Multifidelity Method for Locating Aeroelastic Flutter Boundaries

This paper introduces a multifidelity method that can produce accurate estimates of the flutter boundary at a reduced cost by combining information from low- and high-fidelity aeroelastic models. Estimating the flutter boundary in the presence of nonlinear aerodynamic phenomena is challenging because high-fidelity aeroelastic models are expensive to evaluate, and flutter analysis requires many model evaluations. On the other hand, relatively inexpensive approximate aeroelastic models (low-fidelity models) exist and are routinely applied to reduce the cost of estimating flutter, albeit with lower accuracy. The multifidelity method introduced here uses an active learning algorithm to leverage information from low-fidelity models. A multifidelity statistical surrogate is used to fit damping coefficient estimates computed with different aeroelastic models. This surrogate is used to estimate the uncertainty in the prediction of the flutter boundary, which drives the selection of new evaluations. The effectiveness of the multifidelity method is demonstrated by estimating the aeroelastic flutter boundary of a typical section model at a cost 85% lower when compared with the bisection method. Four aeroelastic models are considered in this example: three models (including the high-fidelity model) use a computational fluid dynamics solver based on the Euler equations, whereas one model uses a two-dimensional doublet-lattice method.