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Record W3094868428 · doi:10.1115/pvp2020-21046

Failure Pressure Prediction of Crack in Corrosion Defects in 2D by Using XFEM

2020· article· en· W3094868428 on OpenAlexaff
Xinfang Zhang, Allan Okodi, Leichuan Tan, Juliana Y. Leung, Samer Adeeb

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicStructural Integrity and Reliability Analysis
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsFinite element methodExtended finite element methodStructural engineeringPolygon meshMaterials scienceCorrosionFracture mechanicsPipeline (software)Computer scienceEngineeringComposite material

Abstract

fetched live from OpenAlex

Abstract Aging pipelines may experience several different types of degradation, such as crack and corrosion, which pose serious concerns for the pipeline integrity. Hybrid flaws such as crack-in-corrosion (CIC), can be challenging to model and understand. For instance, predicting the failure pressure using the finite element method (FEM) is relatively difficult; therefore, the extended finite element method (XFEM) is introduced here. Compared to the conventional FEM, which requires extremely fine meshes and is impractical for modelling dynamic crack propagation, XFEM is computationally efficient as there is no need to update the mesh elements for tracking the crack path. This paper aims to study the applicability of XFEM in predicting the failure pressure of CIC defects in 2D. In particular, mesh size sensitivity and the effects of different CIC parameters on the final failure pressure were examined. ABAQUS v 6.14 was used for this simulation study. For simplicity, only half of the pipe was modelled assuming symmetry around the horizontal plane. A CIC defect was placed at the exterior of the pipe. The corroded area was assumed to be semi-elliptical, and the crack was simulated as a longitudinal crack. In this paper, failure criterion was satisfied when the crack has propagated to the last element. Several models were built in which the length and width of the elements at the crack tip were changed. An optimum mesh size was determined and was applied subsequently in several other models to study the impacts of crack depths, corroded area widths, and corrosion profiles. The results showed that when the total defect depth was fixed at 50% of the wall thickness, the failure pressure decreased with increasing the crack depth, while both corroded area width and corrosion profile only have a secondary effect on the failure pressure. In addition, the failure pressure of a CIC defect was bound between that of a crack-only defect and a corrosion-only defect. When the depth of the crack is higher than 50% of the total defect area, the CIC defect can be treated as a crack only defect with a crack depth equal to the total defect depth.

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 score0.008
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0020.001
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 head0.013
GPT teacher head0.210
Teacher spread0.197 · 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

Citations4
Published2020
Admission routes1
Has abstractyes

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