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Record W2178861178 · doi:10.1115/gt2008-51527

A Model of Nonlinear Fatigue-Creep (Dwell) Interactions

2008· article· en· W2178861178 on OpenAlexaff
Xijia Wu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHigh Temperature Alloys and Creep
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsCreepMaterials scienceCavitationCoalescence (physics)SuperalloyNucleationCrack closureDwell timeWedge (geometry)Fracture mechanicsNonlinear systemStructural engineeringComposite materialMechanicsThermodynamicsPhysicsEngineeringGeometryMathematics

Abstract

fetched live from OpenAlex

A nonlinear creep/dwell-interaction model is derived based on nucleation and propagation of a surface fatigue crack and its coalescence with creep/dwell damages (cavities or wedge cracks) along its path inside the material, which results in the total damage accumulation rate as given by dadN=1+lc+lzλdadNf+dadNenv where (da/dN)f is the pure fatigue crack growth rate, (da/dN)env is the environment-assisted crack growth rate, lc/lz is the cavity/wedge crack size, and λ is the average spacing between the internal cavities or cracks. Since wedge cracks are usually present in the form of dislocation pile-ups at low temperatures and cavitation usually occurs at high temperatures, the model attempts to reconcile the creep/dwell-fatigue phenomena over a broad temperature range of engineering concern. In particular, the model has been used to explain dwell fatigue of titanium alloys and high temperature creep-fatigue interactions in Ni-base superalloys under TCCR, CCCR, THCR and CHCR test conditions (see Annex A for definition of the terminology).

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0030.001
Research integrity0.0030.001
Insufficient payload (model declined to judge)0.0070.002

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 head0.033
GPT teacher head0.234
Teacher spread0.202 · 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 designSimulation or modeling
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

Citations1
Published2008
Admission routes1
Has abstractyes

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