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Record W3095535798 · doi:10.1115/pvp2020-21304

Examination of the Transferability of CTOA From Small-Scale DWTT to Full-Scale Pipe Geometries Using a Cohesive Zone Model

2020· article· en· W3095535798 on OpenAlexaff
Chris Bassindale, Xin Wang, W. R. Tyson, Su Xu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicFatigue and fracture mechanics
Canadian institutionsNatural Resources CanadaCarleton University
Fundersnot available
KeywordsFinite element methodMaterials scienceScale (ratio)Cohesive zone modelStructural engineeringFracture (geology)Composite materialEngineering

Abstract

fetched live from OpenAlex

Abstract In this work, the cohesive zone model (CZM) was used to examine the transferability of the crack tip opening angle (CTOA) from small-scale to full-scale geometries. The pipe steel STPG370 was modeled. A drop-weight tear test (DWTT) model and pipe model were studied using the finite element code ABAQUS 2017x. The cohesive zone model was used to simulate crack propagation in 3D. The CZM parameters were calibrated based on matching the surface CTOA measured from a DWTT finite element model to the surface CTOA measured from the experimental DWTT specimen. The mid-thickness CTOA of the DWTT model was in good agreement with the experimental value determined from E3039 and the University of Tokyo group’s load-displacement data. The CZM parameters were then applied to the pipe model. The internal pressure distribution and decay during the pipe fracture process was modeled using the experimental data and implemented through a user-subroutine (VDLOAD). The mid-thickness CTOA from the DWTT model was similar to the mid-thickness CTOA from the pipe model. The average surface CTOA of the pipe model was in good agreement with the average experimental value. The results give confidence in the transferability of the CTOA between small-scale specimens and full-scale pipe.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
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)0.0020.000

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 head0.030
GPT teacher head0.203
Teacher spread0.174 · 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".

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Citations1
Published2020
Admission routes1
Has abstractyes

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