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Record W3151933576 · doi:10.1115/1.4050091

Numerical Assessment of Elbow Element Response Under Internal Pressure

2021· article· en· W3151933576 on OpenAlexafffund
Saher Attia, Magdi Mohareb, M. Martens, Nader Yoosef‐Ghodsi, Yong Li, Samer Adeeb

Bibliographic record

VenueJournal of Pressure Vessel Technology · 2021
Typearticle
Languageen
FieldEngineering
TopicFatigue and fracture mechanics
Canadian institutionsTransCanada (Canada)University of OttawaUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPipingElbowShell (structure)Internal pressureStructural engineeringFinite element methodNonlinear systemMaterials scienceMechanicsEngineeringMechanical engineeringPhysicsComposite materialSurgery

Abstract

fetched live from OpenAlex

Abstract This study presents a detailed assessment for the response of elbow elements in ABAQUS under internal pressure. Two main cases are considered: (1) a standalone 90 deg pipe bend, which was analyzed using both elastic and elasto-plastic material characterizations using shell and elbow elements; and (2) a geometrically nonlinear analysis with a materially elastic characterization of a piping system was conducting using shell, elbow, and pipe elements. Although the results show the capabilities of elbow elements to simulate the response of pipe bends in the elastic regime, the elements do not provide reliable predictions beyond the elastic range. Additionally, an assessment of the ASME B16.49 2017 elbow thickness equation and previously published stress estimate equations was carried out by comparisons with elbow and shell elements predictions.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.008
GPT teacher head0.263
Teacher spread0.255 · 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 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

Citations10
Published2021
Admission routes2
Has abstractyes

Explore more

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