MétaCan
Menu
Back to cohort
Record W2900148230 · doi:10.1115/ipc2018-78131

Effect of UOE Forming Process on the Buckling Strains of Steel Pipes

2018· article· en· W2900148230 on OpenAlexaff
Majid Tanbakuei Kashani, Magdi Mohareb, Mahyar Asadi, Mathew Smith

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicFatigue and fracture mechanics
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsBucklingResidual stressMaterials scienceBendingDeformation (meteorology)Structural engineeringFinite element methodPipeProcess (computing)Pipeline transportResidualComposite materialEngineeringMechanical engineeringComputer science

Abstract

fetched live from OpenAlex

Oil and gas pipelines are commonly made of steel pipes manufactured through the UOE process. This process starts with a flat steel plate, bends it into a U shape, then bends it further to form an O shape, welds the seam, and then radially expands (E) the pipe. The process induces significant residual stresses in the pipe wall. Such stresses have conventionally been ignored in past finite element analyses aimed at quantifying buckling strain thresholds. The present study develops a numerical technique to investigate the effect of the residual stresses induced in the UOE process on the local buckling strains of pipes. Two types of nonlinear 3D FEA models are developed to quantify the buckling strains of pipes under imposed bending deformation. The first model starts with a flat plate, models the UOE process to capture the residual stresses, and then subjects the pipes to imposed bending deformation, the second model assumes the pipe is free from residual stresses. Comparisons are then performed between the buckling strains predicted by both models.

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 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 score0.353
Threshold uncertainty score0.170

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)0.0000.000

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 head0.009
GPT teacher head0.240
Teacher spread0.232 · 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.

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

Citations3
Published2018
Admission routes1
Has abstractyes

Explore more

Same topicFatigue and fracture mechanicsFrench-language works237,207