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Neural network: A new prediction tool for estimating the aerodynamic behaivior of a pitching delta wing

Soltani, M. R ; Sharif University of Technology | 2003

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  1. Type of Document: Article
  2. DOI: 10.2514/6.2003-3793
  3. Publisher: American Institute of Aeronautics and Astronautics Inc , 2003
  4. Abstract:
  5. In this paper, a new approach based on a Generalized Regression Neural Network (GRNN) has been proposed to predict the unsteady forces and moments on a 70° swept wing undergoing sinusoidal pitching motion. Extensive wind tunnel results were being used for training the network and for verification of the values predicted by this approach. The Generalized Regression Neural Network (GRNN) has been trained by the aforementioned experimental data and subsequently was used as a prediction tool to determine the unsteady longitudinal forces and moments of the pitching delta wing for various reduced frequencies. The results are in a good agreement with those determined by the previous experimental findings. © 2003 by M.R. Soltani and Ali R. Davari
  6. Keywords:
  7. Aerodynamics ; Wind tunnels ; Swept wings ; Neural networks ; Forecasting
  8. Source: 21st AIAA Applied Aerodynamics Conference 2003, Orlando, FL, 23 June 2003 through 26 June 2003 ; 2003 ; 9781624100925 (ISBN)
  9. URL: https://arc.aiaa.org/doi/10.2514/6.2003-3793