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Record W3164839849 · doi:10.5006/3774

Assessment of the Local Mechano-Electrochemical Effect on Pipeline Defects by 2D and 3D Finite Element Models

2021· article· en· W3164839849 on OpenAlexaff
Zhuwu Zhang, Jiuhong Zhang, Jinchang Wang, Y. Frank Cheng

Bibliographic record

VenueCORROSION · 2021
Typearticle
Languageen
FieldMaterials Science
TopicCorrosion Behavior and Inhibition
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsCorrosionMaterials scienceFinite element methodAnodeInternal pressureElectrochemistryPipeline (software)MetallurgyPipeline transportStress (linguistics)Structural engineeringComposite materialChemistryElectrodeEngineeringMechanical engineering

Abstract

fetched live from OpenAlex

Local corrosion at a defect on a pipeline was assessed using both 2-dimensional (2D) and 3-dimensional (3D) finite element models under mechano-electrochemical (M-E) interaction. While the M-E interaction increases corrosion activity at the defect, the assessment of M-E interaction would have different results using 2D and 3D models. Compared with the 3D model, the 2D model produces a greater local stress, a higher local plastic strain, a more negative corrosion potential and a higher anodic current density at the defect, and thus, a lower threshold internal pressure causing local yielding. The 3D model is more conservative for corrosion rate prediction of corroded pipelines. A new concept, rAZ (the ratio of the anodic zone length to defect length in the 2D model, or the ratio of the anodic zone area to the defect area in the 3D model), is proposed to define growth mode of the corrosion defect. There is a smaller rAZ produced in 2D model. At specific internal pressures, the 2D model predicts an ellipsoidal defect center area experiencing accelerated corrosion and potentially resulting in pipeline leaking.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.309
Threshold uncertainty score0.396

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 head0.010
GPT teacher head0.259
Teacher spread0.248 · 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

Citations4
Published2021
Admission routes1
Has abstractyes

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Same venueCORROSIONSame topicCorrosion Behavior and InhibitionFrench-language works237,207