89 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. 10.5194/wes-6-555-2021
2. Experimental investigation and analytical modelling of active yaw control for wind farm power optimization H. Zong & F. Porté-Agel 10.1016/j.renene.2021.02.059
3. 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
4. Initial results from a field campaign of wake steering applied at a commercial wind farm – Part 1 P. Fleming et al. 10.5194/wes-4-273-2019
5. Modelling Yawed Wind Turbine Wakes: Extension of a Gaussian-Based Wake Model D. Wei et al. 10.3390/en14154494
6. Comparison of the Gaussian Wind Farm Model with Historical Data of Three Offshore Wind Farms B. Doekemeijer et al. 10.3390/en15061964
7. Field experiment for open-loop yaw-based wake steering at a commercial onshore wind farm in Italy B. Doekemeijer et al. 10.5194/wes-6-159-2021
8. The characteristics of helically deflected wind turbine wakes H. Korb et al. 10.1017/jfm.2023.390
9. Wind turbine partial wake merging description and quantification R. Scott et al. 10.1002/we.2504
10. Aerodynamic force reduction of rectangular cylinder using deep reinforcement learning-controlled multiple jets L. Yan et al. 10.1063/5.0189009
11. Mechanisms of dynamic near-wake modulation of a utility-scale wind turbine A. Abraham et al. 10.1017/jfm.2021.737
12. Monte-Carlo simulations based hub height optimization using FLORIS for two interacting onshore wind farms G. Kütükçü & O. Uzol 10.1063/5.0107244
13. Extending the dynamic wake meandering model in HAWC2Farm: a comparison with field measurements at the Lillgrund wind farm J. Liew et al. 10.5194/wes-8-1387-2023
14. A Probabilistic Learning Approach Applied to the Optimization of Wake Steering in Wind Farms J. Almeida & F. Rochinha 10.1115/1.4054501
15. A Wake Modeling Paradigm for Wind Farm Design and Control C. Shapiro et al. 10.3390/en12152956
16. A new method to characterize the curled wake shape under yaw misalignment B. Sengers et al. 10.1088/1742-6596/1618/6/062050
17. The Effect of Using Different Wake Models on Wind Farm Layout Optimization: A Comparative Study P. Yang & H. Najafi 10.1115/1.4052775
18. Yaw Optimisation for Wind Farm Production Maximisation Based on a Dynamic Wake Model Z. Deng et al. 10.3390/en16093932
19. Can wind turbine farms increase settlement of particulate matters during dust events? M. Mataji et al. 10.1063/5.0129481
20. Improving wind farm flow models by learning from operational data J. Schreiber et al. 10.5194/wes-5-647-2020
21. Extension and validation of an operational dynamic wake model to yawed configurations M. Lejeune et al. 10.1088/1742-6596/2265/2/022018
22. Accelerated wind farm yaw and layout optimisation with multi-fidelity deep transfer learning wake models S. Anagnostopoulos et al. 10.1016/j.renene.2023.119293
23. Turbulence and Control of Wind Farms C. Shapiro et al. 10.1146/annurev-control-070221-114032
24. Wind Farm Simulation and Layout Optimization in Complex Terrain J. Allen et al. 10.1088/1742-6596/1452/1/012066
25. Wind farm flow control: prospects and challenges J. Meyers et al. 10.5194/wes-7-2271-2022
26. Toward flow control: An assessment of the curled wake model in the FLORIS framework C. Bay et al. 10.1088/1742-6596/1618/2/022033
27. Comparison of modular analytical wake models to the Lillgrund wind plant N. Hamilton et al. 10.1063/5.0018695
28. Experimental results of wake steering using fixed angles P. Fleming et al. 10.5194/wes-6-1521-2021
29. Flow in a large wind field with multiple actuators in the presence of constant vorticity S. Basu et al. 10.1063/5.0104902
30. Dynamic wind farm flow control using free-vortex wake models M. van den Broek et al. 10.5194/wes-9-721-2024
31. Optimizing wind farm control through wake steering using surrogate models based on high-fidelity simulations P. Hulsman et al. 10.5194/wes-5-309-2020
32. FLOW Estimation and Rose Superposition (FLOWERS): an integral approach to engineering wake models M. LoCascio et al. 10.5194/wes-7-1137-2022
33. A data-driven machine learning approach for yaw control applications of wind farms C. Santoni et al. 10.1016/j.taml.2023.100471
34. Curled-Skewed Wakes behind Yawed Wind Turbines Subject to Veered Inflow M. Mohammadi et al. 10.3390/en15239135
35. Stochastic Dynamical Modeling of Wind Farm Turbulence A. Bhatt et al. 10.3390/en16196908
36. A vortex sheet based analytical model of the curled wake behind yawed wind turbines M. Bastankhah et al. 10.1017/jfm.2021.1010
37. Wakes of Wind Turbines in Yaw for Wind Farm Power Optimization A. Crespo 10.3390/en15186553
38. Design and analysis of a wake model for spatially heterogeneous flow A. Farrell et al. 10.5194/wes-6-737-2021
39. 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
40. An adaptation of the super-Gaussian wake model for yawed wind turbines F. Blondel et al. 10.1088/1742-6596/1618/6/062031
41. 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
42. Multifidelity multiobjective optimization for wake-steering strategies J. Quick et al. 10.5194/wes-7-1941-2022
43. Numerical Study on the Yaw Control for Two Wind Turbines under Different Spacings Z. Xin et al. 10.3390/app12147098
44. Design and analysis of a wake steering controller with wind direction variability E. Simley et al. 10.5194/wes-5-451-2020
45. Adaptation of Engineering Wake Models using Gaussian Process Regression and High-Fidelity Simulation Data L. Andersson et al. 10.1088/1742-6596/1618/2/022043
46. Parametric dependencies of the yawed wind‐turbine wake development E. Kleusberg et al. 10.1002/we.2395
47. Asymmetries and similarities of yawed rotor wakes X. Xiong et al. 10.1063/5.0106745
48. Power Maximization and Fatigue-Load Mitigation in a Wind-turbine Array by Active Yaw Control: an LES Study M. Lin & F. Porté-Agel 10.1088/1742-6596/1618/4/042036
49. Pseudo-2D RANS: A LiDAR-driven mid-fidelity model for simulations of wind farm flows S. Letizia & G. Iungo 10.1063/5.0076739
50. Wind Farm Modeling with Interpretable Physics-Informed Machine Learning M. Howland & J. Dabiri 10.3390/en12142716
51. 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 10.1016/j.oceaneng.2020.107445
52. Influence of atmospheric conditions on the power production of utility-scale wind turbines in yaw misalignment M. Howland et al. 10.1063/5.0023746
53. A new method for simulating multiple wind turbine wakes under yawed conditions D. Wei et al. 10.1016/j.oceaneng.2021.109832
54. Sensitivity analysis of wake steering optimisation for wind farm power maximisation F. Gori et al. 10.5194/wes-8-1425-2023
55. Large-Eddy Simulation of Yawed Wind-Turbine Wakes: Comparisons with Wind Tunnel Measurements and Analytical Wake Models M. Lin & F. Porté-Agel 10.3390/en12234574
56. Generation and decay of counter-rotating vortices downstream of yawed wind turbines in the atmospheric boundary layer C. Shapiro et al. 10.1017/jfm.2020.717
57. Large-eddy simulation of a wind-turbine array subjected to active yaw control M. Lin & F. Porté-Agel 10.5194/wes-7-2215-2022
58. The value of wake steering wind farm flow control in US energy markets E. Simley et al. 10.5194/wes-9-219-2024
59. Adjoint optimisation for wind farm flow control with a free-vortex wake model M. van den Broek et al. 10.1016/j.renene.2022.10.120
60. A quantitative review of wind farm control with the objective of wind farm power maximization A. Kheirabadi & R. Nagamune 10.1016/j.jweia.2019.06.015
61. Wake steering of multirotor wind turbines G. Speakman et al. 10.1002/we.2633
62. Effects of wind veer on a yawed wind turbine wake in atmospheric boundary layer flow G. Narasimhan et al. 10.1103/PhysRevFluids.7.114609
63. 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. 10.1088/1742-6596/1618/2/022024
64. 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. 10.3390/pr9122224
65. Towards multi-fidelity deep learning of wind turbine wakes S. Pawar et al. 10.1016/j.renene.2022.10.013
66. Free-vortex models for wind turbine wakes under yaw misalignment – a validation study on far-wake effects M. van den Broek et al. 10.5194/wes-8-1909-2023
67. Quantification of wake shape modulation and deflection for tilt and yaw misaligned wind turbines J. Bossuyt et al. 10.1017/jfm.2021.237
68. Large eddy simulations of curled wakes from tilted wind turbines H. Johlas et al. 10.1016/j.renene.2022.02.018
69. Data-driven wake model parameter estimation to analyze effects of wake superposition M. LoCascio et al. 10.1063/5.0163896
70. Wind farm blockage effects: comparison of different engineering models E. Branlard et al. 10.1088/1742-6596/1618/6/062036
71. Turbine power loss during yaw-misaligned free field tests at different atmospheric conditions P. Hulsman et al. 10.1088/1742-6596/2265/3/032074
72. Incoming flow measurements of a utility-scale wind turbine using super-large-scale particle image velocimetry C. Li et al. 10.1016/j.jweia.2019.104074
73. A point vortex transportation model for yawed wind turbine wakes H. Zong & F. Porté-Agel 10.1017/jfm.2020.123
74. Evolution of eddy viscosity in the wake of a wind turbine R. Scott et al. 10.5194/wes-8-449-2023
75. The curled wake model: equivalence of shed vorticity models L. Martínez-Tossas & E. Branlard 10.1088/1742-6596/1452/1/012069
76. A physically interpretable data-driven surrogate model for wake steering B. Sengers et al. 10.5194/wes-7-1455-2022
77. 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
78. Effectively using multifidelity optimization for wind turbine design J. Jasa et al. 10.5194/wes-7-991-2022
79. Development of a curled wake of a yawed wind turbine under turbulent and sheared inflow P. Hulsman et al. 10.5194/wes-7-237-2022
80. A time‐varying formulation of the curled wake model within the FAST.Farm framework E. Branlard et al. 10.1002/we.2785
81. Control-oriented model for secondary effects of wake steering J. King et al. 10.5194/wes-6-701-2021
82. Wind farm power optimization through wake steering M. Howland et al. 10.1073/pnas.1903680116
83. Highlighting the impact of yaw control by parsing atmospheric conditions based on total variation N. Hamilton 10.1088/1742-6596/1452/1/012006
84. A review of physical and numerical modeling techniques for horizontal-axis wind turbine wakes M. Amiri et al. 10.1016/j.rser.2024.114279
85. 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
86. Further calibration and validation of FLORIS with wind tunnel data F. Campagnolo et al. 10.1088/1742-6596/2265/2/022019
87. Evaluation of the potential for wake steering for U.S. land-based wind power plants D. Bensason et al. 10.1063/5.0039325
88. 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
89. Engineering models for turbine wake velocity deficit and wake deflection. A new proposed approach for onshore and offshore applications R. Ruisi & E. Bossanyi 10.1088/1742-6596/1222/1/012004