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Record W2205829526 · doi:10.1115/pvp2015-45886

Application of Elastic-Plastic Finite Element Analysis to Determine the Ultimate Load of Components

2015· article· en· W2205829526 on OpenAlexfundno aff
Wolf Reinhardt

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicEngineering Structural Analysis Methods
Canadian institutionsnot available
FundersCANDU Owners Group
KeywordsFinite element methodStructural engineeringComponent (thermodynamics)Ultimate loadPressure vesselFocus (optics)Computer scienceStress (linguistics)EngineeringMechanical engineeringPhysics

Abstract

fetched live from OpenAlex

An assessment of pressure retaining components based on ultimate load (or plastic instability load) is used in Section VIII Div. 2 and Section III Appendix F of the ASME Boiler and Pressure Vessel Code, as well as in various fitness-for-service standards and guidelines. The ultimate load analysis strives for a realistic prediction of the plastic behavior of a component up to the highest load or load combination that the component can support. The high level of applied load may cause significant deformations in the component, and for an accurate assessment the analysis must consider the effect of these deformations on the material as well as on equilibrium and the state of stress. This paper discusses briefly the basis of ultimate load analysis and considerations in performing such analysis with finite element analysis. The main focus is to validate the analysis approach by demonstrating close agreement between finite element analysis and analytical solutions and the results of component tests.

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)0.002
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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.022
GPT teacher head0.266
Teacher spread0.244 · 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

Citations0
Published2015
Admission routes1
Has abstractyes

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