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Record W2013018380 · doi:10.1063/1.1354648

Thermal resistance of bridged cracks in fiber-reinforced ceramic composites

2001· article· en· W2013018380 on OpenAlexafffund
J.R. Dryden, Frank W. Zok

Bibliographic record

VenueJournal of Applied Physics · 2001
Typearticle
Languageen
FieldEngineering
TopicComposite Material Mechanics
Canadian institutionsWestern University
FundersOffice of Naval ResearchNatural Sciences and Engineering Research Council of CanadaSteven G. Cancer Foundation
KeywordsResistorComposite materialMaterials scienceCeramic matrix compositeCeramicFiber-reinforced compositeComposite numberThermal resistanceMatrix (chemical analysis)FiberBridging (networking)PerpendicularCreepThermalGeometryMathematicsThermodynamicsPhysicsComputer science

Abstract

fetched live from OpenAlex

The thermal resistance of a bridged matrix crack in a fiber-reinforced ceramic composite is analyzed. The problem is cast in terms of a unit cell comprising an infinitely long composite cylinder with a single matrix crack perpendicular to the fiber axis. At the outset, it is demonstrated that the thermal resistance of such a crack can be represented by a simple circuit consisting of two parallel resistors; one resistor represents the thermal resistance of the gas phase Rg within the matrix crack, and the other resistor represents the constriction resistance Rc of the bridging fiber. The main focus of the article is on determination of Rc and bounds on this resistance are obtained by the use of variational calculus. The analogy between problems involving steady-state heat flow and elasticity in multiphase materials is emphasized. The results for the constriction resistance are compared with the predictions of an approximate analytical model presented by [T. J. Lu and J. W. Hutchinson, Philos. Trans. R. Soc. London, Ser. A 351, 595 (1995)]. In their model the radial temperature variation within the matrix is neglected. The domain in which such variations can be justifiably neglected is found.

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 score0.334
Threshold uncertainty score0.549

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 head0.198
Teacher spread0.192 · 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

Citations5
Published2001
Admission routes2
Has abstractyes

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