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Record W2745605711 · doi:10.1155/2017/7218482

Fragmentation of Millimeter-Size Hypervelocity Projectiles on Combined Mesh-Plate Bumpers

2017· article· en· W2745605711 on OpenAlexafffund
Aleksandr Cherniaev, Igor Telichev

Bibliographic record

VenueAdvances in Materials Science and Engineering · 2017
Typearticle
Languageen
FieldMaterials Science
TopicHigh-Velocity Impact and Material Behavior
Canadian institutionsUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Space Agency
KeywordsProjectileHypervelocityMaterials scienceMillimeterPolygon meshAluminiumSpacecraftShieldComposite materialElectromagnetic shieldingAerospace engineeringOpticsPhysicsMetallurgyComputer scienceGeology

Abstract

fetched live from OpenAlex

This numerical study evaluates the concept of a combined mesh-plate bumper as a shielding system protecting unmanned spacecraft from small (1 mm) orbital debris impacts. Two-component bumpers consisting of an external layer of woven mesh (aluminum or steel) directly applied to a surface of the aluminum plate are considered. Results of numerical modeling with a projectile velocity of 7 km/s indicate that, in comparison to the steel mesh-combined bumper, the combination of aluminum mesh and aluminum plate provides better fragmentation of small hypervelocity projectiles. At the same time, none of the combined mesh/plate bumpers provide a significant increase of ballistic properties as compared to an aluminum plate bumper. This indicates that the positive results reported in the literature for bumpers with metallic meshes and large projectiles are not scalable down to millimeter-sized particles. Based on this investigation’s results, a possible modification of the combined mesh/plate bumper is proposed for the future study.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.041
Threshold uncertainty score0.654

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.015
GPT teacher head0.286
Teacher spread0.271 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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

Citations4
Published2017
Admission routes2
Has abstractyes

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