Cold Spray Nozzle Design and Performance Evaluation using Particle Image Velocimetry (PIV)
Bibliographic record
Abstract
Abstract Based on the principles of cold spray, Cold Gas Dynamic Manufacturing (CGDM) is a high-velocity metal spraying process capable of the high-rate deposition of dissimilar materials under cold conditions. However, unlike many cold-spraying techniques that have focussed primarily on surface coatings, the CGDM process has been tailored for solid free-form fabrication. Central to the needs of the process is the ability to accelerate the feedstock powder – through entrainment into a high-velocity gas flow – to speeds in excess of its critical deposition velocity. The critical factor here is not the gas velocity but rather the ultimate particle velocity; and it is the drag force experienced by the individual powder particles that affect their velocity. This paper documents the methods used to predict, model and validate nozzle performance – in terms of particle acceleration and ultimate velocity – via Computational Fluid Dynamics (CFD) and Particle Image Velocimetry (PIV). Results are presented and discussed pertaining to the effects of nozzle type (de Laval, MLN etc.) and geometry, as well as gas type (Helium and Nitrogen), composition and temperature. Abstract only; no full-text paper available.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".