scholarly journals Experimental Study of Stepped-Lap Scarf Joint Repair for Spar Cap Damage of Wind Turbine Blade in Service

2020 ◽  
Vol 10 (3) ◽  
pp. 922
Author(s):  
Hui Li ◽  
Cheng Chen ◽  
Tongguang Wang ◽  
Long Wang
Keyword(s):  
Crack Propagation ◽  
Tensile Test ◽  
Wind Turbine ◽  
Turbine Blade ◽  
Wind Turbine Blade ◽  
Joint Surface ◽  
Lap Joint ◽  
Scarf Joint ◽  
Static Tensile ◽  
Joint Repair

The objective of this paper was to design configuration parameters for a stepped-lap scarf joint repair, which can be used for spar cap damage of a wind turbine blade in service and to realize the post-repair monitoring. Two experimental studies were included. First, tensile test for the unidirectional tape specimens with a large aspect ratio repaired using a multiple stepped-lap scarf joint method was carried out. The results showed that the reinforcement layer could effectively improve the load-carrying capacity of the repaired zone. The stepped-lap joint surface was identified as the weak part of the spar cap repair, which should be monitored. Second, by embedding carbon nanotube buckypaper sensors on the stepped-lap joint surface of the repaired specimens, quasi-static tensile tests and fatigue tests were carried out. According to the resistance response of the sensors, the quasi-static tensile test confirmed the failure processes, namely the stiffness turning point, damage evolution, crack propagation, and fracture. The fatigue test could accurately identify the progressive failure, namely the initial damage, damage accumulation, initial cracking, and crack propagation to structural failure. The above tests provided an important configuration parameter basis for evaluating the spar cap repair scheme and presented a promising method for the health monitoring of a spar cap after repair.

scholarly journals Full-Scale Fatigue Testing of a Wind Turbine Blade in Flapwise Direction and Examining the Effect of Crack Propagation on the Blade Performance

Materials ◽  
2017 ◽  
Vol 10 (10) ◽  
pp. 1152 ◽  
Author(s):  
Othman Al-Khudairi ◽  
Homayoun Hadavinia ◽  
Christian Little ◽  
Gavin Gillmore ◽  
Peter Greaves ◽  
...  
Keyword(s):  
Crack Propagation ◽  
Wind Turbine ◽  
Turbine Blade ◽  
Fatigue Testing ◽  
Full Scale ◽  
Wind Turbine Blade

Noise analysis of the wind turbine blade

2013 ◽  
Author(s):  
Gwochung Tsai ◽  
Yita Wang ◽  
Yuhchung Hu ◽  
Jaching Jiang
Keyword(s):  
Wind Turbine ◽  
Turbine Blade ◽  
Noise Analysis ◽  
Wind Turbine Blade

DYNAMIC ANALYSIS OF THE FLUID FLOW OVER A WIND TURBINE BLADE

2017 ◽  
Author(s):  
Aldemir Ap Cavalini Jr ◽  
João Marcelo Vedovoto ◽  
Renata Rocha
Keyword(s):  
Fluid Flow ◽  
Dynamic Analysis ◽  
Wind Turbine ◽  
Turbine Blade ◽  
Wind Turbine Blade

scholarly journals Image Enhanced Mask R-CNN: A Deep Learning Pipeline with New Evaluation Measures for Wind Turbine Blade Defect Detection and Classification

2021 ◽  
Vol 7 (3) ◽  
pp. 46
Author(s):  
Jiajun Zhang ◽  
Georgina Cosma ◽  
Jason Watkins
Keyword(s):  
Deep Learning ◽  
Wind Turbine ◽  
Turbine Blade ◽  
Defect Detection ◽  
Recognition Rate ◽  
Weighted Average ◽  
Wind Turbine Blade ◽  
Evaluation Measures ◽  
Augmentation Techniques ◽  
The Mean

Demand for wind power has grown, and this has increased wind turbine blade (WTB) inspections and defect repairs. This paper empirically investigates the performance of state-of-the-art deep learning algorithms, namely, YOLOv3, YOLOv4, and Mask R-CNN for detecting and classifying defects by type. The paper proposes new performance evaluation measures suitable for defect detection tasks, and these are: Prediction Box Accuracy, Recognition Rate, and False Label Rate. Experiments were carried out using a dataset, provided by the industrial partner, that contains images from WTB inspections. Three variations of the dataset were constructed using different image augmentation settings. Results of the experiments revealed that on average, across all proposed evaluation measures, Mask R-CNN outperformed all other algorithms when transformation-based augmentations (i.e., rotation and flipping) were applied. In particular, when using the best dataset, the mean Weighted Average (mWA) values (i.e., mWA is the average of the proposed measures) achieved were: Mask R-CNN: 86.74%, YOLOv3: 70.08%, and YOLOv4: 78.28%. The paper also proposes a new defect detection pipeline, called Image Enhanced Mask R-CNN (IE Mask R-CNN), that includes the best combination of image enhancement and augmentation techniques for pre-processing the dataset, and a Mask R-CNN model tuned for the task of WTB defect detection and classification.

Impact of dielectric barrier discharge plasma on the wake of a wind turbine blade section oscillating in plunge

2021 ◽  
pp. 095765092110337
Author(s):  
GH Maleki ◽  
Ali R Davari ◽  
MR Soltani
Keyword(s):  
Wind Turbine ◽  
Dielectric Barrier Discharge ◽  
Turbine Blade ◽  
Discharge Plasma ◽  
Barrier Discharge ◽  
Wind Turbine Blade ◽  
Plasma Actuators ◽  
Dielectric Barrier Discharge Plasma ◽  
Dielectric Barrier ◽  
Barrier Discharge Plasma

Effects of dielectric barrier discharge plasma have been studied on the wake velocity profiles of a section of a 660 kW wind turbine blade in plunging motion in a wind tunnel. The corresponding unsteady velocity profiles show remarkable improvement when the plasma actuators were operating and the angles of attack of the model were beyond the static stall angles of the airfoil. As a result the drag force was considerably reduced. It is further observed that the plasma-induced flow attenuates the leading edge vortices that are periodically shed into wake and diminishes the large eddies downstream. The favorable effects of the plasma augmentation are shown to occur near the uppermost and lowermost positions of the plunging paths where the wake is primarily dominated by the vortices of the same sign. The wake structure in the presence of the flow induced by the plasma actuators shows that the actual effective angles of attack seen by the plunging airfoil reduces in comparison with that for the case of the plasma augmentation off situation.

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