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Full-Text Articles in Astrodynamics
Using Machine Learning To Predict Hypervelocity Fragment Propagation Of Space Debris Collisions, Katharine Larsen, Riccardo Bevilacqua
Using Machine Learning To Predict Hypervelocity Fragment Propagation Of Space Debris Collisions, Katharine Larsen, Riccardo Bevilacqua
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The future of spaceflight is threatened by the increasing amount of space debris, especially in the near-Earth environment. To continue operations, accurate characterization of hypervelocity fragment propagation following collisions and explosions is imperative. While large debris particles can be tracked by current methods, small particles are often missed. This paper presents a method to estimate fragment fly-out properties, such as fragment, velocity, and mass distributions, using machine learning. Previous work was performed on terrestrial data and associated simulations representing space debris collisions. The fragmentation of high-velocity fragmentation can be modeled by terrestrial fragmentation tests, such as static detonations. Recently, stereoscopic …
Predicting Dynamic Fragmentation Characteristics From High-Impact Energy Events Utilizing Terrestrial Static Arena Test Data And Machine Learning, Katharine Larsen, Riccardo Bevilacqua, Omkar S. Mulekar, Elisabetta L. Jerome, Thomas J. Hatch-Aguilar
Predicting Dynamic Fragmentation Characteristics From High-Impact Energy Events Utilizing Terrestrial Static Arena Test Data And Machine Learning, Katharine Larsen, Riccardo Bevilacqua, Omkar S. Mulekar, Elisabetta L. Jerome, Thomas J. Hatch-Aguilar
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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, …
Experimental Validation Of Inertia Parameters And Attitude Estimation Of Uncooperative Space Targets Using Solid State Lidar, Alessia Nocerino, Roberto Opromolla, Giancarmine Fasano, Michele Grassi, Spencer John, Hancheol Cho, Riccardo Bevilacqua
Experimental Validation Of Inertia Parameters And Attitude Estimation Of Uncooperative Space Targets Using Solid State Lidar, Alessia Nocerino, Roberto Opromolla, Giancarmine Fasano, Michele Grassi, Spencer John, Hancheol Cho, Riccardo Bevilacqua
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This paper presents an experimental activity aimed at assessing performance of techniques for inertia and attitude parameters estimation of an uncooperative but known space target. The adopted experimental set-up includes a scaled-down 3D printed satellite mock-up, a spherical air bearing and a low-cost solid-state LIDAR. The experimental facility also comprises a motion capture system to obtain a benchmark of the pose (position and attitude) parameters and an ad-hoc designed passive balancing system to keep the centre of mass as close as possible to the centre of rotation. The LIDAR-based 3D point clouds, collected while the target rotates on the spherical …