Articles | Volume 9, issue 9
https://doi.org/10.5194/wes-9-1811-2024
© Author(s) 2024. 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-9-1811-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
10 Sep 2024
Research article |
| 10 Sep 2024
| 10 Sep 2024
Aerodynamic effects of leading-edge erosion in wind farm flow modeling
Department of Wind and Energy Systems, Technical University of Denmark (DTU), 4000 Roskilde, Denmark
Tuhfe Göçmen
Department of Wind and Energy Systems, Technical University of Denmark (DTU), 4000 Roskilde, Denmark
Özge Sinem Özçakmak
Department of Wind and Energy Systems, Technical University of Denmark (DTU), 4000 Roskilde, Denmark
Alexander Meyer Forsting
Department of Wind and Energy Systems, Technical University of Denmark (DTU), 4000 Roskilde, Denmark
Ásta Hannesdóttir
Department of Wind and Energy Systems, Technical University of Denmark (DTU), 4000 Roskilde, Denmark
Pierre-Elouan Réthoré
Department of Wind and Energy Systems, Technical University of Denmark (DTU), 4000 Roskilde, Denmark
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Cited
15 citations as recorded by crossref.
- A new approach to the analysis of erosion-emulated damage in wind turbine blades: An experimental study J. Enríquez-Zárate et al. https://doi.org/10.1016/j.ymssp.2026.114437
- Vibration of blades and towers in large offshore and floating wind turbines: Mechanisms, modelling credibility and mitigation pathways Z. Zuo et al. https://doi.org/10.1016/j.rser.2026.117246
- Optimal Wind Turbine Cut-Out Speed for O&M Cost-Revenue Balance A. Bechmann et al. https://doi.org/10.1088/1742-6596/3224/6/062032
- Analysis of leading-edge erosion impact on the design and performance of wind farm flow control I. Sandua-Fernández et al. https://doi.org/10.1088/1742-6596/3131/1/012038
- Reward design for deep Reinforcement Learning driven wind farm control: What matters for optimal performance M. Nilsen et al. https://doi.org/10.1016/j.egyai.2026.100792
- Fault detection in wind turbines using health index monitoring with variational autoencoders S. Wang et al. https://doi.org/10.5194/wes-10-2841-2025
- Integrating Trend Monitoring and Change Point Detection for Wind Turbine Blade Diagnostics: A Physics-Driven Evaluation of Erosion and Twist Faults A. Hassan et al. https://doi.org/10.3390/en19010112
- A Framework of Statistical Process Control for Wind Energy Systems: A Digital Twin Approach Y. Ayaz Atalan https://doi.org/10.1007/s13369-026-11616-0
- Modelling rain erosion surface damage initiation in turbine blades based on inspection data at wind farms H. Hao et al. https://doi.org/10.1016/j.rineng.2026.109517
- Estimation of Wind Farm Losses Using a Jensen Model Based on Actual Wind Turbine Characteristics for an Offshore Wind Farm in the Baltic Sea Z. Malecha & M. Chorowski https://doi.org/10.3390/computation13010020
- Wake Losses, Productivity, and Cost Analysis of a Polish Offshore Wind Farm in the Baltic Sea A. Rasiński & Z. Malecha https://doi.org/10.3390/en18154190
- Hybrid Energy Distance and Permutation Testing for Vibration-Based Wind Turbine Blade Fault Diagnosis D. Teklemariyem et al. https://doi.org/10.3390/en19153696
- Lagrangian Particle Tracking-based Airfoil Optimization for enhanced Erosion Resistance J. Kamp et al. https://doi.org/10.1088/1742-6596/3224/4/042015
- A Review of Natural Hazards’ Impacts on Wind Turbine Performance, Part 2: Earthquakes, Waves, Tropical Cyclones, and Thunderstorm Downbursts X. Wang et al. https://doi.org/10.3390/en19020385
- Waterborne Polyurethane for Wind Turbine Blade Corrosion Protection: Synthesis, Modification Strategies, and Performance Advances Z. Wang et al. https://doi.org/10.3390/coatings16040460
15 citations as recorded by crossref.
- A new approach to the analysis of erosion-emulated damage in wind turbine blades: An experimental study J. Enríquez-Zárate et al. https://doi.org/10.1016/j.ymssp.2026.114437
- Vibration of blades and towers in large offshore and floating wind turbines: Mechanisms, modelling credibility and mitigation pathways Z. Zuo et al. https://doi.org/10.1016/j.rser.2026.117246
- Optimal Wind Turbine Cut-Out Speed for O&M Cost-Revenue Balance A. Bechmann et al. https://doi.org/10.1088/1742-6596/3224/6/062032
- Analysis of leading-edge erosion impact on the design and performance of wind farm flow control I. Sandua-Fernández et al. https://doi.org/10.1088/1742-6596/3131/1/012038
- Reward design for deep Reinforcement Learning driven wind farm control: What matters for optimal performance M. Nilsen et al. https://doi.org/10.1016/j.egyai.2026.100792
- Fault detection in wind turbines using health index monitoring with variational autoencoders S. Wang et al. https://doi.org/10.5194/wes-10-2841-2025
- Integrating Trend Monitoring and Change Point Detection for Wind Turbine Blade Diagnostics: A Physics-Driven Evaluation of Erosion and Twist Faults A. Hassan et al. https://doi.org/10.3390/en19010112
- A Framework of Statistical Process Control for Wind Energy Systems: A Digital Twin Approach Y. Ayaz Atalan https://doi.org/10.1007/s13369-026-11616-0
- Modelling rain erosion surface damage initiation in turbine blades based on inspection data at wind farms H. Hao et al. https://doi.org/10.1016/j.rineng.2026.109517
- Estimation of Wind Farm Losses Using a Jensen Model Based on Actual Wind Turbine Characteristics for an Offshore Wind Farm in the Baltic Sea Z. Malecha & M. Chorowski https://doi.org/10.3390/computation13010020
- Wake Losses, Productivity, and Cost Analysis of a Polish Offshore Wind Farm in the Baltic Sea A. Rasiński & Z. Malecha https://doi.org/10.3390/en18154190
- Hybrid Energy Distance and Permutation Testing for Vibration-Based Wind Turbine Blade Fault Diagnosis D. Teklemariyem et al. https://doi.org/10.3390/en19153696
- Lagrangian Particle Tracking-based Airfoil Optimization for enhanced Erosion Resistance J. Kamp et al. https://doi.org/10.1088/1742-6596/3224/4/042015
- A Review of Natural Hazards’ Impacts on Wind Turbine Performance, Part 2: Earthquakes, Waves, Tropical Cyclones, and Thunderstorm Downbursts X. Wang et al. https://doi.org/10.3390/en19020385
- Waterborne Polyurethane for Wind Turbine Blade Corrosion Protection: Synthesis, Modification Strategies, and Performance Advances Z. Wang et al. https://doi.org/10.3390/coatings16040460
Saved (final revised paper)
Latest update: 12 Sep 2026
Short summary
Leading-edge erosion (LEE) can impact wind turbine aerodynamics and wind farm efficiency. This study couples LEE prediction, aerodynamic loss modeling, and wind farm flow modeling to show that LEE's effects on wake dynamics can affect overall energy production. Without preventive initiatives, the effects of LEE increase over time, resulting in significant annual energy production (AEP) loss.
Leading-edge erosion (LEE) can impact wind turbine aerodynamics and wind farm efficiency. This...
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