A Numerical Study of Dispersed Air Bubbles in a Hydrotransport Pipeline Flow
Bibliographic record
Abstract
Different flow pattern maps and theoretical models were employed to determine the flow velocity needed to provide the dispersed-bubble flow in a hydrotransport pipeline. Comparison and analysis of the results has been carried out. The maximum and minimum bubble sizes were determined by semi-experimental methods. A log-normal function was employed to describe the bubble size distribution. A model for the bubble size change in the turbulent pipe flow was applied to study the evolution of the overall bubble size distribution. This model takes into account the competing factors influencing the bubble size: 1) dissolution (turbulent diffusion) of air in the liquid, causing bubble shrinkage; 2) pressure drop along the pipeline, causing bubble growth. Numerical analysis shows that the bubble dissolution rate strongly depends on the initial air hold-up and initial bubble size. An increase of air hold-up leads to a fast decrease of the dissolution rate. At sufficient high air hold-ups, the dissolution effect becomes negligible and air bubble sizes are dominantly controlled by the pressure drop. Smaller bubbles have higher dissolution rates than larger ones. Compared with a pure liquid flow under the same flow conditions, the effect of air hold-up is stronger in the slurry flow because of the smaller volume occupied by the liquid.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".