Bibliographic record
Abstract
The effect of particle-gas and particle-particle interactions in a cold spray process is studied when the particle loading is high. To examine the effect of the presence of a dense particulate flow on the supersonic gas, an Eulerian-Eulerian approach is used. It is found that when the volume fraction of the injected particles is increased, the turbulence of the gas phase will be augmented by the motion of particles and consequently, the shape, the strength, and the location of the compression and expansion waves will be altered. Shock-particle interactions are demonstrated for various volume fractions. Another important parameter, which will affect the spraying deposition efficiency, is the substrate stand-off distance. It is found that the stagnation pressure alternates for different stand-off distances because of the formation of compression and expansion waves outside the nozzle exit. The particle normal velocity on impact is a strong function of the stagnation pressure on the substrate as particles must pierce through the bow shock formed on that region. The effect of the particle size and number density are also studied for different loading conditions. It is found that small and large particles behave differently as they pass through shock diamonds and the bow shock, i.e., in the case of very small particles, as the loading increases, the impact velocity increases, while, for the large particles, the trend is reversed.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".