Study on corrosion rate of carbon steel pipe under turbulent flow conditions
Bibliographic record
Abstract
Abstract The effect of time (or corrosion products formation) on corrosion rate of carbon steel pipe in aerated 0.1 N NaCl solution under turbulent flow conditions was investigated. Tests were conducted using electrochemical polarisation technique by determining the limiting current density of oxygen reduction in Reynolds number (Re) range of 15 000 to 113 000 and temperature range of 30–60°C. The effect of corrosion products formation on the friction factor increase was studied and discussed. Empirical correlations for limiting current density as a function of Re were obtained at various solution temperatures for clean surface and for corroded surface. It was found that formation of corrosion products with time decreases the corrosion rate at low Re and temperature, while it increases the corrosion rate at high Re and temperature. As the corrosion products formed the surface roughness increased leading to increase the friction factor depending on temperature, Re, and corrosion rate. On a analysé l'effet du temps (ou la formation de produits de corrosion) sur la vitesse de corrosion d'un tuyau d'acier au carbone dans une solution 0.1 N NaCl aérée dans des conditions d'écoulement turbulent. Des essais ont été réalisés à l'aide de la technique de polarisation électrochimique en déterminant la densité de courant limité de la réduction de l'oxygène dans l'écart de nombres de Reynolds de 15000 à 113000 et dans l'échelle de température de 30°C à 60°C. L'effet de la formation de produits de corrosion sur l'augmentation du facteur de frottement a été étudié et discuté. Les corrélations empiriques pour la densité de courant limité comme fonction du nombre de Reynolds ont été obtenues à diverses températures de solution pour une surface propre et une surface corrodée. On a découvert que la formation de produits de corrosion avec le temps réduit la vitesse de corrosion à une température et un nombre de Reynolds bas tout en augmentant la vitesse de corrosion à une température et nombre de Reynolds élevés. Pendant que les produits de corrosion se formaient, la rugosité de la surface augmentait, ce qui a mené à une augmentation du facteur de frottement selon la température, le nombre de Reynolds et la vitesse de corrosion. © 2010 Canadian Society for Chemical Engineering
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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 | 0.001 |
| 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".