Identification of flow field regions by Machine Learning

View Video Presentation: https://doi.org/10.2514/6.2022-0457.vid A machine learning algorithm is here proposed with the objective to identify homogeneous flow regions in CFD solutions. Given a numerical compressible viscous steady solution around a body at high Reynolds numbers, the task is to select the grid cells belonging to the boundary layer, shock waves and external inviscid flow. This selection is necessary to perform an accurate breakdown of the aerodynamic drag in viscous and wave contributions by a classical far field method. The machine learning algorithm overcomes some of the limitations and drawback of currently adopted deterministic zone selection algorithms which require the adoption of case dependent cut-off input values, topological information of the flow domain and subsequent visual check of the proper selection.