Reduced-Order Dynamic Stall Modeling with Swept Flow Effects Using a Surrogate-Based Recurrence Framework

Abstract

The extension of the surrogate-based recurrence framework approach to account for time-varying swept flow effects under dynamic stall conditions is described. Using full-order solutions generated by the OVERFLOW computational fluid dynamics code, the surrogate-based recurrence framework reduced-order modeling approach is shown to effectively mimic full-order solutions of unsteady lift, moment, and drag under dynamic stall conditions while maintaining the computational efficiency associated with semiempirical dynamic stall models. This level of functionality represents a new capability for rotary-wing aeroelasticity applications. Furthermore, a generalized kriging formulation based on nonstationary Gaussian process modeling is implemented in a tractable manner by locally optimizing the high-dimensional likelihood function in the vicinity of the stationary solution. The resulting nonstationary covariance structures are shown to significantly improve the accuracy of the surrogate-based recurrence framework predicted moment stall characteristics compared to a stationary model. It is shown that the nonstationary surrogate-based recurrence framework approach is better able to adapt to abrupt changes in airload behavior caused by the underlying dynamic stall vortex dynamics. The results indicate that the surrogate-based recurrence framework approach based on nonstationary Gaussian process models is a promising alternative to widely used semiempirical rotorcraft dynamic stall models that cannot account for the effects of time-varying velocity components associated with forward flight.