2022 article
Store Separation Trajectory Clusters from Machine Learning
JOURNAL OF AIRCRAFT, Vol. 59, pp. 117β125.
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 -means clustering, followed by a Gaussian mixture model clustering approach. The -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.