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Application of artificial neural network for the prediction of pressure distribution of a plunging airfoil

Rasi Maezabadi, F ; Sharif University of Technology | 2009

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  1. Type of Document: Article
  2. Publisher: 2009
  3. Abstract:
  4. Series of experimental tests were conducted on a section of a 660 kW wind turbine blade to measure the pressure distribution of this model oscillating in plunging motion. In order to minimize the amount of data required to predict aerodynamic loads of the airfoil, a General Regression Neural Network, GRNN, was trained using the measured experimental data. The network once proved to be accurate enough, was used to predict the flow behavior of the airfoil for the desired conditions. Results showed that with using a few of the acquired data, the trained neural network was able to predict accurate results with minimal errors when compared with the corresponding measured values. Therefore with employing this trained network the aerodynamic coefficients of the plunging airfoil, are predicted accurately at different oscillation frequencies, amplitudes, and angles of attack; hence reducing the cost of tests while achieving acceptable accuracy
  5. Keywords:
  6. Airfoil ; Neural network ; Aerodynamic coefficients ; Angles of attack ; Artificial Neural Network ; Cost of tests ; Desired conditions ; Experimental ; Experimental data ; Experimental test ; Flow behaviors ; General regression neural network ; GRNN ; Minimal errors ; Oscillation frequency ; Plunging ; Plunging airfoil ; Trained neural networks ; Wind turbine blades ; Aerodynamics ; Cost reduction ; Neural networks ; Pressure distribution ; Wind power ; Airfoils
  7. Source: World Academy of Science, Engineering and Technology ; Volume 40 , 2009 , Pages 237-242 ; 2010376X (ISSN)
  8. URL: https://publications.waset.org/15072/application-of-artificial-neural-network-for-the-prediction-of-pressure-distribution-of-a-plunging-airfoil