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Record W3131160457 · doi:10.1177/1099636221993876

Effect of extreme cold temperatures on quasi-static indentation and impact behavior of woven carbon fiber epoxy composite sandwich panels with nomex honeycomb core

2021· article· en· W3131160457 on OpenAlexaff
Mathilde Jean-St-Laurent, Marie‐Laure Dano, Marie-Josée Potvin

Bibliographic record

VenueJournal of Sandwich Structures & Materials · 2021
Typearticle
Languageen
FieldEngineering
TopicMechanical Behavior of Composites
Canadian institutionsCanadian Space AgencyUniversité Laval
Fundersnot available
KeywordsComposite materialMaterials scienceEpoxyIndentationComposite numberCore (optical fiber)Impact energyHoneycombHoneycomb structureFiberComposite laminates

Abstract

fetched live from OpenAlex

The effect of extreme cold temperatures on the quasi-static indentation and the low velocity impact behavior of woven carbon/epoxy composite sandwich panels with Nomex honeycomb core was investigated. Impact tests were performed at room temperature, –70°C, and –150°C. Two sizes of hemispherical impactor were used combined to three different impactor masses. All the impact tests were performed at the same initial impact velocity. The effect of temperature on the impact behavior is investigated by studying the load history, load-displacement curves and transmitted energy as a function of time curves. Impact damage induced at various temperatures was studied using different non-destructive and destructive techniques. Globally, more damages are induced with impact temperature decreasing. The results also show that the effect of temperature on the impact behavior is function of the impactor size.

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)0.000
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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0020.000

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 head0.013
GPT teacher head0.270
Teacher spread0.257 · 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

Citations9
Published2021
Admission routes1
Has abstractyes

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