  In this work we present an advanced computational pipeline for the
approximation and prediction of the lift coefficient of a parametrized airfoil
profile. The non-intrusive reduced order method is based on dynamic mode
decomposition (DMD) and it is coupled with dynamic active subspaces (DyAS) to
enhance the future state prediction of the target function and reduce the
parameter space dimensionality. The pipeline is based on high-fidelity
simulations carried out by the application of finite volume method for
turbulent flows, and automatic mesh morphing through radial basis functions
interpolation technique. The proposed pipeline is able to save 1/3 of the
overall computational resources thanks to the application of DMD. Moreover
exploiting DyAS and performing the regression on a lower dimensional space
results in the reduction of the relative error in the approximation of the
time-varying lift coefficient by a factor 2 with respect to using only the DMD.
