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Record W2000914220 · doi:10.1115/ipc2012-90675

Developing Cathodic Protection Based on Disbondment Geometry

2012· article· en· W2000914220 on OpenAlexaff
Karina Chevil, A. Eslami, Weixing Chen, Reg Eadie, Richard Kania, Robert Worthingham, Greg Van Boven

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMaterials Science
TopicCorrosion Behavior and Inhibition
Canadian institutionsSpectra Energy (Canada)TransCanada (Canada)University of Alberta
Fundersnot available
KeywordsCathodic protectionCorrosionMaterials scienceCoatingGalvanic cellMetallurgyComposite materialElectromagnetic shieldingAnodeElectrodeChemistry

Abstract

fetched live from OpenAlex

Coating disbondment on pipelines is a common phenomenon that leads to exposure of the pipeline metal to ground water solutions, promoting a corrosive environment which is associated with stress corrosion cracking (SCC). This investigation tracks the corrosion behavior of X-65 steel at different coating disbondments at Open Circuit Potential (OCP) conditions through weight loss tests, as well as SEM analysis. For the weight loss test, X-65 coupons were placed into a vertical coupon holder with varying gap sizes between the coupons and the shielding (2 mm 5 mm, and 10 mm) simulating the coating disbondment. The results of the tests suggest that the corrosion rate inside the gap is dependent on the mass transfer rate of CO2. For larger gaps, the corrosion rate inside the gap decreases almost linearly with position (highest at the top of the disbondment) and is dependent on the iron carbonate film deposition. For smaller gaps (≤5 mm) a galvanic effect was observed between the coupons at the top of the disbondment (anodic) which showed a high degree of general corrosion, and the coupons at the bottom of the disbondment (cathodic) which had very low corrosion rates. The experimental observations were related to the corrosion scenarios found in the field. Cathodic protection strategies with respect to the geometry of disbondment are also discussed.

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 categoriesInsufficient payload (model declined to judge)
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 score0.322
Threshold uncertainty score0.999

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)0.0020.001

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 head0.058
GPT teacher head0.291
Teacher spread0.234 · 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.

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

Citations5
Published2012
Admission routes1
Has abstractyes

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