Heat Transfer Coefficient of an Under-Expanding Cold Spray Air Jet on a Flat Substrate
Bibliographic record
Abstract
A two-dimensional heat conduction model was developed to determine the transient temperature distribution within a flat substrate that was exposed to the impingement of a cold spray hot air jet during the deposition process.The credibility of employing average heat transfer coefficient in mathematical modelling and prediction of the surface temperature profile of a substrate was studied.Moreover, the condition under which the effect of the presence of the in-flight particles on the heat transfer coefficient of the underexpanding air jet can be neglected was investigated.In this regard, a cold spray unit was used to generate a supersonic air jet.A twodimensional heat conduction model was developed and solved by using Green's functions to determine the temperature distribution within the substrate.By applying a surface integral to the analytically-estimated spatially-varying heat transfer coefficient of an underexpanding cold spray air jet, the average heat transfer coefficient was determined.Both the average and spatially-varying heat transfer coefficients were used separately in the model to predict the transient surface temperature profile of the substrate.It was shown that when the Stokes number of the particles is sufficiently small, the effect of the presence of the particles on the heat transfer coefficient of the impinging dilute air-particle jet can be neglected.It was further found that the surface temperature that was predicted by using the average heat transfer coefficient, unlike the spatially-varying heat transfer coefficient, produced large errors, especially at higher distances from the stagnation point of the air jet on the substrate surface.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".