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Record W2766788281 · doi:10.1115/pvp2017-66092

Closed-Form Stress Intensity Factor Solutions for Deep Surface Cracks in Plates

2017· article· en· W2766788281 on OpenAlexaff
Kisaburo Azuma, Yinsheng Li, Kunio Hasegawa, Steven X. Xu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicFatigue and fracture mechanics
Canadian institutionsKinectrics (Canada)
Fundersnot available
KeywordsStress intensity factorMaterials scienceCrackingCorrosionStress (linguistics)Stress corrosion crackingPressure vesselFracture mechanicsStress concentrationStructural engineeringComposite materialEngineering

Abstract

fetched live from OpenAlex

Materials made of Alloy 82/182/600 used in light-water reactors are known to be susceptible to stress corrosion cracking. It is known that the depth a of some cracks due to primary water stress corrosion cracking is larger than the half-length c. The stress intensity factor solution for cracks plays an important role to predict crack propagation and failure. However, Section XI of the ASME Boiler and Pressure Vessel Code does not provide the solutions for cracks with large aspect ratios a/c. In this study, closed-form stress intensity factor influence coefficients for deep surface cracks in plates are discussed. The crack tip stress distribution is represented by a fourth degree polynomial equation. Influence coefficient tables obtained by using finite element analysis in previous studies are used for curve fitting. The closed-form solutions for the coefficient were developed at the surface points and the deepest points of the cracks with aspect ratio a/c ranged from 1.0 to 8.0. The solutions for the points where the stress intensity factor reaches maximum were also investigated.

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)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.033
GPT teacher head0.251
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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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
Published2017
Admission routes1
Has abstractyes

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