Effect of external wall defect growth on internal wall corrosion of oil pipelines under mechano‐electrochemical interaction
Bibliographic record
Abstract
Abstract The corrosion mechanism of X80 steel in simulated solutions of acid red soil and product oil sediment under mechano‐electrochemical interaction (MEI) was researched to build a new finite element model through multi‐physical field coupling simulation technology. Based on the new model, the effect of external wall defect growth on the internal wall corrosion of oil pipelines was analyzed in detail. The results showed that the distributions of stress and corrosion of the internal wall were extremely affected by the external wall defect depth, and the corrosion rate of the internal wall was enlarged remarkably by the external wall defect growth. Moreover, attributing to the local galvanic cell effect under MEI, the growth rates of corrosion defects in the depth and length directions of the external defect were accelerated because these directions were associated with larger stress and equivalent to the anode, but the corrosion rate in the width direction of the external defect was inhibited because this direction was associated with smaller stress and equivalent to the cathode.
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 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".