Comparison of Tensile Fatigue Resistance and Constant Life Diagrams for Several Potential Wind Turbine Blade Laminates

2009 ◽  
Vol 131 (1) ◽  
Author(s):  
Daniel D. Samborsky ◽  
Timothy J. Wilson ◽  
John F. Mandell
Keyword(s):  
Wind Turbine ◽  
Volume Fraction ◽  
Turbine Blades ◽  
Fatigue Loading ◽  
Loading Conditions ◽  
Test Results ◽  
Wind Turbine Blades ◽  
Fiber Volume ◽  
Tensile Fatigue ◽  
Level Constant

New fatigue test results are presented for four multidirectional laminates of current and potential interest for wind turbine blades, representing three types of fibers: E-glass, WindStrand™ glass, and carbon, all with epoxy resins. A broad range of loading conditions is included for two of the laminates, with the results represented as mean and 95∕95 confidence level constant life diagrams. The constant life diagrams are then used to predict the performance under spectrum fatigue loading relative to an earlier material. Comparisons of the materials show significant improvements under tensile fatigue loading for carbon, WindStrand, and one of the E-glass fabrics relative to many E-glass laminates in the 0.5–0.6 fiber volume fraction range. The carbon fiber dominated laminate shows superior fatigue and static strengths, as well as stiffness, for all loading conditions.

RESEARCH ON THE SELF-SYNCHRONOUS VIBRATION OF FATIGUE LOADING TESTS FOR WIND TURBINE BLADES

2016 ◽  
Vol 40 (5) ◽  
pp. 871-881
Author(s):  
Huang Xuemei ◽  
Zhang Lei’an ◽  
Tao Liming ◽  
Wei Xiuting
Keyword(s):  
Wind Turbine ◽  
Initial Phase ◽  
Turbine Blades ◽  
Fatigue Loading ◽  
Test System ◽  
The Self ◽  
Driving Frequency ◽  
Wind Turbine Blades ◽  
Loading System ◽  
Loading Tests

To carry on fatigue loading tests for wind turbine blades accurately, the self-synchronous vibration mechanism of loading system was investigated. Firstly, the mathematical model of vibration was deduced based on LaGrange Equation, thus the influence factors of self-synchronous vibration could be obtained. Then to study the influencing rules of the initial phase difference between loading equipment and blade, a simulating model was constructed to carry on the numerical simulation and it was found that when the driving frequency of the loading equipment was the same as the natural frequency of the blade, a different initial phase separation would generate different effect on self-synchronous vibration. Finally, an on-site fatigue test system was established to verify the accuracy of mathematical and simulation model mentioned above. It could be concluded that the test results were consistent with the simulating result. The research on the self-synchronous vibration performance of loading system for blade could supply a theory support for the sequent control of blade’s fatigue tests precisely.

Life prediction of wind turbine blades using multi-scale damage model

2021 ◽  
pp. 073168442199588
Author(s):  
Sepideh Aghajani ◽  
Mohammadreza Hemati ◽  
Shams Torabnia
Keyword(s):  
Wind Turbine ◽  
Turbine Blade ◽  
Fatigue Damage ◽  
Life Prediction ◽  
Turbine Blades ◽  
Damage Model ◽  
Fatigue Loading ◽  
Multiaxial Fatigue ◽  
Wind Turbine Blade ◽  
Wind Turbine Blades

Wind turbine blade life prediction is the most important parameter to estimate the power generation cost. Due to the price and importance of wind blade, many experimental and theoretical methods were developed to estimate damages and blade life. A novel multiaxial fatigue damage model is suggested for the life prediction of a wind turbine blade. Fatigue reduction of fiber and interfiber characteristics are separately treated and simulated in this research. Damage behavior is considered in lamina level and then extended to laminate; hence, this model can be used for multidirectional laminated composites. The procedure of fatigue-induced degradation is implemented in an ABAQUS user material subroutine. By applying the fatigue damage model, life is estimated by the satisfaction of lamina fracture criteria. This model provides a comprehensive idea about how damage happens in wind blades regarding a multi-axis fatigue loading condition.

Machine Learning Applications for a Wind Turbine Blade under Continuous Fatigue Loading

2013 ◽  
Vol 588 ◽  
pp. 166-174 ◽  
Author(s):  
Nikolaos Dervilis ◽  
M. Choi ◽  
Ifigeneia Antoniadou ◽  
K.M. Farinholt ◽  
S.G. Taylor ◽  
...  
Keyword(s):  
Machine Learning ◽  
Wind Turbine ◽  
Early Stage ◽  
Turbine Blades ◽  
Failure Detection ◽  
Fatigue Loading ◽  
Machine Learning Techniques ◽  
Wind Turbine Blades ◽  
Machine Learning Applications ◽  
Blade Failure

Structural health monitoring (SHM) systems will be one of the leading factors in the successful establishment of wind turbines in the energy arena. Detection of damage at an early stage is a vital issue as blade failure would be a catastrophic result for the entire wind turbine. In this study the SHM analysis will be based on experimental measurements of vibration analysis, extracted of a 9m CX-100 blade under fatigue loading. For analysis, machine learning techniques utilised for failure detection of wind turbine blades will be applied, like non-linear Neural Networks, including Auto-Associative Neural Network (AANN) and Radial Basis Function (RBF) networks models.

Edgewise vibration suppression of multi-megawatt wind turbine blades using passive tuned mass dampers

2020 ◽  
pp. 0309524X2095381
Author(s):  
Semyung Park ◽  
Matthew A Lackner
Keyword(s):  
Wind Turbine ◽  
Structural Control ◽  
Vibration Suppression ◽  
Linear Time ◽  
Turbine Blades ◽  
Fatigue Loading ◽  
Dynamic Responses ◽  
Control Effect ◽  
Wind Turbine Blades ◽  
Extreme Loads

The lack of aerodynamic damping of wind turbine blades in the edgewise direction causes larger dynamic responses and lowers the reliability. As blades become longer, edgewise fatigue loading increases rapidly. To mitigate the blade edgewise vibration, structural control techniques using a tuned mass damper (TMD) are applied in this paper. The “TMD” module in FASTv8 was upgraded to enable the high-fidelity simulation of structural control of the blade response. With the developed tool, the optimal parameters and generalized design formulas were established through a parametric study. Also, the control effect of the optimal blade-TMD on reducing fatigue and extreme loads of two different multi-megawatts turbine blades is investigated. Fully-coupled non-linear time marching simulations were conducted by running key design load cases (DLCs) with site-specific meteorological conditions. The results provide insight into the potential benefits and impacts of passive structural control to reduce the fatigue and extreme loads of turbine blades.

scholarly journals Delamination at Thick Ply Drops in Carbon and Glass Fiber Laminates Under Fatigue Loading

2008 ◽  
Vol 130 (3) ◽  
Author(s):  
Daniel D. Samborsky ◽  
Timothy J. Wilson ◽  
Pancasatya Agastra ◽  
John F. Mandell
Keyword(s):  
Wind Turbine ◽  
Glass Fiber ◽  
Carbon Fibers ◽  
Glass Fibers ◽  
Turbine Blades ◽  
Fatigue Loading ◽  
Analytical Models ◽  
Stress And Strain ◽  
Wind Turbine Blades ◽  
Static Loads

Delamination at ply drops in composites with thickness tapering has been a concern in applications of carbon fibers. This study explored the resistance to delamination under fatigue loading of carbon and glass fiber prepreg laminates with the same resin system, containing various ply drop geometries, and using thicker plies typical of wind turbine blades. Applied stress and strain levels to produce significant delamination at ply drops have been determined, and the experimental results correlated through finite element and analytical models. Carbon fiber laminates with ply drops, while performing adequately under static loads, delaminated in fatigue at low maximum strain levels except for the thinnest ply drops. The lower elastic modulus of the glass fiber laminates resulted in much higher strains to produce delamination for equivalent ply drop geometries. The results indicate that ply drops for carbon fibers should be much thinner than those commonly used for glass fibers in wind turbine blades.

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