66 citations as recorded by crossref.

1. Development of a novel analytical wake model behind HAWT by considering the nacelle effect R. Mirsane et al. 10.1016/j.enconman.2023.118031
2. Pseudo-2D RANS: A LiDAR-driven mid-fidelity model for simulations of wind farm flows S. Letizia & G. Iungo 10.1063/5.0076739
3. Further improvements to the double-Gaussian wake model C. Zengler et al. 10.1088/1742-6596/2767/9/092066
4. Influence of thrust coefficient on the wake of a wind turbine: A numerical and analytical study D. Vahidi & F. Porté-Agel 10.1016/j.renene.2024.122194
5. Time-Averaged Wind Turbine Wake Flow Field Prediction Using Autoencoder Convolutional Neural Networks Z. Zhang et al. 10.3390/en15010041
6. Characterization of wind turbine flow through nacelle-mounted lidars: a review S. Letizia et al. 10.3389/fmech.2023.1261017
7. Wind turbine wakes modeling and applications: Past, present, and future L. Wang et al. 10.1016/j.oceaneng.2024.118508
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. Two three-dimensional super-Gaussian wake models for hilly terrain L. Dai et al. 10.1063/5.0174297
10. Data-driven surrogate model for wind turbine damage equivalent load R. Haghi & C. Crawford 10.5194/wes-9-2039-2024
11. Theoretical modelling of the three-dimensional wake of vertical axis turbines P. Ouro & M. Lazennec 10.1017/flo.2021.4
12. A physics-based model for wind turbine wake expansion in the atmospheric boundary layer D. Vahidi & F. Porté-Agel 10.1017/jfm.2022.443
13. An adaptation of the super-Gaussian wake model for yawed wind turbines F. Blondel et al. 10.1088/1742-6596/1618/6/062031
14. Development and validation of a three-dimensional wind-turbine wake model based on high-order Gaussian function H. Wei et al. 10.1016/j.oceaneng.2024.119133
15. 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
16. 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
17. The area localized coupled model for analytical mean flow prediction in arbitrary wind farm geometries G. Starke et al. 10.1063/5.0042573
18. Analytical Descriptions of Swirling Wake Profiles W. Schutz & J. Naughton 10.1088/1742-6596/2505/1/012021
19. A vortex sheet based analytical model of the curled wake behind yawed wind turbines M. Bastankhah et al. 10.1017/jfm.2021.1010
20. Large-Scale Wind Turbine’s Load Characteristics Excited by the Wind and Grid in Complex Terrain: A Review W. Li et al. 10.3390/su142417051
21. Two Three-Dimensional Super-Gaussian Wake Models for Wind Turbine Wakes Z. Luo et al. 10.1061/JLEED9.EYENG-5350
22. The multi-scale coupled model: a new framework capturing wind farm–atmosphere interaction and global blockage effects S. Stipa et al. 10.5194/wes-9-1123-2024
23. Ranking multi-fidelity model performances in reproducing internal and external wake impacts at neighbouring offshore wind farms S. Freitas et al. 10.1088/1742-6596/2767/9/092045
24. 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
25. A diffusion-based wind turbine wake model K. Ali et al. 10.1017/jfm.2024.1077
26. Comparison of steady-state analytical wake models implemented in wind farm analysis software R. Mudafort et al. 10.1088/1742-6596/2767/5/052066
27. Research on three-dimensional wake model of horizontal axis wind turbine based on Weibull function Y. Li et al. 10.1063/5.0205533
28. Flow in a large wind field with multiple actuators in the presence of constant vorticity S. Basu et al. 10.1063/5.0104902
29. Modeling of Blockage and Wake Effect: Comparison with Field data P. Maheshwari et al. 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. 10.1016/j.jweia.2023.105368
31. Towards multi-fidelity deep learning of wind turbine wakes S. Pawar et al. 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. 10.1016/j.measurement.2023.113704
33. Analytical solution for the cumulative wake of wind turbines in wind farms M. Bastankhah et al. 10.1017/jfm.2020.1037
34. A review of physical and numerical modeling techniques for horizontal-axis wind turbine wakes M. Amiri et al. 10.1016/j.rser.2024.114279
35. Curled-Skewed Wakes behind Yawed Wind Turbines Subject to Veered Inflow M. Mohammadi et al. 10.3390/en15239135
36. Wind Tunnel Study on the Tip Speed Ratio’s Impact on a Wind Turbine Wake Development I. Neunaber et al. 10.3390/en15228607
37. The impact of the atmospheric boundary layer on the asymmetric wake profile: A bivariate analysis M. Barasa et al. 10.1016/j.seta.2022.102563
38. 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
39. Comparison of modular analytical wake models to the Lillgrund wind plant N. Hamilton et al. 10.1063/5.0018695
40. Evaluation of a lattice Boltzmann-based wind-turbine actuator line model against a Navier-Stokes approach H. Schottenhamml et al. 10.1088/1742-6596/2265/2/022027
41. Large-eddy simulation of upwind-hill effects on wind-turbine wakes and power performance Z. Zhang et al. 10.1016/j.energy.2024.130823
42. FarmConners wind farm flow control benchmark – Part 1: Blind test results T. Göçmen et al. 10.5194/wes-7-1791-2022
43. Derivation and verification of three-dimensional wake model of multiple wind turbines based on super-Gaussian function S. Zhang et al. 10.1016/j.renene.2023.118968
44. Derivation and Verification of Gaussian Terrain Wake Model Based on Wind Field Experiment W. Liu et al. 10.3390/pr10122731
45. A new 3D asymmetric double-Gaussian wake analytical model for horizontal-axis wind turbines Y. Liu et al. 10.1016/j.jweia.2024.105685
46. Bayesian uncertainty quantification framework for wake model calibration and validation with historical wind farm power data F. Aerts et al. 10.1002/we.2841
47. The Effect of Using Different Wake Models on Wind Farm Layout Optimization: A Comparative Study P. Yang & H. Najafi 10.1115/1.4052775
48. Decentralized yaw optimization for maximizing wind farm production based on deep reinforcement learning Z. Deng et al. 10.1016/j.enconman.2023.117031
49. Can wind turbine farms increase settlement of particulate matters during dust events? M. Mataji et al. 10.1063/5.0129481
50. A physically interpretable data-driven surrogate model for wake steering B. Sengers et al. 10.5194/wes-7-1455-2022
51. Brief communication: A momentum-conserving superposition method applied to the super-Gaussian wind turbine wake model F. Blondel 10.5194/wes-8-141-2023
52. Quantification of three-dimensional added turbulence intensity for the horizontal-axis wind turbine considering the wake anisotropy S. Zhang et al. 10.1016/j.energy.2024.130843
53. Quantification of 3D spatiotemporal inhomogeneity for wake characteristics with validations from field measurement and wind tunnel test X. Gao et al. 10.1016/j.energy.2022.124277
54. The actuator farm model for large eddy simulation (LES) of wind-farm-induced atmospheric gravity waves and farm–farm interaction S. Stipa et al. 10.5194/wes-9-2301-2024
55. Yaw Optimisation for Wind Farm Production Maximisation Based on a Dynamic Wake Model Z. Deng et al. 10.3390/en16093932
56. Quantifying and clustering the wake-induced perturbations within a wind farm for load analysis A. Lovera et al. 10.1088/1742-6596/2505/1/012011
57. Breakdown of the velocity and turbulence in the wake of a wind turbine – Part 2: Analytical modelling E. Jézéquel et al. 10.5194/wes-9-119-2024
58. FarmConners market showcase results: wind farm flow control considering electricity prices K. Kölle et al. 10.5194/wes-7-2181-2022
59. Prediction of multiple-wake velocity and wind power using a cosine-shaped wake model Z. Zhang & P. Huang 10.1016/j.renene.2023.119418
60. LiDAR and SCADA data processing for interacting wind turbine wakes with comparison to analytical wake models A. Hegazy et al. 10.1016/j.renene.2021.09.019
61. A Simple Model for Wake-Induced Aerodynamic Interaction of Wind Turbines E. Mahmoodi et al. 10.3390/en16155710
62. A Meandering-Capturing Wake Model Coupled to Rotor-Based Flow-Sensing for Operational Wind Farm Flow Prediction M. Lejeune et al. 10.3389/fenrg.2022.884068
63. Validation of Meso-Wake Models for Array Efficiency Prediction Using Operational Data from Five Offshore Wind Farms J. Rodrigo et al. 10.1088/1742-6596/1618/6/062044
64. 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
65. A new wake‐merging method for wind‐farm power prediction in the presence of heterogeneous background velocity fields L. Lanzilao & J. Meyers 10.1002/we.2669
66. Review on Small Horizontal-Axis Wind Turbines K. Ismail et al. 10.1007/s13369-023-08314-6