Improved Boosting Model for Unsteady Nonlinear Aerodynamics based on Computational Intelligence

Abstract

The large-amplitude-oscillation experiment was carried out with two levels of freedom to provide data. Based on the wind tunnel data, polynomial regression, least-square support vector machines and radial basis function neural networks are studied and compared in this paper. An improved model was also developed in this work for unsteady nonlinear aerodynamics on the basis of standard boosting approach. The results on the wind tunnel data show that the predictions of the method are almost consistent with the actual data, thus demonstrating that these methods can model highly nonlinear aerodynamics. The results also indicate that improved boosting model has better accuracy than the other methods.

LPV Modeling and Identification of Unsteady Aerodynamics for Fast Maneuvering Aircrafts

“…coefficients at large angles of attack has attracted attentions from researchers because the aerodynamics with respect to the angle of attack is complicated, which is nonlinear and unsteady [8]- [10].…”