A numerical study of prediction method for terminal velocity of rising microbubbles in quiescent liquid by simplified rear stagnant cap model
Bibliographic record
Abstract
Abstract A simplified rear stagnant cap model is employed to analyze the effect of contaminants on terminal velocity of microbubbles rising in quiescent liquid. A dataset of the terminal velocity of microbubbles rising in liquids is collected to validate the numerical method and evaluate the Hadamard‐Rybczynski (H–R) correlation and Stokes' law. Both the experimental data and numerical results indicate the inescapable effects from inertia and contaminants. The numerical results show that a steep fall in surface tangential velocity ahead of the stagnant cap results in a sharp increase in local pressure and the increase of the drag acting on a rising contaminated bubble. The evaluation work proved the rationality of the Sadhal–Johnson correlation, which realizes a quantitative prediction of the drag coefficients for contaminated microbubbles. Inclusive of both the contamination and inertia effects, prediction correlations of the upper and lower thresholds for normalization in the Sadhal–Johnson correlation are replaced by the Schiller–Naumann correlation and Mei et al. correlation, extending its application range from < 1 to < 100, applicable of predicting terminal rising velocities of microbubbles with maximum diameter around hundreds of microns.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".