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Record W2065437478 · doi:10.1115/ipc2012-90674

Cyclic Pressure Testing a Section of 34” Pipe Repaired Using the PETROSLEEVE Technology to Determine the Effect on a 50% Crack

2012· article· en· W2065437478 on OpenAlexaff
Rick Wang, Richard Kania, Robert Smyth, Ian R. Smyth

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicStructural Integrity and Reliability Analysis
Canadian institutionsTransCanada (Canada)
Fundersnot available
KeywordsMaterials scienceCompression (physics)Pipeline transportPipeStructural engineeringHydrostatic testComposite materialParis' lawPipeline (software)Fracture mechanicsCrack closureEngineeringMechanical engineering

Abstract

fetched live from OpenAlex

TransCanada Pipelines operates a large mainline pipeline transportation system. Engineering analysis and severe testing was performed to confirm that the PETROSLEEVE© Steel Compression Reinforcement Technology would arrest crack extension in large diameter pipe. This testing involved putting a 50% crack into a section of 862 mm diameter, 9.5mm wall thickness grade 448 pipe. Then a compression sleeve was installed while the pipe was pressurized to 3800 kPa (38% SMYS). Following sleeve installation, the test vessel was subjected to 9000 cycles 7880 to 2960 kPa (80%–30% SMYS); 200 cycles 7800 to 0 kPa (80%–0% SMYS); hold pressures of 8870 kPa (90% SMYS) for 4 hours and 10840 kPa (110% SMYS) for 2 hours. Following the cyclic pressuring, the crack was metallurgically inspected. It was reported by third party inspection that the compression sleeve reinforcement “can effectively suppress fatigue crack growth of an axial flaw (100mm long × 50% of the wall thickness deep) in the API X65 pipe.” This paper reviews the engineering and cyclic testing undertaken.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.013

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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.019
GPT teacher head0.257
Teacher spread0.238 · 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 designBench or experimental
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
Published2012
Admission routes1
Has abstractyes

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