Bibliographic record
Abstract
Cavitation erosion normally occurs in a fluid dynamic system, which can cause failure of metal parts. It is a complicated process involving the interaction of electrochemical corrosion and mechanical wear. In this paper, various research methods for cavitation erosion behavior are reviewed. The present techniques of cavitation erosion degree measurement and different period determination include mass loss, volume loss, pit number, pit depth and surface roughness. 2D and 3D microstructure characterization observations are applied to discuss the evolution process and micro-zone damage. Hardness, residual stress and ratio of hardness to elastic modulus are good indicators for the degradation of surface mechanical properties. Electrochemical examinations are integrated to investigate the effect of cavitation on passive film and cavitation erosion mechanism. Furthermore, the influencing factors (mechanical properties, material chemical composition and microstructure) and the cavitation erosion characteristics of several metals (i.e. carbon steel, copper, stainless steel and titanium alloy) are introduced and summarized. Normally, the addition of Mn, Co, Cr, C and N can increase the cavitation erosion resistance. High hardness, high yield/tensile and toughness strength, good work-hardening property, fine grains are advantageous to the resistance to cavitation erosion. The cavitation erosion preferentially occurs on the lower intensity phase, which absorbs cavitation impact energy and mitigates the damage degree of a high strength phase. The interface between phases and grain boundary are also the weak spots to be attacked in the initiation and propagation of cavitation erosion. For passive metals, stainless steel and titanium alloy, the passive film is in a metastable state of depssivation/repassivation under cavitation. In a strongly corrosive medium, the synergetic effect of cavitation and corrosion promotes the thinning and semiconducting property change of the passive film.
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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 | 0.000 | 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) | 0.000 | 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 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".