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Record W2792516270 · doi:10.1177/2515221117751154

Thermal conductivity of textile reinforcements for composites

2018· article· en· W2792516270 on OpenAlexaff
Yue El-Hage, S. Hind, F. Robitaille

Bibliographic record

VenueJournal of Textiles and Fibrous Materials · 2018
Typearticle
Languageen
FieldMaterials Science
TopicThermal properties of materials
Canadian institutionsNational Research Council CanadaUniversity of Ottawa
Fundersnot available
KeywordsComposite materialMaterials scienceThermal conductivityReinforcementCrimpVolume fractionTextileCarbon fibersThermalComposite number

Abstract

fetched live from OpenAlex

Thermal conductivity data for dry carbon fibre fabrics are required for modelling heat transfer during composites manufacturing processes; however, very few published data are available. This article reports in-plane and through-thickness thermal conductivities measured as a function of fibre volume fraction ( Vf) for non-crimp and twill carbon reinforcement fabrics, three-dimensional weaves and reinforcement stacks assembled with one-sided carbon stitch. Composites made from these reinforcements and glass fibre fabrics are also measured. Clear trends are observed and the effects of Vf, de-bulking and vacuum are quantified along with orthotropy ratios. Limited differences between the conductivity of dry glass and carbon fibre fabrics in the through-thickness direction are reported. An unexpected trend in the relationship between that quantity and Vf is explained summarily through simple simulations.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.021
GPT teacher head0.258
Teacher spread0.237 · 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 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

Citations26
Published2018
Admission routes1
Has abstractyes

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