Modelling rain erosion surface damage initiation in turbine blades based on inspection data at wind farms
Year: 2026
Type: article
Abstract: Leading-edge erosion induced by raindrop impacts on coated wind turbine blades is a ubiquitous phenomenon in onshore and offshore wind farms that causes blade material surface damage that progresses through thickness in composite multilayer configu- ration and finally yields reduction in energy production due to aerodynamic loss. A numerical tool for making accurate prediction on blade’s erosion incubation lifetime subject to the individual turbine’s operation conditions in field is important to guide inspec- tion and maintenance. Based on the wind industrial recommended practice DNVGL-RP-0573, this paper develops a novel model called Blade Erosion Evaluation Tool (BEETool) for rotor blade erosion initiation damage predictions. In particular, the BEETool model factorizes the significance of material properties, rain and wind loads to blade erosion contributions. The model has been compared with simulated results obtained from industrial standardized approach, and also with wind farm damage observations. It has proven to be a good complement for damage forecasting analysis. Moreover, the model has provided excellent initiation damage predictions based on field data in both onshore and offshore wind farms with different rain and wind conditions. The BEETool model focuses on the application in operating wind farms based on field observations, instead of laboratory experimental data prior installation, allowing wind farm owners to make reliable blade erosion damage initiation assessments without detailed manufacturing data which may not always be readily available during operational lifetime.
Source: Results in Engineering
Authors: Hao Hao, Luis Domenech, Fernando Sánchez
Institutions: Imperial College London, Universidad Cardenal Herrera CEU
Language: English
Subfield: Ecological Modeling
Field: Environmental Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,940
Awards: G2955041059, G5450596875
OpenAlex ID: W7128773353
