Weight-Efficiency of Conventional Shielding Systems in Protecting Unmanned Spacecraft from Orbital Debris
Bibliographic record
Abstract
Unmanned spacecraft typically require protection only from much smaller orbital debris as compared to manned missions. This paper presents quantification and comparison of the weight efficiency of conventional shielding concepts, which were originally developed for manned spacecraft, when designed to protect a robotic satellite against small-size (1 mm) orbital debris impacts. The shielding systems under comparison comprise two categories: “single-purpose orbital debris shields,” represented by the Whipple shield and the stuffed Whipple shield; and “multipurpose structural panels,” represented by honeycomb-core and foam-core sandwich panels. First-order estimates of the shields’ parameters are obtained using the well-known ballistic limit equations. These estimates are then used as starting points for further optimization of the shields conducted by means of hydrocode simulations. The simulations employ a combination of the ANSYS Autodyn finite element and smooth particle hydrodynamics solvers. The results obtained indicate that, in the single-purpose orbital debris shields category, the simpler Whipple shield concept provides better performance than the stuffed Whipple configuration; whereas for the multipurpose structural panels, the foam-core sandwich panel can have less than half the weight as compared to a honeycomb-core panel with a similar ballistic performance.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".