Reduced order modelling for static and dynamic aeroelastic predictions with multidisciplinary approach

Abstract

We implement reduced order modelling techniques for aeroelastic predictions of the HIRENASD and S4T wings in order to represent CFD based high-fidelity solutions efficiently. Model reduction techniques such as non-intrusive Polynomial Chaos Expansion and Proper Orthogonal Decomposition are applied to both static and dynamic aeroelastic cases. The high-fidelity solutions are obtained by fluid structure interaction analysis using a 3D Euler unsteady aerodynamic solver and structural modal solution from a finite element solver. The model order reduction strategy is based on a multidisciplinary approach since both structural and aerodynamic input parameters are employed. The model order reduction is performed not only to represent the high-fidelity computational analyses when small variations of input parameters are considered but also to characterize the flutter responses of the S4T wing in a broad range of input values over the entire flight regime for Mach numbers between 0.60 and 1.20. The efficient aeroelastic analyses performed using the developed reduced order models agreed well with the high-fidelity computational analyses.