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Record W2899807175 · doi:10.1115/ipc2018-78720

Influence of Mean Load Pressure Fluctuations on Crack Growth Behavior in Steel Pipelines

2018· article· en· W2899807175 on OpenAlex

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

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicStructural Integrity and Reliability Analysis
Canadian institutionsTransCanada (Canada)University of Alberta
Fundersnot available
KeywordsPipeline transportMaterials scienceAmplitudeStress (linguistics)MechanicsInternal pressureStructural engineeringParis' lawPipeline (software)Crack closureFracture mechanicsComposite materialEngineeringPhysicsMechanical engineering

Abstract

fetched live from OpenAlex

Internal pressure fluctuations during pipeline operations could contribute to crack growth in steel pipelines. These pressure fluctuations create a variable amplitude loading condition with large amplitude cycles at near-zero stress ratio, R (minimum stress / maximum stress) and small amplitude cycles (minor cycles) at near +1 R ratio which can both affect crack propagation. Mean stresses fluctuate with pressure due to fluid friction losses proportional to the distance from the pump/compressor station. A deeper understanding of mean stress sensitivity on crack growth rate in steel pipelines is sought. The aim of this research is to retard crack growth in pipelines by prescribing pressure fluctuations, thus controlling mean stress effects on imperfection growth in steel pipelines under a near neutral pH environment. This study shows that prescriptive mean load pressure fluctuations can be used to reduce crack growth rates in steel pipelines, thus expanding pipeline integrity management methods.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.645
Threshold uncertainty score0.327

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 integrity0.0000.000
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 head0.011
GPT teacher head0.249
Teacher spread0.237 · 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