ABSTRACT
Introduction Fluid-structure interaction can result in dynamic instabilities like fl utter. Nonlinear parameters present in the system can stabilize the diverging growth of fl utter oscillations to a limit cycle oscillation (LCO). Sustained LCO can lead to fatigue failure of rotating structures such as wind turbine rotors. Hence, it is an important design concern in aeroelastic
analysis. Moreover, there is a growing interest in understanding how system uncertainties in structural and aerodynamic parameters and initial conditions affect the characteristics of such dynamical response. Uncertainty quantifi cation in a stochastic framework with stochastic inputs has traditionally been analyzed with Monte Carlo simulations (MCSs). To apply this procedure one should use the distribution of the input parameters to generate a large number of realizations of the response. Probability density function _PDF_ and other required statistics are then approximated from these realizations; however, it is computationally expensive, especially for large complex problems. Hence, there is a need to develop alternate approaches which are computationally cheaper than direct MCS procedure. Perturbation method is a fast tool for obtaining the response statistics in terms of its fi rst and second moments [1]. The statistical response is determined by expanding the stochastic parameters around their mean via a Taylor series [2]. The application of this method is, however, limited to small perturbations and does not readily provide information on high-order statistics [3,4].
