Comparative numerical modelling of the behaviour of CFRP composites under hypervelocity impact
Bibliographic record
Abstract
Micro-meteoroids and orbital debris (MMOD) travelling at orbital speeds (>7 km/s) at low Earth orbit pose a prevalent threat to the operational integrity of satellites through hypervelocity impacts (HVIs). Damage to these structures can be quantified through the economic expenses related to loss of operation and replacement. Facesheets, usually fabricated from metals like aluminum, and, more recently, advanced composite materials such as carbon fiber-reinforced plastics (CFRPs), are generally the first line of defense against incoming debris. However, the deformation and damage that these materials undergo, especially CFRPs, in these situations needs to be better understood to improve design capabilities. Physical testing provides the best avenue for exploring material behaviour, however it is often associated with high economic and time expenses. A commonly accepted alternative relies on using numerical modelling of HVI shielding structures. Numerical studies regarding these impacts generally rely on either the traditional implementation of the finite element method (FEM, which is well-suited to modelling delamination between ply layers) or the smoothed particles hydrodynamics method (SPH, well-suited for modelling fragmentation and extreme deformations), neither of which fully captures the complete scope of failure. An adaptive FEM-SPH method is considered within this study to more comprehensively model the simultaneously occurring damage mechanisms. 16-ply composites under HVI by three different projectile materials, namely steel (high-density), aluminum (medium-density), and nylon (low-density), are modelled within this study. Crater size and delaminated area reported the numerical simulations were compared with results observed in experimental studies and were used as metrics to determine the accuracy of the numerical models.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".