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Record W1985560423 · doi:10.2514/1.c031129

Stress in Triple-Row Riveted Lap Joints Under the Influence of Specific Factors

2011· article· en· W1985560423 on OpenAlexafffund
Gang Li, Guoqing Shi, Nicholas C. Bellinger

Bibliographic record

VenueJournal of Aircraft · 2011
Typearticle
Languageen
FieldEngineering
TopicFatigue and fracture mechanics
Canadian institutionsNational Research Council Canada
FundersMinistère de la Défense NationaleRussian Science Foundation
KeywordsStructural engineeringStress (linguistics)Materials scienceEngineering

Abstract

fetched live from OpenAlex

Effects of fastener clearance fit, friction coefficient, and corrosion pillowing on the stress state in triple-row riveted lap joints were studied numerically using three-dimensional finite element methods. The material elastoplastic constitutive relationship and geometric nonlinear properties, as well as nonlinear contact boundary conditions, were included in the numerical simulations. The numerical modeling was validated using experimental measurements of the rivet driven-head deformation and in situ strains. The lap joints were loaded in tension after riveting. Three different clearance fits, three different friction coefficients, and four corrosion-pillowing conditions were analyzed. The stress variations along prescribed paths and full-field contours of the maximum principal stress on joint faying surfaces during the tensile loading stage were investigated. Insightful results of the stress state in riveted lap joints were obtained, which could effectively explain the phenomena observed from joint fatigue tests. The phenomena include 1) the potential major factor to cause the joint fatigue-life scatter, 2) crack-nucleation site location in the top rivet-hole vicinity on the outer-sheet faying surface for the noncorroded joints, and 3) the occurrence of the multiplesite damages in aged and corroded lap joints.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.615
Threshold uncertainty score0.272

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 head0.027
GPT teacher head0.218
Teacher spread0.191 · 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 designObservational
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

Citations24
Published2011
Admission routes2
Has abstractyes

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