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Volume 34 Issue 8
Aug.  2008
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Journal of Beijing University of Aeronautics and Astronautics  > 2008  >  34(8): 900-903.
Yuan Kungang, Cao Yihua. Effect of ice geometry to airfoil performance using neural networks prediction[J]. Journal of Beijing University of Aeronautics and Astronautics, 2008, 34(8): 900-903. (in Chinese)
Citation: Yuan Kungang, Cao Yihua. Effect of ice geometry to airfoil performance using neural networks prediction[J]. Journal of Beijing University of Aeronautics and Astronautics, 2008, 34(8): 900-903. (in Chinese)
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Effect of ice geometry to airfoil performance using neural networks prediction

  • School of Aeronautic Science and Engineering, Beijing University of Aeronautics and Astronautics, Beijing 100191, China

  • Received Date: 13 Jul 2007
  • Publish Date: 31 Aug 2008
    Abstract
  • The ice accretion geometric characteristics have complex effects on airfoil aerodynamic coefficients. By using levenberg-marguardt(LM) learn algorithm of back-propagation (BP) network, several neural networks were established to get correlations between typical glaze ice geometry (ice horn leading-edge radius, ice height and ice horn position on airfoil surface) and airfoil aerodynamic coefficients. The neural network was also generated for predicting the maximum lift coefficient in typical ice accretion geometric parameters. In addition, the neural network that used to get the relationship of airfoil hinge moment coefficient and ice geometric was obtained to predict the angle of attack stall. The simulation results of neural networks have high coherence with experimental data and can predict coefficients at non-experimental conditions. The horn location on airfoil surface has the most severe effects on airfoil coefficients. The hinge moment coefficient breaks before the stall of angle of attack and can be used for predicting stall more safely.

     

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