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This paper presents a method of data-driven parametric Dynamic ModeDecomposition (p-DMD) to derive a linear parameter-varying reduced-order model(LPV-ROM) for the nonlinear aeroelasticity of highly flexible aircraft. Itdirectly uses the data snapshots obtained at varying flight conditions, andencodes the physical understanding of the nonlinear model's polynomialdependency on flight conditions to produce a polynomial-dependent LPV-ROM.Therefore, this method can handle not only the equilibrium flight conditionsbut also the cases of continuously-varying flight conditions. In the numericalstudies, a highly flexible cantilever wing and a slender vehicle built based onit are first studied with fixed angles of attack as the scheduling parameter.The comparisons between traditional linearization-based parametric modeling andthe data-driven p-DMD modeling are performed to verify the modeling accuracy.The results demonstrate that the current p-DMD modeling method can capture theaeroelastic and flight dynamic responses of highly flexible aircraft in bothtime and frequency domains. In addition, the proposed p-DMD method is appliedto the highly flexible aircraft in a perturbed longitudinal flight with varyingangles of attack as the scheduling parameter. The nonlinear aeroelastic andflight dynamic data are compared with the simulation results of the data-drivenp-DMD model. The comparison results demonstrate that it can accurately capturethe non-equilibrium (or transient) aeroelastic and flight dynamic behaviors ofsuch slender vehicles.

Data-Driven Reduced-Order Aeroelastic Modeling of Highly Flexible Aircraft by Parametric Dynamic Mode Decomposition