Effect of Using Liquid Feedstock in a High Pressure Cold Spray Nozzle
Bibliographic record
Abstract
Abstract This study investigates the effect of water injection in the high pressure chamber of a cold spray nozzle. A de Laval nozzle geometry with constant back pressure and temperature is modeled numerically using Reynolds Stress Model coupled equations. Water spray with a droplet size of 10 – 100 μm is modeled using both uniform and Rosin-Rammler size distributions. The two-phase flow of gas-liquid is modeled using an unsteady discrete phase mass source with two-way coupling with the main gas flow. Upon injection, the droplets in the water spray evaporate while travelling through the nozzle due to momentum and energy exchange with the gas flow. The evaporation behavior in presence of water content is modeled and a correlation between the initial diameter and the diameter just before the throat is obtained. As a result, the proper droplet size distribution with a fully evaporative spray can be used as a carrier of nano-particles in cold spray nozzles. Having the results, guide us to substitute the un-evaporated part of the droplet with an equal diameter agglomerate of nano particles and find a minimum fraction of nano particles suspended in the liquid which guarantees fully evaporative liquid spray injection.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".