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Record W2091468099 · doi:10.1115/omae2009-79448

Constraint-Based Fracture Mechanics Analysis of Cylinders With Circumferential Cracks

2009· article· en· W2091468099 on OpenAlexaff
Michael Bach, Xin Wang, Robert A. Bell

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicFatigue and fracture mechanics
Canadian institutionsCarleton University
Fundersnot available
KeywordsConstraint (computer-aided design)Materials scienceFinite element methodCylinderStructural engineeringStress intensity factorFracture (geology)Ultimate tensile strengthStress (linguistics)Fracture mechanicsFailure assessmentLinear elasticityComposite materialMechanicsMathematicsGeometryEngineeringPhysics

Abstract

fetched live from OpenAlex

In this paper, the fracture behaviour of hollow cylinders with internal circumferential crack under tensile loading is examined extensively. Finite element analysis of the cracked cylinders is conducted to determine the fracture parameters including stress intensity factor, T-stress, and J-integral. Linear elastic finite element analysis is conducted to obtain K and T-stress, and elastic plastic analysis is conducted to obtain fully plastic J-integrals. A wide range of cylinder geometries are studied, with cylinder thickness ratios of ri/ro = 0.2 to 0.8 and crack depth ratio a/t = 0.2 to 0.8. These fracture parameters are then used to construct conventional and constraint-based failure assessment diagrams (FADs) to determine the maximum load carrying capacity of cracked cylinders. It is demonstrated that these tensile loaded cylinders with circumferential cracks are under low constraint conditions, and the load carrying capacity are higher when the low constraint effects are properly accounted for, using constraint-based FADs, comparing to the predictions from the conventional FADs.

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)0.000
metaresearch head score (Gemma)0.001
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: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.006
GPT teacher head0.200
Teacher spread0.193 · 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

Citations1
Published2009
Admission routes1
Has abstractyes

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