  This article presents an original methodology for the prediction of steady
turbulent aerodynamic fields. Due to the important computational cost of
high-fidelity aerodynamic simulations, a surrogate model is employed to cope
with the significant variations of several inflow conditions. Specifically, the
Local Decomposition Method presented in this paper has been derived to capture
nonlinear behaviors resulting from the presence of continuous and discontinuous
signals. A combination of unsupervised and supervised learning algorithms is
coupled with a physical criterion. It decomposes automatically the input
parameter space, from a limited number of high-fidelity simulations, into
subspaces. These latter correspond to different flow regimes. A measure of
entropy identifies the subspace with the expected strongest non-linear behavior
allowing to perform an active resampling on this low-dimensional structure.
Local reduced-order models are built on each subspace using Proper Orthogonal
Decomposition coupled with a multivariate interpolation tool. The methodology
is assessed on the turbulent two-dimensional flow around the RAE2822 transonic
airfoil. It exhibits a significant improvement in term of prediction accuracy
for the Local Decomposition Method compared with the classical method of
surrogate modeling for cases with different flow regimes.
