Some thoughts on modelling abrasion-corrosion: wear by hard particles in corrosive environments
Bibliographic record
Abstract
Abstract Wear by hard particles can involve abrasion or erosion and is one of the most severe forms of wear. When a corrosive environment is present, the material loss rate can be significantly increased due to interactions (synergy) between the mechanical and chemical/electrochemical actions. In developing strategies for mitigating such adverse synergistic effect, it is important to understand the complex effect of various parameters on material loss under given tribocorrosion conditions. In this paper, a model is presented for wear-corrosion synergy in abrasive wear by hard particles applicable to many conditions in both the marine renewable (abrasion by high concentrations of large sand particles on tidal turbines) and extractive metallurgy (abrasive wear in mineral extraction). The mechanical wear loss is modeled based on the grooving mechanism (micro-cutting/micro-ploughing). Wear-enhanced corrosion is calculated from the fresh surface areas generated by grooving and the corresponding transient corrosion current. The concept of “corrosion-degraded layer” on the worn surface is introduced to account for the corrosion-enhanced wear; within this corrosion-degraded layer, the material loss rate is higher under the same mechanical wear conditions than in the material that is unaffected by corrosion. Based on the model, the effect of wear conditions on synergy in hard particle wear-corrosion has been discussed. The relative thickness of the corrosion-degraded layer to the depth of hard particle penetration (grooving) in the mechanical wear is found to be an important parameter in determining the relative severity of synergy in different tribo-corrosion systems. Good qualitative agreement has been observed between the predictions and published experimental results obtained from a range of abrasion-corrosion and erosion-corrosion lab testing.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 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 | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.006 |
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 teacher head, 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".