State consistence of data-driven reduced order models for parametric aeroelastic analysis

Abstract This paper investigates the state consistence of parametric data-driven reduced order models (ROMs) in a state-space form obtained by various system identification methods, including autoregressive exogenous (ARX) and subspace (N4SID), for aeroelastic analysis varying flight conditions. The target envelop is first partitioned into discrete grid points, on each which an aerodynamic ROM constructed using to capture dependence generalized force displacement structural modes. High-fidelity modal perturbation simulations are used generate training verification data. Aerodynamic ROMs not point interpolating those at neighboring points. Through thorough model coefficients pole migration, it found that only ARX-based preserves consistence, hence, allowing direct interpolation matrices non-grid rapid database development entire parameter space. In contrast, N4SID-based destroys yields physically meaningless results when interpolated. origin difference caused both methods also discussed. interpolated ARX coupled with exhibit excellent agreement high fidelity full (mostly <5% relative error) salient computational efficiency.