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Unsteady three dimensional aerodynamic load prediction using neural networks

Soltani, M. R ; Sharif University of Technology | 2007

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  1. Type of Document: Article
  2. DOI: 10.1109/IJCNN.2007.4371264
  3. Publisher: 2007
  4. Abstract:
  5. The focus of the current research is to develop an intelligent design process that uses existing data as a tool for the designers, one that fully utilizes the ability of the computer to interpolate and extrapolate the scattered data. Surface pressure measurements were conducted for a pitch oscillation wing in a subsonic closed circuit wind tunnel. Experimental results have been used to train a multilayer perceptron network. This work indicates that neural networks can reliably predict aerodynamic coefficients and forecast the effects of reduced frequencies on the wind turbine blade performance. ©2007 IEEE
  6. Keywords:
  7. Aerodynamics ; Air ; Computer networks ; Electric generators ; Electric load forecasting ; Forecasting ; Gas dynamics ; Pressure measurement ; Process design ; Process engineering ; Three dimensional ; Turbines ; Wind effects ; Wind power ; Joint conference ; Neural networks
  8. Source: 2007 International Joint Conference on Neural Networks, IJCNN 2007, Orlando, FL, 12 August 2007 through 17 August 2007 ; 2007 , Pages 1995-1999 ; 10987576 (ISSN) ; 142441380X (ISBN); 9781424413805 (ISBN)
  9. URL: https://ieeexplore.ieee.org/document/4371264