Development of 3D Multicomponent Model for Cold Spray Process Using Nitrogen and Air
Bibliographic record
Abstract
Cold spray is a unique coating technology that allows for solid state deposition of particles under atmospheric pressure. In this paper, a three dimensional, Computational Fluid Dynamics (CFD) multicomponent model is developed to estimate cold spray gas conditions involving both nitrogen and air. Calibration of the model followed by validation is accomplished by considering the thermal history of substrate exposed to cold spray supersonic jet. The developed holistic multicomponent model is effective in determining the state of gas and particles from injection point to the substrate surface with the advantage of optimizing very rapid cold spray deposition in nanoseconds. The validation of k-ε type CFD multicomponent model is done by using the temperature measured for a titanium substrate exposed to cold spray nitrogen at 800 °C and 3 MPa. Heat transfer and radiation are considered for the de Laval nozzle used in cold spray experiments. The calibrated multicomponent model has successfully estimated the state of propellant gas for the chosen high pressure and high temperature cold spray conditions. Moreover, the multicomponent model predictions are in good agreement with a previous holistic three dimensional cold spray model in which only nitrogen was used as the surrounding as well as the propellant gas.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".