Dataset supporting the publication "Low-dimensional Models for Aerofoil Icing"

This dataset contains supplementary material in support of the journal article: D Massegur, D Clifford, A Da Ronch, R Lombardi, M Panzeri (2022) "Low–dimensional Models for Aerofoil Icing", American Institute of Aeronautics and Astronautics, https://doi.org/10.2514/6.2022-3696 The data includes results from an adaptive sampling strategy to identify the critical icing conditions across the icing envelope for continuous intermittent icing; a classical proper orthogonal decomposition; and more modern neural network architectures. The variety in simulated ice profiles, ranging from smooth to rough and irregular shapes, motivated the use of an unsupervised classification of the icing envelope. This allowed deploying the proper orthogonal decomposition locally within each cluster, improving sensibly the prediction accuracy over the global model. The data is presented in several zipped folders: ice_shapes_CFD.zip (.dat files) ice_shapes_ConvAE.zip (.dat files) ice_shapes_localPOD.zip (.dat files) ice_shapes_globalPOD.zip (.dat files)