Stochastic Aeroelastic Modeling Using Bayesian Inference

This study focuses on stochastic modelling of 2D aeroelastic model to quantify the uncertainties in the parameters of aeroelastic models, specifically the plunging and torsional stiffness. To measure the frequency response, a simulation of an aeroelastic model in a wind tunnel is considered. The excitation is provided by Gust. The frequency-velocity curve is generated from the simulated dynamic aeroelastic response. This curve is then used to establish the likelihood function for Bayesian identification of the aeroelastic model. The Metropolis–Hastings algorithm in Markov-Chain Monte Carlo (MCMC) sampling is used to analyze the measurement data obtained from the frequency-velocity curves. Using Bayesian inference, the uncertainties in the aeroelastic model are modelled. Overall, this study provides insights into the identification of uncertainties in aeroelastic models using advanced statistical techniques.