Parametric Aeroelastic Reduced-Order Model with State-Consistence Enforcement

State-space reduced-order models (ROMs) constructed by traditional system identification methods suffer from the state-inconsistence issue and poor ROM interpolatability for varying flight conditions. This paper presents a novel system identification method and a state-consistence enforcement (SCE) algorithm to generate a state-space aeroelastic (AE)-ROM within a broad flight parameter space. A new regularization term is proposed to modify the traditional autoregressive exogenous formulation and specifically penalize state inconsistence between ROMs at varying flight conditions. The new formulation cast in the form of the generalized Tikhonov regularization problem can be computed very efficiently and produce fundamentally state-consistent ROMs. Rigorous numerical analysis that compares the state-consistent AE-ROM with the full-order model and the ROM without SCE is also conducted. The results convincingly prove that the proposed approach significantly improves the state consistence of ROMs at varying flight conditions. To the best of our knowledge, this research represents the first effort of developing a state-consistent data-driven AE-ROM and enabling aeroelasticity analysis within a broad flight parameter space.

Automated summary

This paper introduces a novel system identification method and a state-consistence enforcement algorithm to construct a state-space aeroelastic reduced-order model (AE-ROM) with consistency across varying flight conditions. A new regularization term is proposed to penalize state inconsistence between ROMs, resulting in fundamentally state-consistent ROMs. Numerical analysis confirms the significant improvement in state consistence using the proposed method.