BLEEPID
Towards improved reliability and reduced costs of offshore wind by blade-leading-edge erosion prediction and drone-based inspection
Introduction
Leading edge erosion of offshore wind blades has drastic impact on maintenance costs and operational energy production and may lead to unwanted microplastics in the environment. Herein, we will develop fundamental image capturing and camera techniques, combined with quantitative wear analysis from experimental erosion testing and multi-physics modelling combining CFD-FSI impact and subsurface fatigue modelling. These advances will enable accurate drone-based inspections of offshore wind blades, and thus optimize maintenance planning and extent the lifetime of the blade.
Context
By 2030, wind energy should yield an annual energy production of 25 TWh green electricity, hereby covering 25-30% of the Belgian electricity demand. Leading-edge erosion (LEE) is a severe erosive wear mechanism which involves progressive material removal from the blade-tip leading edges, due to (sub-)surface fatigue. The repeated highspeed impact of water droplets induces severe pressure shock-waves in the blade material leading to initiation, propagation and coalescence of cracks and eventually material loss, pit formation, delamination and disintegration of the structural integrity. Although wind turbines are designed and built for an operational lifespan of 25 years at maximum energy capacity, current generation of turbines require often extensive maintenance after 5-10 years due to severe LEE and reduced energy production.
Today inspection of wind turbine blades is mostly performed manually by personnel on-site. Drone-based inspections using high-resolution camera techniques become increasingly more popular and may provide valuable data to support such costly decisions concerning maintenance and repair. A lot can be gained since the impact of LEE for the Belgian wind farms is substantial: 1) drastic impact on O&M, (2) energy production loss due to changing drag and lift coefficients and (3) marine pollution due to breaking away of microplastics and polymeric materials.
Goal, approach and results
The project aims to improve the maintenance planning and operation control of offshore wind farms, by using camera-equipped drones to remotely inspect the status of blades and make accurate, quantitative measurements of the erosive wear using accurate images combined with fundamental wear characterization using both experiments and modelling. Four objectives are identified which contribute to mitigating LEE:
To develop image-capturing and analysis methods for high-accurate drone-based LEE inspection. To contribute to reliable predictive maintenance tools, by developing both adequate data-driven and physics-based models for the description and prediction of the evolution of leading-edge erosion based on a current blade-state and precipitation parameters. To provide detailed insight in the interplay of liquid droplet impact on representative leading edge protections using controlled experiments and multi-physics modelling, with the aim to improve those materials in the future or to provide minimum LEP specifications to the wind-park owners. To develop a framework and model to evaluate the socio-economic impact of the newly developed LEE detection and prediction models in terms of levelized cost of energy (LCOE) and their carbon footprint. Simulate the socio-economic impact of these new models on the Princess Elisabeth offshore wind farm development zone. Within the project Sirris develops a framework and model to evaluate the socio-economic impact of the newly developed LEE detection and prediction models in terms of levelized cost of energy (LCOE) and their carbon footprint. The socio-economic impact of these new models will be simulated on the Princess Elisabeth offshore wind farm development zone.
Results
WP2:
Journal article: Sterckx, Jonathan and Luong, Hiep and Vlaminck, Michiel and De Bauw, Koenraad, Accurate and Robust 3d Reconstruction of the Leading Edge of Wind Turbine Blades from High-Resolution Images. Automation in Construction (2025), 175, 106153 (https://doi.org/10.1016/j.autcon.2025.106153)
Journal Article: Sterckx, Jonathan and Vlaminck, Michiel and Luong, Hiep, Segmentation and quantification of surface defects in 3D reconstructions for damage assessment and inspection. (2025) IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING. 22. p.19439-19450 (10.1109/TASE.2025.3593967)
Poster presentation: i-Know Innovation Day 2023 (https://iknow.ugent.be/)
Poster presentation: FEARS 2024 (https://fears.ugent.be/)
Researcher Jonathan Sterckx has won the prestigious AI4Blades Challenge, in a team with Hiep Luong, Michiel Vlaminck and Bert Conings, and was awarded the price at the Technology Workshop of WindEurope (June 2025, in Istanbul)
WP4:
Poster presentation: Ramachandran Nambiar, Vinayak, et al. “CFD-FSI MODELLING OF WIND TURBINE BLADE LEADING EDGE EROSION.” Faculty of Engineering and Architecture Research Symposium (FEARS) 2023, UGent, 2023.
Poster presentation: Shadmani, A., De Waele, W., & Fauconnier, D. (2023). Probabilistic Peridynamics model for damage calculation of wind turbine blades. 19th EAWE PhD Seminar: Book of Proceedings, 150–153. European Academy of Wind Energy.
Results from T4.1 will result in a scientific publication, which will detail the pressures, stresses, and solid deformations for various impact conditions.
The damage model on leading-edge erosion will be part of a planned scientific publication.
Flyer with key takeaways
Publications
CFD-FSI modelling of wind turbine blade leading edge erosion
Accurate and robust 3D reconstruction of wind turbine blade leading edges_BLEEPID_deliverable_2_1_3
Segmentation and Quantification of Surface Defects-BLEEPID_deliverable_2_2_2
Blade leading edge erosion prediction & drone-based inspection (BLEEPID)-iKnow_BLEEPID_poster
Blade erosion quantification by drone-captured high-resolution images – FEARS2024_BLEEPID-1
Project funded by
