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Record W2077880687 · doi:10.1115/1.1465439

Investigating the Interaction Behavior Between Two Arbitrarily Oriented Surface Cracks Using Multilevel Substructuring

2002· article· en· W2077880687 on OpenAlexafffund
Walied A. Moussa

Bibliographic record

VenueJournal of Pressure Vessel Technology · 2002
Typearticle
Languageen
FieldEngineering
TopicFatigue and fracture mechanics
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCoalescence (physics)Stress intensity factorMaterials scienceFinite element methodBrittlenessStructural engineeringStrain energyFracture mechanicsTension (geology)Position (finance)Composite materialUltimate tensile strengthEngineeringPhysics

Abstract

fetched live from OpenAlex

The existence of arbitrarily oriented multiple cracks is a common problem in brittle materials. Some of these materials, such as ceramics, are used in mechanical and aerospace structures that suffer from aging. Because of that, such structures have shown some signs of sudden partial or total failure. The interaction and coalescence of multiple cracks may significantly affect the designed lives of aging structures. Knowledge of the growth behavior of interacting cracks is still limited. In this paper, a novel submodeling meshing algorithm is used to construct different cases of arbitrarily oriented identical surface cracks in a plate subjected to remote tension. These cases are solved using finite element analysis (FEA) and covered a wide range of crack geometries. The stress intensity factors (SIFs) and the energy release rates (G) for these cracks are calculated as a function of their relative orientation and the position along the interaction crack-front. In this paper, the studied ratio of crack depth to plate thickness, a/t, and to crack length, a/c, are kept at 0.2 and 0.3, respectively. Where possible, a comparison of the 3-D results with 2-D ones is also considered.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.152
Threshold uncertainty score0.674

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 integrity0.0000.001
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 head0.034
GPT teacher head0.268
Teacher spread0.234 · 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 designBench or experimental
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

Citations2
Published2002
Admission routes2
Has abstractyes

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