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Record W2077879914 · doi:10.1115/pvp2003-1837

Stress Intensity Correction Factors for Radial-Longitudinal Surface Cracks in Thick-Walled Cylinders

2003· article· en· W2077879914 on OpenAlexaff
A. Kiciak, D. J. Burns, G. Glinka

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicFatigue and fracture mechanics
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsStress intensity factorSurface (topology)Enhanced Data Rates for GSM EvolutionCylinderMaterials scienceStress (linguistics)Quadratic equationWeight functionStructural engineeringGeometryMathematicsMathematical analysisComposite materialFracture mechanicsEngineering

Abstract

fetched live from OpenAlex

Section XI and Appendix D of Section VIII, Div. 3 of the ASME Code include an influence coefficients method for calculating Mode I crack tip stress intensity factors, K1. When outlining the technical basis for this method, Cippola mentions that the fiee surface correction factors G0, G1, G2 and G3, that are tabulated for uniform, linear, quadratic and cubic stress variations, respectively, were derived using Shen and Glinka’s weight functions for surface cracks in plates. In the interim, the authors and colleagues have extended the validity range of their weight functions for cracks in plates and developed solutions for internal or external cracks in thin- or thick-walled vessels. This paper is a comprehensive comparison of correction factors, Gi, obtained from weight functions for an edge or surface semi-elliptic crack in a plate with those obtained for a radial-longitudinal edge or surface semi-elliptic crack in a cylinder. The differences, which in some cases are large, and the well know uncertainties when calculating K1 for the surface points of cracks are discussed.

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)0.001
metaresearch head score (Gemma)0.009
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.023
GPT teacher head0.232
Teacher spread0.209 · 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

Citations0
Published2003
Admission routes1
Has abstractyes

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