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In this work we present an advanced computational pipeline for theapproximation and prediction of the lift coefficient of a parametrized airfoilprofile. The non-intrusive reduced order method is based on dynamic modedecomposition (DMD) and it is coupled with dynamic active subspaces (DyAS) toenhance the future state prediction of the target function and reduce theparameter space dimensionality. The pipeline is based on high-fidelitysimulations carried out by the application of finite volume method forturbulent flows, and automatic mesh morphing through radial basis functionsinterpolation technique. The proposed pipeline is able to save 1/3 of theoverall computational resources thanks to the application of DMD. Moreoverexploiting DyAS and performing the regression on a lower dimensional spaceresults in the reduction of the relative error in the approximation of thetime-varying lift coefficient by a factor 2 with respect to using only the DMD.

Enhancing CFD predictions in shape design problems by model and parameter space reduction