scholarly journals Full-scale fatigue tests of CX-100 wind turbine blades. Part II: analysis

2012 ◽  
Author(s):  
Stuart G. Taylor ◽  
Hyomi Jeong ◽  
Jae Kyeong Jang ◽  
Gyuhae Park ◽  
Kevin M. Farinholt ◽  
...  
Keyword(s):  
Wind Turbine ◽  
Turbine Blades ◽  
Fatigue Tests ◽  
Full Scale ◽  
Wind Turbine Blades

scholarly journals Full-scale fatigue tests of CX-100 wind turbine blades. Part I: testing

2012 ◽  
Author(s):  
Kevin M. Farinholt ◽  
Stuart G. Taylor ◽  
Gyuhae Park ◽  
Curtt M. Ammerman
Keyword(s):  
Wind Turbine ◽  
Turbine Blades ◽  
Fatigue Tests ◽  
Full Scale ◽  
Wind Turbine Blades

scholarly journals Fatigue in Fiber-Metal Laminates for Small Wind Turbine Blades Application

2018 ◽  
Vol 165 ◽  
pp. 07005
Author(s):  
Wei Sai ◽  
Gin Boay Chai
Keyword(s):  
Wind Turbine ◽  
Turbine Blades ◽  
Maximum Load ◽  
Tensile Tests ◽  
Fatigue Tests ◽  
Wind Condition ◽  
Wind Turbine Blades ◽  
Operation Condition ◽  
Small Wind Turbine ◽  
Fiber Metal

A methodology to study the fatigue of a wind turbine blade in a 10KW small wind turbine is proposed in this paper. Two working conditions (namely normal fatigue operation condition and extreme wind condition) are considered based on IEC61400-2. The maximum load calculated from both cases were used as a reference to perform material sample fatigue study. Fiber-metal laminate – GLARE 3/2 with a centre 1mm notch on the external aluminium layers was modelled based on fracture mechanics approach to calculate the stress intensity factor and fatigue crack growth rate at maximum applied stress of 240Mpa. GLARE panel fabrication and tensile tests were included. The fatigue tests were performed on unnotched samples with stress range from 80Mpa to 300Mpa and plotted into S-N curve.

Evaluation of dual-axis fatigue testing of large wind turbine blades

2011 ◽  
Vol 226 (7) ◽  
pp. 1693-1704 ◽  
Author(s):  
Peter R Greaves ◽  
Robert G Dominy ◽  
Grant L Ingram ◽  
Hui Long ◽  
Richard Court
Keyword(s):  
Service Life ◽  
Wind Turbine ◽  
Fatigue Testing ◽  
Turbine Blades ◽  
Bending Moment ◽  
Fatigue Tests ◽  
Simulation Software ◽  
Wind Turbine Blades ◽  
Moment Distribution ◽  
Large Wind

Full-scale fatigue testing is part of the certification process for large wind turbine blades. That testing is usually performed about the flapwise and edgewise axes independently but a new method for resonant fatigue testing has been developed in which the flapwise and edgewise directions are tested simultaneously, thus also allowing the interactions between the two mutually perpendicular loads to be investigated. The method has been evaluated by comparing the Palmgren–Miner damage sum around the cross-section at selected points along the blade length that results from a simulated service life, as specified in the design standards, and testing. Bending moments at each point were generated using wind turbine simulation software and the test loads were designed to cause the same amount of damage as the true service life. The mode shape of the blade was tuned by optimising the position of the excitation equipment, so that the bending moment distribution was as close as possible to the target loads. The loads were converted to strain–time histories using strength of materials approach, and fatigue analysis was performed. The results show that if the bending moment distribution is correct along the length of the blade, then dual-axis resonant testing tests the blade much more thoroughly than sequential tests in the flapwise and edgewise directions. This approach is shown to be more representative of the loading seen in service and can thus contribute to a potential reduction in the weight of wind turbine blades and the duration of fatigue tests leading to reduced cost.

scholarly journals PSO-BP Neural Network-Based Strain Prediction of Wind Turbine Blades

Materials ◽  
2019 ◽  
Vol 12 (12) ◽  
pp. 1889 ◽  
Author(s):  
Xin Liu ◽  
Zheng Liu ◽  
Zhongwei Liang ◽  
Shun-Peng Zhu ◽  
José A. F. O. Correia ◽  
...  
Keyword(s):  
Neural Network ◽  
Wind Turbine ◽  
Turbine Blade ◽  
Turbine Blades ◽  
Full Scale ◽  
Wind Turbine Blade ◽  
Blade Design ◽  
Wind Turbine Blades ◽  
Static Testing ◽  
Bpnn Model

The full-scale static testing of wind turbine blades is an effective means to verify the accuracy and rationality of the blade design, and it is an indispensable part in the blade certification process. In the full-scale static experiments, the strain of the wind turbine blade is related to the applied loads, loading positions, stiffness, deflection, and other factors. At present, researches focus on the analysis of blade failure causes, blade load-bearing capacity, and parameter measurement methods in addition to the correlation analysis between the strain and the applied loads primarily. However, they neglect the loading positions and blade displacements. The correlation among the strain and applied loads, loading positions, displacements, etc. is nonlinear; besides that, the number of design variables is numerous, and thus the calculation and prediction of the blade strain are quite complicated and difficult using traditional numerical methods. Moreover, in full-scale static testing, the number of measuring points and strain gauges are limited, so the test data have insufficient significance to the calibration of the blade design. This paper has performed a study on the new strain prediction method by introducing intelligent algorithms. Back propagation neural network (BPNN) improved by Particle Swarm Optimization (PSO) has significant advantages in dealing with non-linear fitting and multi-input parameters. Models based on BPNN improved by PSO (PSO-BPNN) have better robustness and accuracy. Based on the advantages of the neural network in dealing with complex problems, a strain-predictive PSO-BPNN model for full-scale static experiment of a certain wind turbine blade was established. In addition, the strain values for the unmeasured points were predicted. The accuracy of the PSO-BPNN prediction model was verified by comparing with the BPNN model and the simulation test. Both the applicability and usability of strain-predictive neural network models were verified by comparing the prediction results with simulation outcomes. The comparison results show that PSO-BPNN can be utilized to predict the strain of unmeasured points of wind turbine blades during static testing, and this provides more data for characteristic structural parameters calculation.

scholarly journals GA-BP Neural Network-Based Strain Prediction in Full-Scale Static Testing of Wind Turbine Blades

Energies ◽  
2019 ◽  
Vol 12 (6) ◽  
pp. 1026 ◽  
Author(s):  
Zheng Liu ◽  
Xin Liu ◽  
Kan Wang ◽  
Zhongwei Liang ◽  
José A.F.O. Correia ◽  
...  
Keyword(s):  
Wind Turbine ◽  
Turbine Blades ◽  
Back Propagation ◽  
Health Assessment ◽  
Prediction Method ◽  
Full Scale ◽  
Wind Turbine Blades ◽  
Static Test ◽  
Static Testing ◽  
Input Variables

This paper proposes a strain prediction method for wind turbine blades using genetic algorithm back propagation neural networks (GA-BPNNs) with applied loads, loading positions, and displacement as inputs, and the study can be used to provide more data for the wind turbine blades’ health assessment and life prediction. Among all parameters to be tested in full-scale static testing of wind turbine blades, strain is very important. The correlation between the blade strain and the applied loads, loading position, displacement, etc., is non-linear, and the number of input variables is too much, thus the calculation and prediction of the blade strain are very complex and difficult. Moreover, the number of measuring points on the blade is limited, so the full-scale blade static test cannot usually provide enough data and information for the improvement of the blade design. As a result of these concerns, this paper studies strain prediction methods for full-scale blade static testing by introducing GA-BPNN. The accuracy and usability of the GA-BPNN prediction model was verified by the comparison with BPNN model and the FEA results. The results show that BPNN can be effectively used to predict the strain of unmeasured points of wind turbine blades.

Analysis of the Aeroelastic Dynamics of Wind-Turbine Blades

2012 ◽  
Author(s):  
Lucas I. Lago ◽  
Fernando L. Ponta ◽  
Alejandro D. Otero
Keyword(s):  
Wind Turbine ◽  
Strain Energy ◽  
Structural Model ◽  
Numerical Models ◽  
Turbine Blades ◽  
Computational Cost ◽  
Full Scale ◽  
Wind Turbine Blades ◽  
Time Step ◽  
Aerodynamic Model

Reducing the uncertainties related to blade dynamics by the improvement of the quality of numerical simulations of the fluid structure interaction process is a key for a breakthrough in windturbine technology. A fundamental step in that direction is the implementation of aeroelastic models capable of capturing the complex features of innovative prototype blades, so they can be tested at realistic full-scale conditions with a reasonable computational cost. We make use of a code based on a combination of two advanced numerical models implemented in a parallel HPC supercomputer platform: First, a model of the structural response of heterogeneous composite blades, based on a variation of the dimensional reduction technique proposed by Hodges and Yu. This technique has the capacity of reducing the geometrical complexity of the blade section into a stiffness matrix for an equivalent beam. The reduced 1-D strain energy is equivalent to the actual 3-D strain energy in an asymptotic sense, allowing accurate modeling of the blade structure as a1-D finite-element problem. This substantially reduces the computational effort required to model the structural dynamics at each time step. Second, a novel aerodynamic model based on an advanced implementation of the BEM (Blade Element Momentum) Theory; where all velocities and forces are re-projected through orthogonal matrices into the instantaneous deformed configuration to fully include the effects of large displacements and rotation of the airfoil sections into the computation of aerodynamic forces. This allows the aerodynamic model to take into account the effects of the complex flexo-torsional deformation that can be captured by the more sophisticated structural model mentioned above. In this presentation, we report some recent results we have obtained applying our code to full-scale composite laminate wind-turbine blades, analyzing the fundamental vibrational modes and the stress load in normal operational conditions.

A review of full-scale structural testing of wind turbine blades

2014 ◽  
Vol 33 ◽  
pp. 177-187 ◽  
Author(s):  
H.F. Zhou ◽  
H.Y. Dou ◽  
L.Z. Qin ◽  
Y. Chen ◽  
Y.Q. Ni ◽  
...  
Keyword(s):  
Wind Turbine ◽  
Turbine Blades ◽  
Full Scale ◽  
Structural Testing ◽  
Wind Turbine Blades

Structural Analysis of Wind-Turbine Blades by a Generalized Timoshenko Beam Model

2010 ◽  
Vol 132 (1) ◽  
Author(s):  
Alejandro D. Otero ◽  
Fernando L. Ponta
Keyword(s):  
Wind Turbine ◽  
Turbine Blades ◽  
3D Structure ◽  
Computational Cost ◽  
Full Scale ◽  
Beam Model ◽  
Wind Turbine Blades ◽  
Operational Conditions ◽  
Variational Asymptotic ◽  
Complex Features

An important aspect in wind-turbine technology nowadays is to reduce the uncertainties related to blade dynamics by the improvement of the quality of numerical simulations of the fluid-structure interaction process. A fundamental step in that direction is the implementation of structural models capable of capturing the complex features of innovative prototype blades, so that they can be tested at realistic full-scale conditions with a reasonable computational cost. To this end, we developed a code based on a modified implementation of the variational-asymptotic beam sectional (VABS) technique proposed by Hodges et al. VABS has the capacity of reducing the geometrical complexity of the blade section into a stiffness matrix for an equivalent beam, allowing accurate modeling of the 3D structure of the blade as a 1D finite-element problem. In this paper, we report some recent results we have obtained by applying our code to full-scale composite laminate wind-turbine blades, analyzing the fundamental vibrational modes and the stress load in normal operational conditions.

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