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Aeroelastic problems involve coupling of fluid and structural solvers to obtain the response of the system under a set of operating conditions. Unsteady aeroelastic phenomena such as flutter require the solution of these coupled equations for the entire flight regime and operating conditions. These coupled problems generally tend to be computationally intensive, primarily due to the fluid component. Uncertainties in structural parameters (due to e.g. manufacturing variability and fatigue) are important for determining the stability of the resulting system. The corresponding uncertain analysis requires many code runs, and can be computationally intractable. In this paper, we apply system identification to obtain a reduced-order model for the aerodynamic solver. This model is then coupled with the structural solver for obtaining the full aeroelastic solution. System identification is a data driven modeling approach for approximating a system based on input and observed outputs of the system. The resulting approximation is extremely cheap. We employ this to model the aerodynamic forces, given structural displacements, and these forces are exchanged with the structural solver to update the aeroelastic solution. Two ROMs are considered, AutoRegressive with eXogenous inputs (ARX), and a Linear Parameter Varying (LPV) model. The latter is expected to capture dynamics of the underlying system at multiple operating points. A model is built and applied to 2-DoF airfoils. The obtained response for the 2-dimensional system is very much in agreement with the high-fidelity CFD solver. Results show that this data-driven approach can accurately predict the aerodynamic forces. With this technique, uncertainty in stability characteristics of the system can be rapidly estimated. An uncertainty quantification is performed by Monte-Carlo on the reduced order model.

SYSTEM IDENTIFICATION FOR UNCERTAINTY QUANTIFICATION IN AEROELASTIC PROBLEMS