Enhancing CFD predictions in shape design problems by model and parameter space reduction
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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 and it is coupled with dynamic active subspaces 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 results in a decrease of the relative error in the approximation of the time-varying lift coefficient of more than 4% at regime.
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