The effect of gas-injector location on bubble formation in liquid cross flow
Bibliographic record
Abstract
Liquid flows incorporating small-size bubbles play a vital role in many industrial applications. In this work, an experimental investigation is conducted on bubble formation during gas injection from a microtube into the channel of a downward liquid cross flow. The tip of the air injector has been located at the wall (wall orifice) and also at several locations from the wall to channel centerline (nozzle injection). The size, shape, and velocity of the bubbles along with liquid velocity field are measured using a shadow-particle image velocimetry/particle tracking velocimetry system. The process of bubble formation for the wall orifice and the nozzle injection configurations is physically explained. The effect of variation in water and air flow rates on the observed phenomena is also investigated by considering water average velocities of 0.46, 0.65, and 0.83 m/s and also air average velocities of 1.32, 1.97, 2.63, and 3.29 m/s. It was observed that shifting the air injector tip toward the center of the channel resulted in the coalescence of some of the preliminary bubbles and the formation of larger bubbles termed secondary and multiple bubbles. Increase in air flow rate and reduction in water flow rate also intensify the rate of bubble coalescence. A correlation-based model is also suggested to overcome the shortcoming of the available models in the literature which are developed to only estimate the size of the preliminary bubbles. The model predicts the percent of the preliminary, secondary, and multiple bubbles along with the average size of secondary and multiple bubbles as a function of nozzle position within a cross flow.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".