Deformation and solidification process of a super–cooled droplet impacting on the substrate under plasma spraying conditions
Bibliographic record
Abstract
To date, many modelling efforts related to the deformation and solidication processes of a droplet impacting on the substrate under plasma spraying conditions have been reported. However, to the authors’ knowledge, no modelling effort has dealt with the super-cooling effects on the deformation and solidification processes, though much evidence of super-coolingeffects has been reported. In this paper, we will show the first results derived from our recent modelling efforts for thecase of Al2O3 droplets, which clearly show the strong effects of the super-cooling conditions on the deformation and solidification processes. For example, we predict a significant decrease in deformation degree — defined as the ratio of droplet to splat diameters — to less than 2.0, and also a faster solidification front velocity of up to 5 m/s. Although the small deformation degree is clearly caused by the larger value of viscosity under super-cooled conditions, therapid solidification is eventually caused by the super-cooling. The model did not predict a dendritic growth, but it clearly suggested that plasma sprayed particles are not all but may be actually in super-cooled state, and any modelling efforts should include the super-cooling effects.
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 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 | 0.000 |
| 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) | 0.002 | 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".