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Record W1873958662 · doi:10.1115/1.4031599

Slurry Erosion of Pipeline Steel: Effect of Velocity and Microstructure

2015· article· en· W1873958662 on OpenAlexaff
Tahrim Alam, Md. Aminul Islam, Zoheir Farhat

Bibliographic record

VenueJournal of Tribology · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicErosion and Abrasive Machining
Canadian institutionsDalhousie University
Fundersnot available
KeywordsSlurryMicrostructureMaterials scienceErosionMetallurgyScanning electron microscopeFerrite (magnet)Composite materialErosion corrosionCorrosionGeology

Abstract

fetched live from OpenAlex

Pipelines are the most flexible, economic, and convenient way for oil and gas transportation. Material degradation by slurry erosion is a common feature in oil transmission pipeline. In the present work, slurry erosion of AISI 1018, AISI 1080, API X42, and API X70 steels is investigated in terms of slurry velocity and target material microstructure. The slurry velocity and impact angle employed were 0.2, 0.29, 0.36, and 0.43 m s−1 and 90 deg, respectively. It is found that erosion rate increases with increasing slurry velocity. Scanning electron microscopy was employed to investigate the eroded surface and subsurface of the steels. Plastic deformation, microcutting, and fracture are identified as dominant erosion mechanisms. Pearlitic microstructure exhibits superior erosion resistance compared to ferrite depending upon slurry velocity and microstructural orientation.

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 score0.002
Threshold uncertainty score0.003

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)0.0000.000

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 head0.263
Teacher spread0.252 · 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

Citations62
Published2015
Admission routes1
Has abstractyes

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