Effect of ice geometry to airfoil performance using neural networks prediction
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摘要: 积冰几何形状对翼型气动系数的影响是复杂的.采用BP(Back Propagation)神经网络的LM(Levenberg-Marguardt)学习算法,建立明冰的典型几何特性(冰角前缘半径、冰角高度和冰角位置)对翼型气动系数影响的神经网络,得到该3种几何参数对气动系数影响的规律;建立了典型冰形参数对最大升力系数影响的神经网络,该网络能很好的预测冰形参数对应的最大升力系数值;此外,建立了冰型位置对舵面铰链力矩系数影响的神经网络.仿真结果表明,BP神经网络仿真结果与实验值具有高度一致性,并能预测非实验值条件下的气动系数;翼型表面积冰位置变化对气动系数影响最大;铰链力矩系数在失速迎角达到之前就发生突变,可以更安全地用来预测失速的发生.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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Key words:
- ice accretion /
- aerodynamic performance /
- aerodynamic coefficient /
- neural network /
- stall
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