A comparative multi-objective investigation for metallic and non-metallic materials on the erosion resistance of wind turbine blade with performance evaluation. [PDF]
Sci RepAbou Taleb ASA, Abdelgaliel IH, Aly MF.
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Image Recognition of Wind Turbine Blade Defects Using Attention-Based MobileNetv1-YOLOv4 and Transfer Learning. [PDF]
Sensors (Basel), 2022 Zhang C, Yang T, Yang J.
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Computational Analysis of a Wind Turbine Blade for Different Advanced Materials. [PDF]
Materials (Basel)Fragkos PF, Theotokoglou EE.
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The Springer Model for Lifetime Prediction of Wind Turbine Blade Leading Edge Protection Systems: A Review and Sensitivity Study. [PDF]
Materials (Basel), 2022 Hoksbergen N, Akkerman R, Baran I.
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Enhancing wind turbine blade damage detection with YOLO-Wind. [PDF]
Sci RepZhanfang Z, Tuo L.
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The Role of Orientation and Temperature on the Mechanical Properties of a 20 Years Old Wind Turbine Blade GFR Composite. [PDF]
Polymers (Basel), 2021 Ahmed MMZ +4 more
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Turbine-to-Textile: Upcycling Wind Turbine Blade Waste into High-Performance PAN Composite Fibers. [PDF]
ACS Appl Polym MaterThippanna V +15 more
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Wind Turbine Blade Defect Recognition Method Based on Large-Vision-Model Transfer Learning. [PDF]
Sensors (Basel)Li X +5 more
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Optimization of Hierarchical Groove-Perforation Structures in PET Foam Cores for Wind Turbine Blade Applications. [PDF]
Materials (Basel)Li J, Lin G, Chen X.
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Wind turbine blade damage detection based on acoustic signals. [PDF]
Sci RepYang C, Ding S, Zhou G.
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