Prediction and experimental evaluation of the threshold velocity in water droplet erosion
Bibliographic record
Abstract
Gradual wear of materials due to repetitive high-speed impacts of water droplets is a serious reliability concern for blades of gas, steam, and wind turbines. The phenomenon is commonly referred to as water droplet erosion (WDE). Analogous to fatigue, WDE has an endurance regime, called threshold velocity, where material resists erosion damage for prolonged exposure. The ability to predict the threshold velocity from material properties is crucial for the prevention of WDE phenomenon. In the past decades, developing models to predict the threshold velocity have been attempted. This has resulted in one semi-analytical model and several empirical equations, all of which exhibited limitations in applicability and physical meaning. Drawing on previous attempts along with contemporary theoretical and experimental investigations of WDE phenomenon, we report on the prediction of threshold velocity using an analytical model. The developed model represents the WDE threshold velocity using materials properties and impact conditions. A procedure to experimentally evaluate the threshold velocity in rotating erosion test devices has also been developed. The developed model predicted threshold velocities with higher accuracy than the previous analytical model. The present work introduces an important tool to the design and selection of WDE resistant materials.
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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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".