The Effect of Corrosion on Slurry Abrasion of Wear Resistant Steels
Bibliographic record
Abstract
Pipeline maintenance and replacement for slurry transportation constitutes a significant fraction of cost in many mining operations, particularly in the oil sand industry. It is thus important to better understand wear attack mechanisms and major factors affecting wear in such applications. In this study, the effect of corrosion on slurry abrasion response of pipe steels and abrasion resistant steels has been investigated using the Miller tester in silica slurries (water- or salt solution-based). A concept of relative synergy is introduced to illustrate the importance of corrosion-enhanced wear for a given material, which is defined as the percentage difference in wear rates between sliding in salt slurry and in de-ionized (DI) water slurry with respect to wear rate in DI water slurry. Steel hardness is found to have significant effect on the corrosion-abrasion behavior. Hard steels tend to show higher relative synergy. Extensive pitting corrosion is observed for hard steels after testing in salt solution slurry. For low hardness steels, general corrosion (with micro-pitting) is the dominant corrosion mechanism. Based on semi-empirical analysis, a wear map is constructed to illustrate the transitions of abrasion-corrosion regimes under different materials and working/testing conditions. The importance of mechanical interaction frequency and severity on corrosion-abrasion synergy is highlighted. The effect of material’s corrosion resistance on relative corrosion-abrasion synergy is currently not well understood and is not explicitly shown in the wear map. However, qualitatively, corrosion resistant materials generally show lower synergy under similar working/testing conditions. Hard and corrosion resistant materials should be employed when the working conditions fall within the abrasion-corrosion regime.
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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".