Submitting Campus

Daytona Beach

Student Status

Graduate

Class

Graduate Student Works

Advisor Name

Dr. Riccardo Bevilacqua

Abstract/Description

To continue space operations with the increasing space debris, accurate characterization of fragment fly-out properties from hypervelocity impacts is essential. However, with limited realistic experimentation and the need for data, available static arena test data, collected utilizing a novel stereoscopic imaging technique, is the primary dataset for this paper. This research leverages machine learning methodologies to predict fragmentation characteristics using combined data from this imaging technique and simulations, produced considering dynamic impact conditions. Gaussian mixture models (GMMs), fit via expectation maximization (EM), are used to model fragment track intersections on a defined surface of intersection. After modeling the fragment distributions, k-nearest neighbor (K-NN) regressors are used to predict the desired characteristics. Using Monte Carlo simulations, the K-NN regression is shown to predict the distributions for both the total number of fragments intersecting a given surface, as well as the expected total fragment velocity and mass associated with that surface. This information can then be used to estimate the kinetic energy of the particle to classify the particle and avoid debris collisions.

Document Type

Article

Publication/Presentation Date

8-2023

Publication Title

Acta Astronautica

DOI

https://doi.org/10.1016/j.actaastro.2023.04.036

Publisher

Elsevier

Grant or Award Name

U.S. Air Force Office of Scientific Research (award number FA9550-20-1-0200)

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