The Influence of Particle Morphology on In-flight Particle Velocity in Cold Spray
Bibliographic record
Abstract
Abstract Cold spray is a relatively recent spray coating technology in which metal or alloy particles are plastically deformed by the kinetic energy of the particles accelerated in a supersonic gas flow through a convergent-divergent nozzle before hitting the substrate. The particle velocity at impact onto the substrate is a key factor in determining the characteristics of the cold spray deposit. Therefore, various studies have been carried out on particle acceleration with the aim of obtaining faster cold spray particle velocities. Mathematical modeling has also been carried out on spherical particle acceleration in a supersonic gas flow in a Laval nozzle. To understand better how a non-spherical particle behaves in a supersonic gas flow, experiments were carried out on the effect of morphology on particle acceleration in cold spray. Two types of powder morphology were used for the experiment, one was spherical and the other was angular and jagged. The particle size distributions were almost the same. In-flight particle velocities of the spherical and angular particles were measured with a DPV-2000. It was found that the particle morphology greatly influenced the in-flight particle velocity and deposit efficiency.
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 | 0.001 |
| 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) | 0.001 | 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".