Melting behavior of in-flight particles in supersonic plasma jets
Bibliographic record
Abstract
BackgroundNuclear high-temperature resistant ceramic materials have been widely used in nuclear energy, military, and aerospace fields in recent years owing to their excellent thermal insulation and high-temperature oxidation resistance.PurposeThis study aims to investigate the mass and heat transfer process between plasma fluid and flying particles in supersonic plasma spraying during the preparation of yttrium-stabilized zirconia thermal barrier coatings, so as to reveal the process parameters of flying particles.MethodsFirstly, the computational fluid dynamics (CFD) approach was employed to simulate the interaction between flying particles in the plasma spraying process. Then, a three-dimensional mathematical model of the plasma spraying flow field was established, and the jet characteristics of different spraying parameters in the de Laval nozzle and the melting and stress state of flying particles were analyzed by using this model. Furthermore, the online monitoring device Spray Watch 2i (Osier, Finland) was used to compare the online measurement of the velocity and temperature of flying particles obtained with the simulation results.ResultsThe comparison results show that relative errors are within 15%, verifying the simulation results effectively by experimental results. When the spraying power is reduced from 71 kW to 36 kW (i.e., reduced by 49.2%), the maximum velocity of the plasma jet is reduced by 8.5%, and the maximum temperature is reduced by 22.2%.ConclusionsA correlation between plasma spraying parameters, jet characteristics, and melting of flying particles is revealed in this study, providing theoretical guidance for the precise control of high-performance thermal insulation coating structures required for accident resistant fuel cladding in nuclear reactions.
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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 | 0.000 | 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".