Experimental investigation on dynamic behaviour of bubbles emerging from micro‐capillary orifice in a flow channel
Bibliographic record
Abstract
Abstract The flow phenomenon of liquid with bubbles is widespread in various industrial fields, which determines the mass transfer characteristics of the equipment. In this work, the dynamic behaviour of bubbles emerging from micro‐capillary orifice in a flow channel was studied by a visualization experiment, while the effects of gas flow rate and liquid flow rate on these processes of bubble growth, departure, and inrush were explored. The experimental results showed that one bubble formation cycle can be divided into three stages: Waiting, departure, and inrush, as well as the dynamic behaviour of bubble emerging from micro‐capillary orifice in a flow channel, were significantly affected by gas flow rate and liquid flow rate. At a higher gas flow rate, the growth time and the departure time were shorter, as well as the departure volume of the leading bubble and the inrush volume of the trailing bubble were smaller, while the transverse longitudinal ratio fluctuated more violently, and the swing amplitude of the bubble centroid was greater. With an increasing liquid flow rate, the growth time, the departure time, and the inrush time shortened, while the departure volume of the leading bubble decreased and the fluctuation of the bubble centroid weakened. These findings are conducive to improving the performance of the equipment by optimizing the design of the aerator to regulate the dynamic behaviour of bubbles.
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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".