Effect of Substrate Properties on the Formation of Plasma-Sprayed Alumina Splats
Bibliographic record
Abstract
Abstract The effect of substrate characteristics on the formation of plasma-sprayed alumina splats was studied using both experiments and numerical simulation. Knowledge of the particle and substrate conditions is critical in understanding coating formation and in validating computational models. The size, velocity and temperature of the alumina particles prior to impact were measured using a particle in-flight diagnostic system. Experiments were performed on two substrate materials: stainless steel and glass. Substrate temperatures were varied in a range of 20-500°C and controlled with an electric heater. For each substrate material, a transition temperature was observed above which there was no fingering/splashing and the splats had a circular disk shape. A 3D computational model of free surface flows with heat transfer and solidification was used to simulate the impact of alumina particles in conditions given by the experiments. The splat shapes from numerical model were comparable to those of the experiments for hot stainless steel substrate. For a cold substrate, the numerical model did not show any fingering/splashing. In the experiments, however, we observed two types of splat shapes: intensive splashing with no central core and circular disk splat. Substrate surface contamination, not considered in the numerical model, was the probable cause of droplet splashing on the cold substrate.
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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".