Store separation of a generic, thin-finned, missile through a continuously oscillating shear layer into subsonic flow was conducted experimentally through 100 drop tests to identify potential groups of trajectories and statistical phenomena. Change in store pitch was observed using a high-speed camera. Trajectories were grouped using machine learning with a k -means clustering, followed by a Gaussian mixture model clustering approach. The k -means clustering revealed two primary groups and one outlier group. The statistical strength of the primary groups was confirmed with the Gaussian mixture model, which places 89% of trajectories into one of two groups. The existence of two primary groups is strong evidence of a bifurcation.


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    Title :

    Store Separation Trajectory Clusters from Machine Learning


    Contributors:

    Published in:

    Journal of Aircraft ; 59 , 1 ; 117-125


    Publication date :

    2022-01-01




    Type of media :

    Conference paper , Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




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