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Record W2763746113 · doi:10.3221/igf-esis.42.09

Fatigue crack propagation prediction of a pressure vessel mild steel based on a strain energy density model

2017· article· en· W2763746113 on OpenAlexaff
Peter Huffman, A.J.M. Ferreira, José A.F.O. Correia, Abílio M.P. De Jesus, Grzegorz Lesiuk, F. Berto, Alfonso Fernández‐Canteli, G. Glinka

Bibliographic record

VenueFrattura ed Integrità Strutturale · 2017
Typearticle
Languageen
FieldEngineering
TopicFatigue and fracture mechanics
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsFracture mechanicsMaterials scienceStrain energy density functionStructural engineeringParis' lawCrack closureStrain (injury)Strain energyPressure vesselStress (linguistics)Crack growth resistance curveStrain energy release rateMechanicsComposite materialFinite element methodEngineeringPhysics

Abstract

fetched live from OpenAlex

Fatigue crack growth (FCG) rates have traditionally been formulated from fracture mechanics, whereas fatigue crack initiation has been empirically described using stress-life or strain-life methods. More recently, there has been efforts towards the use of the local stress-strain and similitude concepts to formulate fatigue crack growth rates. A new model has been developed which derives stress-life, strain-life and fatigue crack growth rates from strain energy density concepts. This new model has the advantage to predict an intrinsic stress ratio effect of the form ?ar=(?amp)?·(?max )(1-?), which is dependent on the cyclic stress-strain behaviour of the material. This new fatigue crack propagation model was proposed by Huffman based on Walkerlike strain-life relation. This model is applied to FCG data available for the P355NL1 pressure vessel steel. A comparison of the experimental results and the Huffman crack propagation model is made.

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: Empirical
Teacher disagreement score0.027
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.025
GPT teacher head0.233
Teacher spread0.208 · 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

Citations28
Published2017
Admission routes1
Has abstractyes

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