Model-form and predictive uncertainty quantification in linear aeroelasticity

In this work, Bayesian techniques are employed to quantify model-form and predictive uncertainty in the linear behavior of an elastically mounted airfoil undergoing pitching and plunging motions. The Bayesian model averaging approach is used to construct an adjusted stochastic model from different model classes for time-harmonic incompressible flows. From a set of deterministic function approximations, we construct different stochastic models, whose uncertain coefficients are calibrated using Bayesian inference with regard to the critical flutter velocity. Results show substantial reductions in the predictive uncertainties of the critical flutter speed compared to non-calibrated stochastic simulations. In particular, it is shown that an efficient adjusted model can be derived by considering a possible bias in the random error term on the posterior predictive distributions of the flutter index.