Sparse polynomial surrogates for non-intrusive, high-dimensional uncertainty quantification of aeroelastic computations

Abstract

This paper is concerned with aircraft aeroelastic interactions and the propagation of parametric uncertainties in numerical simulations using high-fidelity fluid flow solvers. More specifically, the influence of variable operational and structural parameters (random inputs) on the drag performance and deformation (outputs) of a flexible wing in transonic regime, is assessed. Because of the complexity of fluid flow solvers, non-intrusive uncertainty quantification techniques are favored. Polynomial surrogate models based on homogeneous chaos expansions in the random inputs are commonly considered in this respect. The polynomial expansion coefficients are constructed using either structured sampling sets of the input parameters, as Gauss quadrature nodes, or unstructured sampling sets, as in Monte-Carlo methods. In complex systems such as the advanced aeroelastic test case studied here, the output quantities of interest generally depend only weakly on the multiple

Flutter Characteristics of a Modified Z-Shaped Folding Wing Using a New Non-Intrusive Model

“…At present, non-intrusive methods are mostly used in the field of uncertainty quantification and propagation [19][20][21][22][23]. In recent years, some scholars have also begun to use non-invasive methods for research in the field of aeroelasticity [24,25].…”