Articles | Volume 3, issue 1
https://doi.org/10.5194/wes-3-43-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-3-43-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
28 Feb 2018
Research article |
| 28 Feb 2018
| 28 Feb 2018
Assessing spacing impact on coherent features in a wind turbine array boundary layer
Naseem Ali
Department of Mechanical and Materials Engineering, Portland State University, Portland, Oregon, USA
Nicholas Hamilton
National Renewable Energy Laboratory, Boulder, Colorado 80401, USA
Dominic DeLucia
Department of Mechanical and Materials Engineering, Portland State University, Portland, Oregon, USA
Raúl Bayoán Cal
CORRESPONDING AUTHOR
Department of Mechanical and Materials Engineering, Portland State University, Portland, Oregon, USA
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Total article views: 3,870 (including HTML, PDF, and XML)
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Cited
23 citations as recorded by crossref.
- 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
- 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
- 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
- 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
- Toward understanding waked flow fields behind a wind turbine using proper orthogonal decomposition J. Moon & L. Manuel https://doi.org/10.1063/5.0035751
- 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
- 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
- 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
- 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
- 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
- Increasing the efficiency of wind farms F. Hassan et al. https://doi.org/10.21285/1814-3520-2022-2-217-227
- 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
- Characterizing tilt effects on wind plants R. Scott et al. https://doi.org/10.1063/5.0009853
- Multirotor wind turbine wakes M. Bastankhah & M. Abkar https://doi.org/10.1063/1.5097285
- Cluster-based probabilistic structure dynamical model of wind turbine wake N. Ali et al. https://doi.org/10.1080/14685248.2021.1925125
- 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
- The flow field within a staggered hydrokinetic turbine array Y. Chen et al. https://doi.org/10.1016/j.renene.2024.120046
- 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
- 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
- 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
- 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
- Performance and wake characteristics of tidal turbines in an infinitely large array P. Ouro & T. Nishino https://doi.org/10.1017/jfm.2021.692
- 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
23 citations as recorded by crossref.
- 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
- 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
- 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
- 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
- Toward understanding waked flow fields behind a wind turbine using proper orthogonal decomposition J. Moon & L. Manuel https://doi.org/10.1063/5.0035751
- 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
- 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
- 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
- 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
- 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
- Increasing the efficiency of wind farms F. Hassan et al. https://doi.org/10.21285/1814-3520-2022-2-217-227
- 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
- Characterizing tilt effects on wind plants R. Scott et al. https://doi.org/10.1063/5.0009853
- Multirotor wind turbine wakes M. Bastankhah & M. Abkar https://doi.org/10.1063/1.5097285
- Cluster-based probabilistic structure dynamical model of wind turbine wake N. Ali et al. https://doi.org/10.1080/14685248.2021.1925125
- 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
- The flow field within a staggered hydrokinetic turbine array Y. Chen et al. https://doi.org/10.1016/j.renene.2024.120046
- 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
- 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
- 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
- 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
- Performance and wake characteristics of tidal turbines in an infinitely large array P. Ouro & T. Nishino https://doi.org/10.1017/jfm.2021.692
- 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
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