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Record W3132623936 · doi:10.3390/applmech2010003

Hypervelocity Impacts on Satellite Sandwich Structures—A Review of Experimental Findings and Predictive Models

2021· article· en· W3132623936 on OpenAlexafffund
Riley Carriere, Aleksandr Cherniaev

Bibliographic record

VenueApplied Mechanics · 2021
Typearticle
Languageen
FieldMaterials Science
TopicHigh-Velocity Impact and Material Behavior
Canadian institutionsUniversity of Windsor
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsHypervelocityAerospace engineeringLow earth orbitElectromagnetic shieldingSpace debrisSandwich-structured compositeSatelliteComputer scienceMaterials scienceEngineeringSpacecraftPhysicsCore (optical fiber)AstronomyComposite material

Abstract

fetched live from OpenAlex

Sandwich panels are widely used in the design of unmanned satellites and, in addition to having a structural function, can often serve as shielding, protecting the satellites’ equipment from hypervelocity impacts (HVI) of orbital debris and micrometeoroids. This paper provides a comprehensive review of experimental studies in the field of HVI on sandwich panels with honeycomb- and open-cell foam cores, as well as an examination of available predictive models for the assessment of the panels’ ballistic limits. The emphasis of the review is placed on: (i) identifying gaps in the existing experimental database and the appropriate directions for its further expansion; and (ii) understanding the limitations of the available predictive models and the potential for their improvement.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0040.004
Science and technology studies0.0000.001
Scholarly communication0.0020.003
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.025
GPT teacher head0.276
Teacher spread0.251 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations20
Published2021
Admission routes2
Has abstractyes

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