1.5MW Wind Turbine Structural Dynamic Analysis

2012 ◽  
Vol 522 ◽  
pp. 323-326
Author(s):  
Jie Kai Gong ◽  
Wen Lei Sun ◽  
An Wu
Keyword(s):  
Wind Turbine ◽  
Dynamic Model ◽  
Mode Shapes ◽  
Systematic Design ◽  
Structural Dynamic ◽  
Wind Field Model ◽  
Structural Dynamic Model ◽  
Design And Manufacturing ◽  
Shaft Torque ◽  
Important Significance

In recent decades, the rapidly development of wind energy in China and the increasing of size and complexity of wind turbine have requested the improvement in wind turbine systematic design technology. A reasonable systematic dynamic model is an important part for systematic design of MW-class wind turbine. In structural dynamic model, the flexibility of blade and tower is represented by presumed mode shapes. In this paper, based on presumed mode shape method, the structural dynamic equations of wind turbine were constructed. Along with the wind field model, the wind turbine aeroelastic systematic dynamic model was constructed. Using the model, the deflections and load of blade, low speed shaft torque of a 1.5MW wind turbine have been calculated. Therefore, the construction of wind turbines systematic dynamic model has an important significance for the development of wind systematic design and manufacturing capacity.

scholarly journals Neural Network Identification and Control of a Parametrically Excited Structural Dynamic Model of an F-15 Tail Section

2000 ◽  
Vol 7 (6) ◽  
pp. 355-361 ◽  
Author(s):  
Ayman A. El-Badawy ◽  
Ali H. Nayfeh ◽  
Hugh Van Landingham
Keyword(s):  
Neural Network ◽  
Computer Simulation ◽  
Dynamic Model ◽  
Parametric Excitation ◽  
Vibration Suppression ◽  
Great Promise ◽  
Structural Dynamic ◽  
Neural Network Controller ◽  
Structural Dynamic Model ◽  
Computer Simulation Studies

We investigated the design of a neural-network-based adaptive control system for a smart structural dynamic model of the twin tails of an F-15 tail section. A neural network controller was developed and tested in computer simulation for active vibration suppression of the model subjected to parametric excitation. First, an emulator neural network was trained to represent the structure to be controlled and thus used in predicting the future responses of the model. Second, a neurocontroller to determine the necessary control action on the structure was developed. The control was implemented through the application of a smart material actuator. A strain gauge sensor was assumed to be on each tail. Results from computer-simulation studies have shown great promise for control of the vibration of the twin tails under parametric excitation using artificial neural networks.

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