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Record W2075138969 · doi:10.1115/ipc2014-33141

Mechanical Integrity Evaluation of Unequal Wall Thickness Transition Joints in Transmission Pipeline

2014· article· en· W2075138969 on OpenAlexaff
Xiaotong Huo, Shawn Kenny, M. Martens

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMechanical stress and fatigue analysis
Canadian institutionsTransCanada (Canada)Memorial University of Newfoundland
Fundersnot available
KeywordsBevelWeldingStructural engineeringFinite element methodButt jointJoint (building)Bevel gearStress (linguistics)Materials scienceEngineeringMechanical engineering

Abstract

fetched live from OpenAlex

Transition welds joining pipe segments of unequal wall thickness are typically designed using back-bevel butt welds in accordance with industry recommended practices. An alternative approach, for joining transition pieces, would be the use of a counterbore-taper design, which has been successively utilized by TransCanada PipeLines. In comparison with the back-bevel joint design, the counterbore-taper design provides a simple geometry that facilitates the welding process for joints of unequal wall thickness, improves the NDT quality and reliability, and increases the process efficiency for welding and NDT tasks. The counterbore-taper design reduces the effect of stress concentrations at the weldment and enhances fatigue life. A parameter study, using continuum based finite element methods, was conducted to comparatively examine the mechanical performance of a pipe joint, using back-bevel and counterbore-taper designs, with unequal wall thickness and different material grade. The parameters examined include pipe diameter, D/t ratio, axial force and moment. The numerical study assessed the mechanical stress response, including stress path, initial yield and onset of plastic collapse, for back-bevel and counterbore-taper joint designs. Based on these preliminary investigations, the performance of each transition joint design was evaluated and guidance on the selection of the joints design method was provided.

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)0.002
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.814
Threshold uncertainty score0.739

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.039
GPT teacher head0.275
Teacher spread0.236 · 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 designSimulation or modeling
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

Citations2
Published2014
Admission routes1
Has abstractyes

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