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Record W1985024871 · doi:10.1115/ipc2014-33033

Methanol-Induced Axial Stress Corrosion Cracking in a Northern Canadian Liquids Pipeline

2014· article· en· W1985024871 on OpenAlexaboutno aff
Barbara N. Padgett, Mohamed R. Chebaro, David M. Norfleet, John A. Beavers, Scott Ironside

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMaterials Science
TopicCorrosion Behavior and Inhibition
Canadian institutionsnot available
FundersDNV GL
KeywordsStress corrosion crackingIntergranular corrosionMaterials scienceMetallurgyPipeline transportCrackingCorrosionMethanolPipeline (software)WeldingPetroleumStress (linguistics)Ultimate tensile strengthSlow strain rate testingEnvironmental stress crackingForensic engineeringEnvironmental scienceComposite materialGeologyEngineeringMechanical engineering

Abstract

fetched live from OpenAlex

Three in-service leaks on a crude oil pipeline operating in Canada were investigated to identify their metallurgical cause(s). The releases were found to be associated with cracks originating from the internal surface of the pipeline. Further similarities between the releases included: (1) the axial directionality of the cracks, (2) the short crack length, (3) the crack location adjacent to girth welds, (4) the circumferential location of the cracks and (5) the intergranular crack morphologies. A comprehensive metallurgical investigation concluded that the likely crack-initiating mechanism was methanol-induced stress corrosion cracking (SCC). While this SCC mechanism is extremely rare in buried petroleum pipelines, all other plausible causes were considered and eliminated. Methanol-induced SCC, similar to other forms of SCC, requires three contributing factors: (1) a susceptible material, (2) a corrosive environment and (3) sufficient tensile stresses. Although much research has been performed on the effects of ethanol on pipeline steels, published data on the effects of methanol is very scarce. A laboratory research program using slow strain rate (SSR) testing was initiated to determine if pipeline steels are susceptible to methanol-induced SCC and identify the conditions necessary to reproduce it in a laboratory environment. This paper outlines the major findings from this program.

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 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 categoriesInsufficient payload (model declined to judge)
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 score0.738
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 integrity0.0000.000
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 head0.019
GPT teacher head0.259
Teacher spread0.240 · 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 teacher head, not a consensus.

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

Citations5
Published2014
Admission routes1
Has abstractyes

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