Influencing factors of various combinations of abrasion, cavitation, and corrosion caused by multiphase flow impact
Bibliographic record
Abstract
The surface of ships and other marine transportation equipment is abraded by sandy seawater when under operation. And with a change in speed, cavitation erosion will occur. Seawater has a corrosive effect on metal materials, and the form of damage to the surface materials of ships is the combined erosion of abrasion, cavitation, and corrosion. To study the influencing factors of various forms of erosion, an experimental device for combined erosion was developed. The effects of sand concentration, sand size, salt concentration, and impact speed on the various combinations of abrasion, cavitation, and corrosion of 0.45% C steel specimens are studied experimentally. The results showed that the degree of wear of the combined erosion of abrasion, cavitation, and corrosion was stronger than the combined erosion of abrasion and cavitation or single corrosion erosion. Abrasion, cavitation, and corrosion promote each other, which aggravates the wear of materials and accelerates the loss of material. Under the conditions of low sand concentration and low salt concentration, the mass loss of metal materials for various combinations of abrasion, cavitation, and corrosion was proportional to the sand concentration, sand size, salt concentration, and impact speed. The greatest factor of metal degradation was impact speed.
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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 | 0.001 | 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) | 0.000 | 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".