scholarly journals Modal testing of the TX-100 wind turbine blade.

2006 ◽  
Author(s):  
Sarah Reese ◽  
Daniel Todd Griffith ◽  
Miguel Casias ◽  
Todd William Simmermacher ◽  
Gregory A. Smith
Keyword(s):  
Wind Turbine ◽  
Turbine Blade ◽  
Wind Turbine Blade ◽  
Modal Testing

scholarly journals Structural Design, Analysis, and Testing of a 10 kW Fabric-Covered Wind Turbine Blade

Energies ◽  
2020 ◽  
Vol 13 (12) ◽  
pp. 3276
Author(s):  
Dong-Kuk Choi ◽  
Bong-Do Pyeon ◽  
Soo-Yong Lee ◽  
Hak-Gu Lee ◽  
Jae-Sung Bae
Keyword(s):  
Wind Turbine ◽  
Turbine Blade ◽  
Wind Turbine Blade ◽  
Structural Testing ◽  
Modal Testing ◽  
Mode Shapes ◽  
Variational Asymptotic ◽  
Design Loads ◽  
Sectional Analysis ◽  
Sufficient Strength

Reducing the weight of a wind turbine blade is a major issue. Wind turbines have become larger in size to increase power generating efficiency. The blade has also grown in length to take more wind energy. A fabric-based wind turbine blade, introduced by General Electric Co., reduced the blade weight. In this study, a small fabric-covered blade for a 10 kW wind turbine was developed to verify structural ability. The blade was designed on the cross-section using variational asymptotic beam sectional analysis (VABS), structural analysis was carried out using MSC.Nastran for the design loads. A modal analysis was performed to compare the modal frequency and mode shapes. Static structural testing and modal testing were fulfilled. The analysis results were compared with the testing results. The fabric-covered structure was confirmed to reduce the blade mass with sufficient strength.

scholarly journals Single Output and Algebraic Modal Parameters Identification of a Wind Turbine Blade: Experimental Results

2021 ◽  
Vol 11 (7) ◽  
pp. 3016
Author(s):  
Luis Gerardo Trujillo-Franco ◽  
Hugo Francisco Abundis-Fong ◽  
Rafael Campos-Amezcua ◽  
Roberto Gomez-Martinez ◽  
Armando Irvin Martinez-Perez ◽  
...  
Keyword(s):  
Wind Turbine ◽  
Turbine Blade ◽  
Wind Turbine Blade ◽  
Parameters Identification ◽  
Parametric Estimation ◽  
Modal Testing ◽  
Modal Parameters ◽  
Modal Parameter ◽  
Single Output ◽  
Modal Parameters Identification

This paper describes the evaluation of a single output, online, and time domain modal parameters identification technique based on differential algebra and operational calculus. In addition, an analysis of the frequency response function (FRF) of the system is conducted in a specific set up, emulating its nominal or operational conditions to determine the influence of the non-linearities over the dynamic behavior of the system in those particular magnitudes of deformations; thus, this influence is quantified by a numerical index. This methodology is applied to a wind turbine blade submitted to wind tunnel experiments. The natural frequencies and modal damping ratios of six bending modes associated with the blade are estimated using real-time velocity measurements from one single point of the blade. A comparison with the usual impact hammer modal testing is performed to evaluate and establish the proposed approach’s main contributions. The developed modal parameter identification algorithms are implemented to run into a standard personal computer (PC) where the data acquisition system’s measurements are conditioned and processed. The results show the performance and the fast parametric estimation of the proposed algebraic identification approach.

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.

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