Numerical study on the influence of dispersed bubbles on liquid‐phase apparent viscosity in two‐dimensional parallel plate
Bibliographic record
Abstract
Bubbly liquid exists widely in industrial fields, so the detailed understanding of physical properties of bubbly liquid is significant for improving product quality and for strengthening industrial processes. In this paper, in order to understand the modulation mechanism of bubbles on the liquid‐phase apparent viscosity, based on the two‐dimensional parallel plate model, the effect of dispersed bubbles on the liquid‐phase apparent viscosity was deeply investigated with the volume of fluid (VOF) method combined with a dynamic mesh. The influence of the volume fraction, the capillary number, and the distance between bubbles on the liquid‐phase apparent viscosity was studied in detail for the dispersed bubbly liquid. The present studies show that both the capillary number and the volume fraction have a great effect on the liquid‐phase apparent viscosity; for the cases with the same volume fraction, the bubble injection causes the decrease of the relative viscosity of the liquid phase when the capillary number of bubbles is relatively large ( Ca > 0.5), and the larger the volume fraction is, the more sharply the relative viscosity of the liquid phase decreases. However, under the same volume fraction, the relative viscosity of the liquid phase increases when the capillary number of bubbles is relatively small ( Ca < 0.5), and the higher the volume fraction is, the more obviously the relative viscosity of the liquid phase increases. In addition, as the volume fraction increases, the bubble‐bubble interaction becomes very important, and the contribution of each bubble to the shear stress (i.e. the liquid apparent viscosity) decreases.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".