Experimental Study and Numerical Investigation of the Phenomena Occurring During Long Duration Cold Spray Deposition
Bibliographic record
Abstract
Low Pressure Cold Spray process is a relatively new additive technique allowing to create high quality metallic coatings, through the high velocity spraying of particles, on both metallic and non-metallic substrates. The adhesion mechanism of the particles is, to date, not fully understood and the phenomena occurring during long depositions (which are the more interesting for industrial processes) are not known and studied in details. Aiming to fulfill this lack of knowledge, the phenomena occurring during long deposition were studied through both careful experimental evaluations and numerical approach. Particular attention was paid at the working conditions of the De Laval nozzle system which accelerates the particles, a computational fluid dynamics model focusing on both geometrical features and spray behavior was proposed. Micron-sized aluminum powders and compressed air as carrier gas were used in this experimentation, numerical simulations are performed to predict the gas flow regime. It has been found that the deposition efficiency tends to decrease due to the critical phenomena occur into the spray nozzle during long time depositions. The sprayed particles tend to stick to the walls of the nozzle and, moreover, shock waves occur inside the nozzle further promoting the particles stick phenomena, making the system inefficient.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 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".