Revisiting the structural collapse of a 52.3 m composite wind turbine blade in a full-scale bending test

Wind Energy ◽  
2017 ◽  
Vol 20 (6) ◽  
pp. 1111-1127 ◽  
Author(s):  
Xiao Chen ◽  
Xiaolu Zhao ◽  
Jianzhong Xu
Keyword(s):  
Wind Turbine ◽  
Turbine Blade ◽  
Full Scale ◽  
Wind Turbine Blade ◽  
Bending Test ◽  
Structural Collapse

Structural investigation of composite wind turbine blade considering structural collapse in full-scale static tests

2013 ◽  
Vol 97 ◽  
pp. 15-29 ◽  
Author(s):  
Jinshui Yang ◽  
Chaoyi Peng ◽  
Jiayu Xiao ◽  
Jingcheng Zeng ◽  
Suli Xing ◽  
...  
Keyword(s):  
Wind Turbine ◽  
Turbine Blade ◽  
Structural Investigation ◽  
Full Scale ◽  
Wind Turbine Blade ◽  
Structural Collapse ◽  
Static Tests

A method of determining test load for full-scale wind turbine blade fatigue tests

2018 ◽  
Vol 32 (11) ◽  
pp. 5097-5104 ◽  
Author(s):  
Qiang Ma ◽  
Zong-Wen An ◽  
Jian-Xiong Gao ◽  
Hai-Xia Kou ◽  
Xue-Zong Bai
Keyword(s):  
Wind Turbine ◽  
Turbine Blade ◽  
Fatigue Tests ◽  
Full Scale ◽  
Wind Turbine Blade ◽  
Test Load

scholarly journals Virtual full-scale testing for investigating strength characteristics of a composite wind turbine blade

2020 ◽  
Author(s):  
Can Muyan ◽  
Demirkan Coker
Keyword(s):  
Wind Turbine ◽  
Turbine Blade ◽  
Mechanical Response ◽  
Leading Edge ◽  
Full Scale ◽  
Wind Turbine Blade ◽  
Combined Loading ◽  
Strength Analysis ◽  
Damage Development ◽  
Extreme Load

Abstract. Full-scale structural tests enable us to monitor mechanical response of the blades under various loading scenarios. Yet these tests must be accompanied with numerical simulations, so that the physical basis of the progressive damage development can be captured and interpreted correctly. Within the scope of this paper the previous work of the authors concerning the strength analysis of an existing 5-m GFRP wind turbine blade using Puck failure criteria is revisited. An important outcome of the previous study was that nonlinear Puck material model was found to be necessary for a more realistic simulation of failure mechanisms. In the current work, under extreme load cases internal flange at the leading edge, trailing edge of the blade are identified as the mainly damaged regions. Moreover, dominant failure mechanism is expected to be the de-bonding at the trailing and leading edges. When extreme load case is applied as a combination of edge-wise and flap-wise loading cases, less damage is observed compared to the pure flap-wise loading case. This damage evolution is attributed to the stiffer structural behavior of the blade under combined loading condition.

Structural collapse characteristics of a 48.8 m wind turbine blade under ultimate bending loading

2019 ◽  
Vol 106 ◽  
pp. 104150 ◽  
Author(s):  
Lei'an Zhang ◽  
Yanzhen Guo ◽  
Liangfeng Yu ◽  
Xiuting Wei ◽  
Weisheng Liu ◽  
...  
Keyword(s):  
Wind Turbine ◽  
Turbine Blade ◽  
Wind Turbine Blade ◽  
Structural Collapse ◽  
Collapse Characteristics ◽  
Bending Loading

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.

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