Research of numerical simulation for HVAF thermal spraying process on rotational roll
Bibliographic record
Abstract
Abstract HVAF thermal spraying has the characteristics of low spraying temperature, high coating density, and strong corrosion resistance. It is widely used in the aerospace, iron–steel metallurgy, national defense and military industry, petrochemical industry, and other fields for manufacturing protection and repair strengthening, which has achieved significant economic benefits. In this study, a numerical model of HVAF thermal spraying on a circular roll was established by the computational fluid dynamics method. The characteristics of the spraying flame, evolution of the gas composition mass fraction, and influence of the spraying parameters on particle flight behaviors were calculated and revealed. Based on the dynamic mesh method, the effect of roll speed on the spraying flame characteristics and particle flight behaviors was analyzed. Calculations show that the spraying flame is extruded at the Laval nozzle and the speed rapidly increases to 805 m/s, which increases to a supersonic speed through the barrel. The flame flow rises rapidly reaching the surface of the roll, which is 780 m/s. The highest temperature is in the combustion chamber, and the flame temperature of the airshed is a damped vibration. The flame covers the surface of the roll to preheat it, and the flame temperature there decreases layer by layer from the inside to outside. The particle diameter significantly effects the powder flight behavior. The flame velocity increases with the barrel length increasing. The flame temperature up to the peak when the barrel length is 190 mm. As the rotation speed of the roll increases, the temperature, velocity, and pressure of the flame flow on the roll surface change in a certain extent. The particle spatter will be increased with the rotational speed increasing of the roll, which little affects the particle temperature.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".