A machine learning algorithm is here proposed with the objective to identify homogeneous flow regions in computational fluid dynamics 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. The Gaussian mixture algorithm demonstrated to overcome some of the limitations and drawback of the currently adopted deterministic region selection methods, which require the adoption of case-dependent cutoff inputs, topological information, and final human check. This paper shows an example of application of this selection method performing an accurate breakdown of the aerodynamic drag in viscous and wave contributions by a classical far-field method. The new algorithm essentially leads to the same results of the reference method in terms of drag decomposition; slight differences could only be found in the shock-wave/boundary-layer interaction zone, where the drag breakdown is inherently ambiguous.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Identification of Flowfield Regions by Machine Learning


    Contributors:

    Published in:

    AIAA Journal ; 61 , 4 ; 1503-1518


    Publication date :

    2023-04-01




    Type of media :

    Conference paper , Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




    Flowfield Diagnostics

    S. Lederman / A. Celentano / J. Glaser | AIAA | 1979


    Plug Nozzle Flowfield Analysis

    T. Rommel / G. Hagemann / C.-A. Schley et al. | AIAA | 1997


    Plug Nozzle Flowfield Analysis

    Rommel, T. | Online Contents | 1997


    Supercritical airfoil flowfield measurements

    HURLEY, F. / SPAID, F. / ROOS, F. et al. | AIAA | 1975


    Simulating flowfield around helicopters

    Emerald Group Publishing | 2003