Advances in Parametric and Model-Form Uncertainty Quantification in Canonical Aeroelastic Systems

Abstract

Uncertainty quantification is going to play a crucial role in the aeroelastic design and optimization of aircraft. Stochastic aeroelastic models are currently being considered to account for manufacturing tolerance in material properties, variability in flight conditions or uncertainty in the aeroelastic model itself. In this paper, some challenging issues in the development of efficient and robust stochastic solvers are reported within the framework of canonical aeroelastic systems. First, independent or correlated parametric uncertainties are propagated to compute the probability density function of the critical flutter velocity or the limit cycle oscillations in the presence of discontinuous responses. Secondly, inverse stochastic aeroelastic problems are addressed, in which experimental data are used to calibrate several stochastic aerodynamic models within a Bayesian framework. Studied configurations concern linear and non-linear pitching and plunging airfoils, and the stochastic flutter of a cantilevered straight composite wing subject to ply angle and thickness uncertainties.