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Record W2898593453 · doi:10.1115/pvp2018-85093

Investigation of Calculating Stress Intensity Factor at Surface Point and its Inclusion in Engineering Standards

2018· article· en· W2898593453 on OpenAlexaff
Steven X. Xu, Greg Thorwald, Patrick Le Delliou, Russell C. Cipolla

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicFatigue and fracture mechanics
Canadian institutionsKinectrics (Canada)
Fundersnot available
KeywordsStress intensity factorSection (typography)Closure (psychology)InterimPoint (geometry)CylinderSurface (topology)Stress (linguistics)Structural engineeringAction (physics)EngineeringProcess (computing)Mechanical engineeringMathematicsComputer scienceGeometryFracture mechanicsLawPhysics

Abstract

fetched live from OpenAlex

Article A-3000 of Appendix A in ASME Section XI provides methods to calculate stress intensity factors that are used in Section XI linear elastic fracture mechanics based flaw evaluation procedures. The ASME Section XI Working Group on Flaw Evaluation has been in the process of rewriting Article A-3000 of Appendix A. The rewrite of Article A-3000 includes implementation of closed-form equations for stress intensity factor influence coefficients for cylinder geometries. Closed-form relations for stress influence coefficients G0 and G1 for axial flaws on the outside surface in cylinders were recently developed and implemented into the 2017 Edition of ASME Section XI Appendix A. The closed-form equations were implemented with one restriction on the application related to very long flaws. This restriction was taken as an interim approach to addressing a technical concern from the US NRC staff. NRC staff had technical concern on the large percentage fitting errors for the G1 influence coefficients at surface point for some very long flaws. An action was assigned within the ASME Section XI Working Group on Flaw Evaluation to investigate the accuracy of surface point G values for very long flaws. The intent of the investigation is to provide technical justification for using the closed-form equations with no restriction and to identify any issues in the source data or during the fitting process. This paper describes current results from this ongoing investigation.

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)0.005
metaresearch head score (Gemma)0.023
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 score0.011
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.023
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.003
Science and technology studies0.0010.001
Scholarly communication0.0020.003
Open science0.0020.001
Research integrity0.0010.001
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 head0.013
GPT teacher head0.219
Teacher spread0.205 · 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
Published2018
Admission routes1
Has abstractyes

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