Identification of Computational-Fluid-Dynamics Based Unsteady Aerodynamic Models for Aeroelastic Analysis

Three approaches for reduced-order modeling of computational-fluid-dynamics-(CFD) based unsteady aerodynamics, employing system-identification methods, are presented, and used for generation of three models: A frequency-domain model, a time-domain autoregressive-moving-average model, and a discrete-time state-space model. All models are identified based on the same identification data, which consists of the time histories of the generalized aerodynamic forces developed in response to filtered white-Gaussian-noise modal excitation, computed in a CFD analysis. The models are used for rapid flutter analysis via traditional frequency-domain methods, linear stability analysis, and time simulation. The method is applied for flutter analysis of the AGARD 445.6 wing. The filtered white-Gaussian-noise input is found to be applicable within the framework of CFD, yielding informative identification data sets. The identification process is simple, and the resulting reduced-order models closely reproduce the CFD system response to various excitations. Reduced-order model-based flutter analysis is rapid and yields accurate results compared with wind-tunnel test, CFD, and linear aerodynamics results.