Physical experimental static testing and structural design optimisation for a composite wind turbine blade

2017 ◽  
Vol 164 ◽  
pp. 90-103 ◽  
Author(s):  
E.M. Fagan ◽  
M. Flanagan ◽  
S.B. Leen ◽  
T. Flanagan ◽  
A. Doyle ◽  
...  
Keyword(s):  
Wind Turbine ◽  
Turbine Blade ◽  
Structural Design ◽  
Wind Turbine Blade ◽  
Design Optimisation ◽  
Static Testing

Monitoring Multi-Site Damage Growth During Quasi-Static Testing of a Wind Turbine Blade using a Structural Neural System

2008 ◽  
Vol 7 (2) ◽  
pp. 157-173 ◽  
Author(s):  
Goutham R. Kirikera ◽  
Vishal Shinde ◽  
Mark J. Schulz ◽  
Mannur J. Sundaresan ◽  
Scott Hughes ◽  
...  
Keyword(s):  
Wind Turbine ◽  
Turbine Blade ◽  
Neural System ◽  
Wind Turbine Blade ◽  
Damage Growth ◽  
Static Testing

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.

Aero-structural design and optimization of a small wind turbine blade

2016 ◽  
Vol 87 ◽  
pp. 837-848 ◽  
Author(s):  
Abolfazl Pourrajabian ◽  
Peyman Amir Nazmi Afshar ◽  
Mehdi Ahmadizadeh ◽  
David Wood
Keyword(s):  
Wind Turbine ◽  
Turbine Blade ◽  
Structural Design ◽  
Wind Turbine Blade ◽  
Design And Optimization ◽  
Small Wind Turbine

scholarly journals Structural Design and Analysis of a 10MW Wind Turbine Blade

2012 ◽  
Vol 24 ◽  
pp. 194-201 ◽  
Author(s):  
Kevin Cox ◽  
Andreas Echtermeyer
Keyword(s):  
Wind Turbine ◽  
Turbine Blade ◽  
Structural Design ◽  
Wind Turbine Blade
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