Vapor bubble dynamics and heat transfer characteristics over the spherical surface during saturated pool boiling
Bibliographic record
Abstract
Abstract The present study deals with numerical investigations of the boiling phenomena over a spherical surface at different degrees of superheat (Δ T ), varying from 10 to 500 K. Various phenomena like vapor sliding, bubble formation, pinch‐off, induced vorticity have been illustrated for a deep understanding of the boiling process over a spherical surface. The effect of the degree of superheat on the bubble pinch‐off time and volume is also investigated. Further, reported the spatial observation of vapor sliding and retention over the surface with time scale, overall and average characteristics. The fast Fourier transform of a spaced average void fraction of liquid and Nusselt number showed the dominance of film boiling with respect to the degree of superheat. As the degree of superheat increases, the vapor generation rate also increases, which produces a more vapor–liquid interface. Further, with an increase in the degree of superheat, the vapor generation progression shifted from linear to nonlinear patterns. A sphere with Δ T = 500 K generated 32.59 times more vapor than a sphere with Δ T = 10 K. It is found that the vapor generation is dependent on the degree of superheating and exposed time for heating. Thus, a correlation and artificial neural network model have been developed to predict vapor generation during boiling over the spherical surface as a function of time and degree of superheat.
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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) | 0.000 | 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".