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Record W1992810430 · doi:10.2514/6.2010-2823

Effects of Cold Temperature, Moisture and Freeze-Thaw Cycles on the Mechanical Properties of Unidirectional Glass Fiber-Epoxy Composites

2010· article· en· W1992810430 on OpenAlexafffund
Laurent Cormier, Simon Joncas

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMechanical Behavior of Composites
Canadian institutionsUniversité du Québec
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsComposite materialEpoxyMaterials scienceMoistureGlass fiberFiber

Abstract

fetched live from OpenAlex

´The durability of composite materials used for wind turbines blades exposed to a northern climate presents uncertainties as their behavior for very cold temperature applications is not completely understood. The goal of this project is to confront actual theories to experimental results for mechanical properties of unidirectional glass-epoxy composites exposed to moisture, cold temperature and freeze-thaw cycles. Tests were made at ambient temperature and -40℃ on four sample families. The families consisted of either dry or moisture saturated specimens, of which half of the families were further conditioned with 100 freeze-thaw cycles between -40℃ and 40℃. Tensile, compressive and short beam shear tests were conducted. Results showed the inadequacy of classical theories for predicting strength of the specimens exposed to low temperature and/or moisture. Contrary to the results presented in most of the literature, freeze-thaw cycles did not significantly change the strength or modulus of the test specimens. However, low temperature provided an important increase in strength while modulus was retained. Moisture had a stronger effect on properties than models would predict but there was no evidence of a synergistic effect between moisture and temperature. Comparison of the results with those presented in the literature shows that the behavior of composites exposed to low temperatures or freezethaw cycles is very sensitive to the nature of the constituents and the molding process used to produce the parts.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.035
Threshold uncertainty score0.471

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.007
GPT teacher head0.189
Teacher spread0.182 · 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

Citations20
Published2010
Admission routes2
Has abstractyes

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