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Record W4403114081 · doi:10.3390/met14101132

Evaluating the Effect of Blended and Pure Hydrogen in X60 Pipeline Steel for Low-Pressure Transmission Using Hollow-Specimen Slow-Strain-Rate Tensile Testing

2024· article· en· W4403114081 on OpenAlexaff
Rashiga Walallawita, Matthew C. Hinchliff, D. Sediako, John P. Quinn, Vincent Chou, Kim Walker, Matthew Hill

Bibliographic record

VenueMetals · 2024
Typearticle
Languageen
FieldMaterials Science
TopicHydrogen embrittlement and corrosion behaviors in metals
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
Fundersnot available
KeywordsMaterials scienceUltimate tensile strengthStrain (injury)Strain ratePipeline (software)Tensile strainHydrogenTransmission (telecommunications)Composite materialMetallurgyHydrostatic testMechanical engineeringEngineeringChemistryElectrical engineeringAnatomy

Abstract

fetched live from OpenAlex

This study employs a custom hollow specimen setup to investigate the HE in API 5L X60 pipeline base and welded materials exposed to pure hydrogen and a 20% hydrogen–natural gas blend at 2.07 MPa. Results indicate embrittlement with increasing hydrogen concentration. The base material showed a hydrogen embrittlement index (HEI) of 11.6% at 20% hydrogen and 12.4% at 100% hydrogen. For the welded material, the HEI was 14.6% at 20% hydrogen and 18.0% at 100% hydrogen. Fractography analysis revealed that the base and welded materials exhibited typical ductile fracture features in the absence of hydrogen, transitioning to a mixture of quasi-cleavage and micro-void coalescence (MVC) features in hydrogen environments. Additionally, with hydrogen, increased formation of secondary cracks was observed. Notably, the study identified the Hydrogen-Enhanced Localized Plasticity (HELP) mechanism as a probable contributor to hydrogen-assisted fracture.

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)0.000
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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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 head0.065
GPT teacher head0.357
Teacher spread0.292 · 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

Citations10
Published2024
Admission routes1
Has abstractyes

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