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Record W4252017510 · doi:10.3221/igf-esis.42.9

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

2017· article· en· W4252017510 on OpenAlex
P. J. Huffman, João P. R. Ferreira, J.A.F.O. Correia, Abílio M.P. De Jesus, G. Lesiuk, F. Berto, Alfonso Fernández‐Canteli, G. Glinka

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueFrattura ed Integrità Strutturale · 2017
Typearticle
Languageen
FieldEngineering
TopicFatigue and fracture mechanics
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsStrain energy density functionMaterials scienceFracture mechanicsStructural engineeringParis' lawStrain (injury)Crack closureStrain energyStress (linguistics)Pressure vesselStrain energy release rateMechanicsComposite materialFinite element methodEngineering

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.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.668
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
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 head0.025
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