High Angle of Attack Parameter Estimation of Cascaded Fins Using Neural Network

Abstract

Parameter estimation of cascaded fins at high angle of attack is carried out using a new neural-network approach. Two new lift models are proposed to overcome the problems faced by high angle-of-attack lift models based on Kirchhoff’s theory of flow separation. Lift modeling is carried out using a neural network for single and cascaded planar fins of airfoil and rectangular cross sections. Neural partial differentiation is applied to find the lift curve slope at zero angle of attack, which is more consistent compared to that based on average slope obtained using output-error method. Neural approach is applied to model the steady flow separation point on the fin. Separation and stall characteristic parameters are estimated using a new neural formulation. It is shown that the values of these parameters, obtained using the proposed neural method, better represent the observed lift data compared to those from the output-error method. Moreover, a modified equation is suggested to better model the steady separation point for the cascaded fins. It is shown that the new equation for the separation point, when used in conjunction with the new lift coefficient equation, produces lift coefficient values which better match the measured ones. It is also shown that for an optimum gap-to-chord ratio the separation parameter will be maximum implying a delayed stall. Further, it is shown that an increase in the number of fins in the cascade has the effect of increasing the stall angle in addition to the increase in lift curve slope.