Probabilistic parameter estimation of a fluttering aeroelastic system in the transitional Reynolds number regime

We present a Bayesian parameter estimation results of a self-sustaining aeroelastic oscillator. The system consists of an elastically mounted rigid wing on a rig fixed in a wind tunnel. For certain flow conditions in the transitional Reynolds number regime, i.e. 10 , 000 < Re < 1 , 000 , 000 , the wing absorbs energy from the flow and settles in a stable limit cycle oscillation (LCO). The LCO originates from laminar boundary layer separation which leads to negative aerodynamic damping at small angles of attack. We propose an empirical model of the aeroelastic system in the form of a generalized Duffing–van der Pol oscillator. A statistical technique is developed to estimate the linear and nonlinear parameters of the aeroelastic oscillator. In addition, the model noise term, in part accounting for the amplitude modulation of the LCO, is also estimated. In particular, we apply a Bayesian inference technique for parameter estimation that involves a state estimation problem using the measurement data. We exploit the extended Kalman filter for the state estimation. To generate samples from the posterior joint probability density functions of the aeroelastic parameters, we use the Markov Chain Monte Carlo (MCMC) simulation. Finally, the confidence interval of the static aerodynamic moment coefficient is presented.