  Aeroelasticity in the transonic regime is challenging because of the strongly
nonlinear phenomena involved in the formation of shock waves and flow
separation. In this work, we introduce a computationally efficient framework
for accurate transonic aeroelastic analysis. We use dynamic mode decomposition
with control (DMDc) to extract surrogate models from high-fidelity
computational fluid dynamics (CFD) simulations. Instead of identifying models
of the full flow field or focusing on global performance indices, we directly
predict the pressure distribution on the body surface. The learned surrogate
models provide information about the system stability and can be used for
control synthesis and response studies. Specific techniques are introduced to
avoid spurious instabilities of the aerodynamic model. We use the high-fidelity
CFD code SU2 to generate data and test our method on the benchmark super
critical wing (BSCW). Our python-based software is fully open-source and will
be included in the SU2 package to streamline the workflow from defining the
high-fidelity aerodynamic model to creating a surrogate model for flutter
analysis.
