Estimation of Internal Flow in the Nozzle for Cold Spray using the outer surface temperature
Bibliographic record
Abstract
Cold spray (CS) is a thermal spraying method in which solid particles are mixed with an inert gas, accelerated at supersonic speed through a Laval nozzle, and collide with a substrate to form coating. The particle adhesion rate in CS nozzle strongly depends on the gas velocity of the working gas. Therefore, it is important to understand the flow condition inside the CS nozzle which accelerated the gas flow. One of the most traditional methods to diagnose the internal flow is to measure static pressure through thin ports. Although this method can accurately estimate the internal flow, it is not practical to apply to the commercial CS nozzle. Therefore, there is a need for a non-destructive measurement technique that can easily monitor the internal flow of the nozzle. In this study, we focused on the fact that the temperature of the compressible fluid depends on the velocity, and investigated the Mach number estimation method using the outer surface temperature of the nozzle. In addition, the Mach number obtained from the estimation method and the static wall pressure in the nozzle was compared to clarify the validity of the method.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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".