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Record W2075415584 · doi:10.1115/1.4000199

Simplified Stress Categorization Using a Single Linear Elastic Analysis

2009· article· en· W2075415584 on OpenAlexafffund
Farhad M. Hossain, Wolf Reinhardt, R. Seshadri

Bibliographic record

VenueJournal of Pressure Vessel Technology · 2009
Typearticle
Languageen
FieldEngineering
TopicFatigue and fracture mechanics
Canadian institutionsMemorial University of Newfoundland
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsTangentPressure vesselStructural engineeringLimit loadFinite element methodLinear elasticityStress (linguistics)Computer scienceMathematicsAlgorithmEngineeringGeometryMechanical engineering

Abstract

fetched live from OpenAlex

The ASME Boiler and Pressure Vessel Code (code) provides guidelines for the classification of linear elastic stresses into primary, secondary, and peak stresses. Although these guidelines cover a wide range of pressure containing components, the guidelines are sometimes difficult to employ for three-dimensional components with complex geometry. This paper uses the mα-tangent method, an assessment of constraint in the component, based on limit load multiplier estimates as a stress classification tool. The method is applied to several practical pressure vessel components from simple to relatively complex geometric configurations. The results compare well with those obtained by conventional techniques, e.g., inelastic finite element analysis.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.830
Threshold uncertainty score0.536

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.012
GPT teacher head0.235
Teacher spread0.223 · 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

Citations3
Published2009
Admission routes2
Has abstractyes

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