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Record W2062487035 · doi:10.1115/pvp2012-78529

A Numerical Study on CMOD Compliance for Single-Edge Bending Specimens

2012· article· en· W2062487035 on OpenAlexafffund
Enyang Wang, Wenxing Zhou, Guowu Shen, Da-Ming Duan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMechanical stress and fatigue analysis
Canadian institutionsTransCanada (Canada)Natural Resources CanadaWestern University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPlane stressMaterials sciencePlane (geometry)Structural engineeringBendingFinite element methodStress (linguistics)Enhanced Data Rates for GSM EvolutionComposite materialGeometryMathematicsComputer scienceEngineering

Abstract

fetched live from OpenAlex

Several well-known equations for estimating the crack length in the SE(B) specimens from the normalized CMOD compliance are evaluated based on two- and three-dimensional finite element analyses. Two-dimensional analyses are carried out first to verify the reported accuracy and applicable ranges for each equation based on the plane strain models with six different crack lengths. Three-dimensional analyses are then carried out to estimate the errors of prediction of the equations that evaluate the crack length from the plane stress- and plane strain-based CMOD compliances. Both plain-sided and side-grooved models are included in the three-dimensional analyses and have seven different thickness-to-width ratios. The error of prediction of a given equation is largely impacted by the thickness-to-width ratio, crack length, presence of side grooves, and use of the plane stress- and plane strain-normalized CMOD compliance. Based on the errors of prediction, the relevance of the plane strain and plane stress conditions to the actual state of stress in the ligament of the SE(B) specimens is inferred. Knowledge of the relevance of the plane stress and plane strain conditions can be used to select either plane stress- or plane strain-based CMOD compliance in the crack length-CMOD equations and improve the accuracy of the prediction.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.773
Threshold uncertainty score0.354

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.136
GPT teacher head0.307
Teacher spread0.172 · 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

Citations4
Published2012
Admission routes2
Has abstractyes

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