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Record W1990161967 · doi:10.1115/pvp2012-78091

Simulation of Ductile Crack Propagation in Pipeline Steels Using Cohesive Zone Modeling

2012· article· en· W1990161967 on OpenAlexaff
Andrew Dunbar, Xin Wang, Bill Tyson, Su Xu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicFatigue and fracture mechanics
Canadian institutionsNatural Resources CanadaCarleton University
FundersAustralian Government
KeywordsMaterials scienceCohesive zone modelToughnessFracture mechanicsFracture toughnessPipeline transportComposite materialCrack tip opening displacementStructural engineeringCrack closureEngineeringMechanical engineering

Abstract

fetched live from OpenAlex

This paper presents recent results of numerical studies on stable crack extension of high toughness steels typical of those in modern gas pipelines using cohesive zone modeling. Two sets of materials are modeled. The first material set models a typical structural steel, with variable toughness described by four traction-separation (TS) laws. The second set models an X70 pipe steel, with three different TS laws. For each TS law, there are three defining parameters: the maximum cohesive strength, the final separation and the work of separation. The specimens analyzed include a crack in an infinite plate (small-scale yielding, SSY) and a standard drop-weight tear test (DWTT). Fracture propagation characteristics and values of crack-tip opening angle (CTOA) are obtained from these two types of specimens. It is shown that cohesive zone models can be successfully used to simulate ductile crack propagation and to numerically measure CTOAs. The ductile crack propagation characteristics and CTOAs obtained from SSY and DWTT specimens are compared for each set of steels. In addition, the CTOA results from numerical cohesive zone modeling of DWTT specimens of X70 steel are compared with those from laboratory 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 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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.589
Threshold uncertainty score0.284

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 head0.037
GPT teacher head0.262
Teacher spread0.225 · 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

Citations3
Published2012
Admission routes1
Has abstractyes

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