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.
Store Separation Trajectory Clusters from Machine Learning
Journal of Aircraft ; 59 , 1 ; 117-125
2022-01-01
Conference paper , Article (Journal)
Electronic Resource
English
Store separation trajectory analysis
AIAA | 1980
|Trajectory Optimization for Guided Store Separation
British Library Conference Proceedings | 2012
|Trajectory Optimization for Guided Store Separation
AIAA | 2012
|FLIP 4 Store-Separation Trajectory Simulation Code
AIAA | 2009
|FLIP 4 Store-Separation Trajectory Simulation Code
British Library Conference Proceedings | 2009
|