Comparison of neural network types and architectures for generating a surrogate aerodynamic wind turbine blade model

2021 ◽  
Vol 216 ◽  
pp. 104696
Author(s):  
Eric Rowland Lalonde ◽  
Benjamin Vischschraper ◽  
Girma Bitsuamlak ◽  
Kaoshan Dai
Keyword(s):  
Neural Network ◽  
Wind Turbine ◽  
Turbine Blade ◽  
Wind Turbine Blade ◽  
Blade Model

Dynamic Analysis for Wind Turbine Composite Blade

2013 ◽  
Vol 364 ◽  
pp. 102-106 ◽  
Author(s):  
Li Qun Zhou ◽  
Shuai Heng Xing ◽  
Yu Ping Li
Keyword(s):  
Wind Turbine ◽  
Turbine Blade ◽  
Wind Turbine Blade ◽  
Inertia Force ◽  
Rotational Inertia ◽  
Fiber Layer ◽  
Composite Blade ◽  
Nature Frequency ◽  
The Relationship ◽  
Blade Model

Wind turbine blade model is analyzed based on finite element method. Research and comparison of blade natural frequencies is made in different rotational working conditions taking into account external factors such as the rotational inertia force. Also the relationship between the composite ply angle and natural frequency is analyzed. The result shows that the nature frequency of wind turbine blade is influence greatly by the stress stiffening effect for the blade rotation. And the nature frequency of wind turbine blade can be designed by adjusting the single fiber layer ply angle of blade.

scholarly journals Influence of the wind turbine blade model on echo's Doppler characteristics

2019 ◽  
Vol 2019 (16) ◽  
pp. 1419-1422 ◽  
Author(s):  
Bo Tang ◽  
Bin Hao ◽  
Li Huang ◽  
Jiawei Yang
Keyword(s):  
Wind Turbine ◽  
Turbine Blade ◽  
Wind Turbine Blade ◽  
Blade Model

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 Performance of a wind turbine blade in sandstorms using a CFD-BEM based neural network

2020 ◽  
Vol 12 (5) ◽  
pp. 053310
Author(s):  
Iham F. Zidane ◽  
Greg Swadener ◽  
Xianghong Ma ◽  
Mohamed F. Shehadeh ◽  
Mahmoud H. Salem ◽  
...  
Keyword(s):  
Neural Network ◽  
Wind Turbine ◽  
Turbine Blade ◽  
Wind Turbine Blade

Crack Diagnosis Method of Wind Turbine Blade Based on Convolution Neural Network with 3D Vibration Information Fusion

2020 ◽  
Vol 40 (22) ◽  
pp. 2212004
Author(s):  
郭迎福 Guo Yingfu ◽  
全伟铭 Quan Weiming ◽  
王文韫 Wang Wenyun ◽  
周浩 Zhou Hao ◽  
邹龙洲 Zou Longzhou
Keyword(s):  
Neural Network ◽  
Wind Turbine ◽  
Turbine Blade ◽  
Information Fusion ◽  
Wind Turbine Blade ◽  
Convolution Neural Network ◽  
Diagnosis Method
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