Experimental and CFD simulation of interactions between water droplets with different surface features to understand water droplet erosion
Bibliographic record
Abstract
Water droplet erosion (WDE) has received considerable attention in recent years. Different approaches have been proposed to understand WDE and find lasting solutions. Among them is understanding the interaction between the droplet impacts and the target surface, especially at the erosion initiation stage. For this reason, we studied the interactions between water droplets and different surface features to understand WDE. These surface features included flat smooth surfaces and grooved and porous samples. For the grooved samples, depths of 1.0 and 0.5 mm were studied and their WDE performance was evaluated. The 0.5 mm groove showed a longer incubation period than the flat reference sample. This work suggested that a thin water film is formed in the groove, which aids in dampening the impacts of subsequent water droplets. However, the maximum erosion rate is not affected by introducing these grooves. The WDE performance of the porous samples is better than that of the solid material. This is because the porous structure dissipates the impact energy of the water droplets. The simulation results were in agreement with the experimental observations in this work. Furthermore, the simulation showed that the water droplet impacting patterns on different surface features are attributed to the effect of radial and axial airflows.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".