Numerical Study of the Effects of Twin-Fluid Atomization on the Suspension Plasma Spraying Process
Bibliographic record
Abstract
Suspension plasma spraying (SPS) is an effective technique to enhance the quality of the thermal barrier, wear-resistant, corrosion-resistant, and superhydrophobic coatings. To create the suspension in the SPS technique, nano and sub-micron solid particles are added to a base liquid (typically water or ethanol). Subsequently, by using either a mechanical injection system with a plain orifice or a twin-fluid atomizer (e.g., air-blast or effervescent), the suspension is injected into the high-velocity high-temperature plasma flow. In the present work, we simulate the interactions between the air-blast suspension spray and the plasma crossflow by using a three-dimensional two-way coupled Eulerian–Lagrangian model. Here, the suspension consists of ethanol (85 wt.%) and nickel (15 wt.%). Furthermore, at the standoff distance of 40 mm, a flat substrate is placed. To model the turbulence and the droplet breakup, Reynolds Stress Model (RSM) and Kelvin-Helmholtz Rayleigh-Taylor breakup model are used, respectively. Tracking of the fine particles is continued after suspension’s fragmentation and evaporation, until their deposition on the substrate. In addition, the effects of several parameters such as suspension mass flow rate, spray angle, and injector location on the in-flight behavior of droplets/particles as well as the particle velocity and temperature upon impact are investigated. It is shown that the injector location and the spray angle have a significant influence on the droplet/particle in-flight behavior. If the injector is far from the plasma or the spray angle is too wide, the particle temperature and velocity upon impact decrease considerably.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".