.1063/5.0163896
66. Data-driven optimisation of wind farm layout and wake steering with large-eddy simulations N. Bempedelis et al. 10.5194/wes-9-869-2024
67. 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
68. Stochastic black-box optimization using multi-fidelity score function estimator A. Agrawal et al. 10.1088/2632-2153/ad8e2b
69. The fluid mechanics of active flow control at very large scales C. Meneveau 10.1017/jfm.2024.846
70. 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
71. FarmConners wind farm flow control benchmark – Part 1: Blind test results T. Göçmen et al. 10.5194/wes-7-1791-2022
72. Effectively using multifidelity optimization for wind turbine design J. Jasa et al. 10.5194/wes-7-991-2022
73. Robust wind farm layout optimization M. Sinner & P. Fleming 10.1088/1742-6596/2767/3/032036
74. Experimental results of wake steering using fixed angles P. Fleming et al. 10.5194/wes-6-1521-2021
75. Evaluation of the potential for wake steering for U.S. land-based wind power plants D. Bensason et al. 10.1063/5.0039325
76. Design and analysis of a wake model for spatially heterogeneous flow A. Farrell et al. 10.5194/wes-6-737-2021
77. An adaptation of the super-Gaussian wake model for yawed wind turbines F. Blondel et al. 10.1088/1742-6596/1618/6/062031
78. 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
79. 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
80. Review of wake management techniques for wind turbines D. Houck 10.1002/we.2668
81. Optimal closed-loop wake steering – Part 1: Conventionally neutral atmospheric boundary layer conditions M. Howland et al. 10.5194/wes-5-1315-2020
82. Sensitivity and Uncertainty of the FLORIS Model Applied on the Lillgrund Wind Farm M. van Beek et al. 10.3390/en14051293
83. 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
84. Design and analysis of a wake steering controller with wind direction variability E. Simley et al. 10.5194/wes-5-451-2020