A New Pipeline Crevice Corrosion Model with O2 and CP
Bibliographic record
Abstract
Abstract This paper provides a review of a pipeline crevice corrosion model (PCCM) previously developed to investigate the crevice corrosion of coated pipelines under cathodic protection (CP).1,2 The crevice is a channel, formed between a pipe surface and a disbonded high-density polyethylene (HDPE) coating. Oxygen (O2) is the cause of the crevice corrosion; it reaches into the crevice by migration through coating defects (holidays) and by diffusion through the coating. An induced current caused by an O2 concentration cell has been quantified flowing through the crevice solution layer from within the deaerated area to the aerated holiday. This induced current results from polarization at the pipe surface. The polarization was considered in this model and the crevice corrosion levels off within the crevice. The earlier models did not consider the pipe polarization and the corrosion rate decreases sharply from the holiday into the crevice. External CP current reduces crevice corrosion by consuming O2 near the holiday area. The CP cannot penetrate sufficiently deep into the crevice and the corrosion there is determined by O2 transport through the coating and the local pH. Lower crevice solution resistivity increases the CP penetration into the crevice significantly. The key for a sufficient protection of the pipelines is an appropriate CP that reduces all O2 diffusing from holidays and a good coating that minimizes the permeation of O2.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".