Internal cathodic protection to study the erosion‐corrosion of <scp>AISI</scp> 1018 carbon steel
Bibliographic record
Abstract
Abstract Steel elbows used in pipelines carrying potash slurry may undergo material loss due to mechanical erosion and/or electrochemical corrosion. Cathodic protection was used to study the effects of flow velocity and slurry concentration on the protection current density required to maintain AISI 1018 carbon steel elbows at a set electric potential. A flow loop with a peristaltic pump was used to pump a slurry consisting of silica sand in a saturated potash brine through the steel elbows. The protection current density measurements were performed under varying slurry flow velocities of 2.5, 3.0, 3.5, and 4.0 m/s and varying slurry concentrations ranging from 0‐35 wt%. The results show maximum values of protection current density were required for mid‐range slurry concentrations. It is concluded that, within the parameters of study, high flow velocities require higher protection current density than low flow velocities. Furthermore, high flow velocities result in a local maximum protection current density being reached at lower slurry concentrations; conversely, a local maximum protection current density is reached at higher slurry concentrations in lower flow velocities. The contrast in local maximum protection current densities likely occurs due to additional particle‐particle collisions at higher velocities causing the maximum mass transfer rate of oxygen to be reached at lower slurry concentrations. SEM micrographs show that wear becomes more evenly distributed with increasing flow velocity due to the homogeneous distribution of particles in the flowing medium.
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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) | 0.000 | 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".