Multivariate orthogonal function modeling was applied to wind tunnel databases for eight different aircraft to identify a generic global aerodynamic model structure that could be used for any of the aircraft. For each aircraft database and each coefficient, global models were identified from multivariate polynomials in the nondimensional states and controls, using an orthogonalization procedure. A predicted squared-error criterion was used to automatically select the model terms. Modeling terms selected in at least half of the analyses, which totaled 45 terms, were retained to form the generic nonlinear aerodynamic (GNA) model structure. Least squares was used to estimate the model parameters and associated uncertainty that best fit the GNA model structure to each database. The result was a single generic aerodynamic model structure that could be used to accurately characterize the global aerodynamics for any of the eight aircraft, simply by changing the values of the model parameters. Nonlinear flight simulations were used to demonstrate that the GNA model produces accurate trim solutions, local dynamic behavior (modal frequencies and damping ratios), and global dynamic behavior under large-amplitude excitation. This compact global aerodynamic model can decrease flight computer memory requirements for implementing onboard fault detection or flight control systems, enable quick changes for conceptual aircraft models, and provide smooth analytical functions for control and optimization applications. All information required to construct nonlinear simulations of these eight aircraft are contained within this paper.
NomenclatureRoman a orthogonal model parameters a x , a y , a z body-axis accelerations [g] b wing span [ft] C D , C Y , C L aerodynamic force coefficients C l , C m , C n aerodynamic moment coefficients cov(.) covariancē c mean aerodynamic chord [ft] J(θ) least-squares cost function L, M , N aerodynamic moments [ft·lbf] m mass [slug] N number of observations n number of parameters P orthogonal regressors PSE predicted squared error p, q, r roll, pitch, and yaw rates [rad/s] q dynamic pressure [lbf/ft 2 ] R 2 coefficient of determination S wing reference area [ft 2 ] T thrust [lbf] t time [s] V airspeed [ft/s] X, Y , Z aerodynamic forces [lbf] Greek α angle of attack [rad] β sideslip angle [rad] δ a , δ e , δ r aileron, elevator, rudder deflection [rad] θ model parameters ν measurement noise σ 2 covariance υ residual Superscriptṡ time derivative T matrix transposẽ normalized rates [rad] estimate * Research Engineer, Dynamic Systems and Control Branch, MS 308, AIAA Member † Research Engineer, Dynamic Systems and Control Branch, MS 308, AIAA Associate Fellow 1 of 16 American Institute of Aeronautics and Astronautics Downloaded by CORNELL UNIVERSITY on July 31, 2015 | http://arc.aiaa.org |
