A Comprehensive Review on Fluid Dynamics and Transport of Suspension/Liquid Droplets and Particles in High-Velocity Oxygen-Fuel (HVOF) Thermal Spray
Bibliographic record
Abstract
In thermal spraying processes, molten, semi-molten, or solid particles, which are sufficiently fast in a stream of gas, are deposited on a substrate. These particles can plastically deform while impacting on the substrate, which results in the formation of well-adhered and dense coatings. Clearly, particles in flight conditions, such as velocity, trajectory, temperature, and melting state, have enormous influence on the coating properties and should be well understood to control and improve the coating quality. The focus of this study is on the high velocity oxygen fuel (HVOF) spraying and high velocity suspension flame spraying (HVSFS) techniques, which are widely used in academia and industry to generate different types of coatings. Extensive numerical and experimental studies were carried out and are still in progress to estimate the particle in-flight behavior in thermal spray processes. In this review paper, the fundamental phenomena involved in the mentioned thermal spray techniques, such as shock diamonds, combustion, primary atomization, secondary atomization, etc., are discussed comprehensively. In addition, the basic aspects and emerging trends in simulation of thermal spray processes are reviewed. The numerical approaches such as Eulerian-Lagrangian and volume of fluid along with their advantages and disadvantages are explained in detail. Furthermore, this article provides a detailed review on simulation studies published to date.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".