25 citations as recorded by crossref.

1. 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
2. Dynamic-mode-decomposition of the wake of the NREL-5MW wind turbine impinged by a laminar inflow G. De Cillis et al. https://doi.org/10.1016/j.renene.2022.08.113
3. Spatial structure and energy content of wakes behind single and arrayed undulated cylinders T. Drucker-Boisvert et al. https://doi.org/10.1007/s00348-026-04276-1
4. Toward understanding waked flow fields behind a wind turbine using proper orthogonal decomposition J. Moon & L. Manuel https://doi.org/10.1063/5.0035751
5. Clustering sparse sensor placement identification and deep learning based forecasting for wind turbine wakes N. Ali et al. https://doi.org/10.1063/5.0036281
6. Increasing the efficiency of wind farms F. Hassan et al. https://doi.org/10.21285/1814-3520-2022-2-217-227
7. Floating wind farm experiments through scaling for wake characterization, power extraction, and turbine dynamics J. Bossuyt et al. https://doi.org/10.1103/PhysRevFluids.8.120501
8. Characterizing tilt effects on wind plants R. Scott et al. https://doi.org/10.1063/5.0009853
9. Multirotor wind turbine wakes M. Bastankhah & M. Abkar https://doi.org/10.1063/1.5097285
10. 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
11. The flow field within a staggered hydrokinetic turbine array Y. Chen et al. https://doi.org/10.1016/j.renene.2024.120046
12. Turbulence kinetic energy budget and conditional sampling of momentum, scalar, and intermittency fluxes in thermally stratified wind farms N. Ali et al. https://doi.org/10.1080/14685248.2018.1564831
13. POD‐based analysis of a wind turbine wake under the influence of tower and nacelle G. De Cillis et al. https://doi.org/10.1002/we.2592
14. Classification of the Reynolds stress anisotropy tensor in very large thermally stratified wind farms using colormap image segmentation N. Ali et al. https://doi.org/10.1063/1.5113654
15. Dynamic mode decomposition of wind turbines wake: a path to data-driven reduced order modeling R. Zangeneh https://doi.org/10.1080/14786451.2025.2607286
16. Scale evolution, intermittency and fluctuation relations in the near-wake of a wind turbine array N. Ali & R. Cal https://doi.org/10.1016/j.chaos.2018.12.018
17. Multi-scale/fractal processes in the wake of a wind turbine array boundary layer N. Ali et al. https://doi.org/10.1080/14685248.2019.1590584
18. Impact of Wind Turbines on Near-Surface Microclimate and Soil Moisture Under Different Stability Regimes S. Weerappulli Hewage et al. https://doi.org/10.1007/s10546-026-00996-3
19. Cluster-based probabilistic structure dynamical model of wind turbine wake N. Ali et al. https://doi.org/10.1080/14685248.2021.1925125
20. Quantification of Preferential Contribution of Reynolds Shear Stresses and Flux of Mean Kinetic Energy Via Conditional Sampling in a Wind Turbine Array H. Kadum et al. https://doi.org/10.1115/1.4040568
21. Systematic analysis of performance and cost of two floating offshore wind turbines with significant interactions L. Zhang et al. https://doi.org/10.1016/j.apenergy.2022.119341
22. Dynamic effects of inertial particles on the wake recovery of a model wind turbine S. Smith et al. https://doi.org/10.1016/j.renene.2020.09.037
23. Experimental Investigation of the Evolution of Coherent Flow Structures within an Actuator Disc Wind Farm J. Maus & M. Hölling https://doi.org/10.1088/1742-6596/3224/3/032021
24. Performance and wake characteristics of tidal turbines in an infinitely large array P. Ouro & T. Nishino https://doi.org/10.1017/jfm.2021.692
25. Predictive modeling of wake flow for a floating offshore wind turbine using dynamic mode decomposition X. Zhao et al. https://doi.org/10.1016/j.oceaneng.2025.121590