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Record W2315576092 · doi:10.1115/1.4033181

Ovalization Defect in Energy Pipes Caused by Concentrated Load

2016· article· en· W2315576092 on OpenAlexafffund
Hossein Ghaednia, Sreekanta Das

Bibliographic record

VenueJournal of Offshore Mechanics and Arctic Engineering · 2016
Typearticle
Languageen
FieldEngineering
TopicStructural Integrity and Reliability Analysis
Canadian institutionsUniversity of Windsor
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPipeline transportFinite element methodPipeline (software)Structural engineeringEngineeringGeotechnical engineeringStructural integrityFull scaleForensic engineeringMechanical engineering

Abstract

fetched live from OpenAlex

Steel pipes are used to build pipelines that carry gas and oil across a country or a continent. The majority of onshore pipelines run underground; hence, they are called buried pipelines. These buried pipelines must endure external interferences and complex loading that result from geotechnical causes, aggressive environments, and operational requirements. Many segments of an underground pipeline may rest on rock tips and other localized hard surfaces, resulting in concentrated reaction load acting on small area of the outer wall of the operating pipeline. As a result, permanent inward deformations in the pipe wall, known as dent defect, can form. In addition, a resulting cross-sectional irregularity, known as an ovalization defect, can also occur. Pipe ovalization defects are a concern of pipeline operating companies, as the defect may challenge a pipeline's operation and/or structural integrity and safety. This research was completed by the Centre of Engineering Research in Pipelines located at the University of Windsor to examine the effects that rock tip shape, operating (internal) pressure, and a pipe's diameter-to-thickness ratio (D/t) have on an NPS30 X70-grade pipe's ovalization defect when it is subjected to such a concentrated load. This article discusses the lab-based full-scale examinations, finite element analysis (FEA) simulations, results, and discussions.

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 score0.005

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 studies0.0000.001
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 head0.005
GPT teacher head0.186
Teacher spread0.180 · 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

Citations1
Published2016
Admission routes2
Has abstractyes

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