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Record W4254290855 · doi:10.1115/1.1689358

Stress Intensity Factors for a Curved-Front Internal Crack in an Autofrettaged Tube With Bauschinger Effect

2004· article· en· W4254290855 on OpenAlexaff
Choon-Lai Tan, Anthony P. Parker, Chantz W. V. Cassell

Bibliographic record

VenueJournal of Pressure Vessel Technology · 2004
Typearticle
Languageen
FieldEngineering
TopicFatigue and fracture mechanics
Canadian institutionsCarleton University
Fundersnot available
KeywordsAutofrettageBauschinger effectMaterials scienceStress intensity factorResidual stressEccentricity (behavior)Structural engineeringvon Mises yield criterionMechanicsCrack closurePressure vesselStress concentrationStress (linguistics)Composite materialFinite element methodFracture mechanicsPlasticityEngineeringPhysics

Abstract

fetched live from OpenAlex

Pressure vessel steels exhibit the Bauschinger effect that significantly reduces post-autofrettage residual compressive hoop stresses in the near-bore region in comparison with ‘ideal’ (elastic-perfectly plastic) behavior. These reduced hoop stress profiles were calculated using Von Mises’ criterion via a nonlinear analysis for the case of open-end (engineering plane strain) autofrettage. These profiles were then used to obtain stress intensity factor solutions via the Boundary Integral Equation (BIE) method, commonly known as the Boundary Element Method (BEM). Results are presented for tubes of diameter ratio 2 and 2.5 with an internal semi-elliptical surface crack having a maximum depth/surface length ratio of 0.4 (i.e., an eccentricity of 0.8). Crack depths range from 20% to 80% of wall thickness and results are presented for seven locations on the crack front from maximum depth to free surface. For crack depths up to 20% of wall thickness there is a significant reduction in magnitude of autofrettage stress intensity factor due to Bauschinger effect. For typical overstrain levels this reduction is approximately 30% of “ideal” values. Such a reduction may, in turn, cause an order of magnitude reduction in the fatigue lifetime of the vessel.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.703
Threshold uncertainty score0.752

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.227
Teacher spread0.219 · 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

Citations2
Published2004
Admission routes1
Has abstractyes

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