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Record W2090589599 · doi:10.1115/1.4005867

Limit Load Solutions for Cracked Elbows Subjected to Internal Pressure and In-Plane Bending

2012· article· en· W2090589599 on OpenAlexfundno aff
C. Hari Manoj Simha

Bibliographic record

VenueJournal of Pressure Vessel Technology · 2012
Typearticle
Languageen
FieldEngineering
TopicNumerical methods in engineering
Canadian institutionsnot available
FundersCANDU Owners Group
KeywordsLimit loadLimit (mathematics)Bending momentInternal pressureMoment (physics)Envelope (radar)Limit analysisStructural engineeringMechanicsBendingPlane (geometry)Materials scienceMathematicsPhysicsEngineeringFinite element methodComposite materialMathematical analysisGeometryClassical mechanics

Abstract

fetched live from OpenAlex

Abstract In this article, limit load solutions for cracked elbows containing through-wall and part through-wall axial and circumferential cracks under internal pressure and in-plane bending loading are presented. For elbows with axial cracks, limit pressure solutions are presented, and modifications to existing limit moment solutions are proposed. The foregoing limit pressure and limit moment solutions are used in conjunction with a novel interaction curve to obtain limit load solutions for elbows with axial cracks under combined pressure and moment loading. If the applied moment and pressure are within (outside) the envelope of the interaction curve, no failure (failure) is indicated. Furthermore, limit pressure and limit moment solutions for circumferentially cracked elbows are developed using the same interaction curve. Limit loads computed with the solutions presented in this work are compared with experimental results and the agreement is found to be within acceptable limits after accounting for the uncertainties in the experimental results.

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)0.004
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: none
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.017
GPT teacher head0.271
Teacher spread0.254 · 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

Citations3
Published2012
Admission routes1
Has abstractyes

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