Development of a corrugated core sandwich panel with enhanced MMOD shielding capability
Bibliographic record
Abstract
The growing threat of orbital debris is a significant concern for active and future spacecraft. This study exploits the concept of redesigning the components of spacecraft, adding to them a new function of space debris protection. The sandwich panel is a traditional member of the satellite structure due to its high structural support capability. However, the honeycomb sandwich panel, the most commonly used sandwich structure, has demonstrated a very low efficiency for protection against space debris. Therefore, it’s necessary to examine other types of sandwich panels that can serve structural purposes and simultaneously provide adequate protection against space debris. This thesis thoroughly investigated the shielding performance of a Corrugated Core Sandwich Panel (CCSP) against hypervelocity impact as a potential alternative to conventional shield designs. To eliminate the current limitation of the experimental investigation, a robust numerical model was developed using a combination of Smoothed-Particle Hydrodynamics (SPH) technique and the Finite Element Method (FEM) in ANSYS/AUTODYN software. The developed numerical models were validated using experimental data. Parametric studies identified the areas on the CCSP that are susceptible to hypervelocity impact and its effect on the overall shielding performance of the panel. Several strategies to enhance the shielding performance of the CCSP have been proposed. Upgrading the corrugated core of the CCSP configuration leads to a 20.5% improvement. In addition, it was demonstrated that the CCSP performance could be further enhanced with ballistic inserts containing aramid or ceramic fabric layers. A triangular prism-shaped polymeric foam has been suggested for making the inserts, which are meant to fit the space within the corrugated plate core.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".