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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 …
Flexible Mold For Microstructures Replication, Zefu Ren, Dae Won Kim
Flexible Mold For Microstructures Replication, Zefu Ren, Dae Won Kim
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Space debris has been a growing concern in space exploration sector. To combat this issue, biomimicry is utilized to create a gecko’s feet microstructure that will be attached to a gripper or robotic arm. This will enable capture of debris through the use of dry adhesive microstructure. However, the production of such microstructures is expensive which hinders their implementation. The objective of this research is to develop an advanced fabrication process to mass produce gecko’s feet microstructure with soft polymer mold. The possibility of using different coating methods with coating materials will be justified. The process of fabricating mold and …