Bubble formation regimes during gas injection into a liquid cross flow in a conduit
Bibliographic record
Abstract
Abstract A study was performed to characterize the different bubble formation regimes that occur during the process of gas jet injection into a liquid cross flow in a conduit. Air was injected perpendicularly into a turbulent, fully developed water flow circulating through a 12.7 mm square channel. Three different gas injectors, with diameters of 0.27 mm, 0.52 mm, and 1.59 mm were used. The bulk water velocity values ranged between 1.1 and 4.3 m/s. The effects that the gas injection velocity, liquid mean velocity, and injection gas injection diameter have on the process of bubble generation were investigated. A high‐speed visualization technique was used to determine the regimes near the gas inlet region. Four distinct regimes were identified: Single Bubbling (SB), Pulse (P), Elongated Jetting (EJ), and Atomizing Jetting (AJ). It was observed that the shift between regimes occurs gradually, producing the need to identify transitional regions: SBP and PTJ. Sets of independent dimensionless variables were used to categorize the proposed regimes using bubble formation maps. It was determined that the injection diameter plays a primary role in jet formation: as the injection diameter increased, the observable number of regimes decreased, indicating a more stable and continuous process of bubble generation. Empirical correlations that delimit the boundaries between ordered and chaotic bubble generation are presented.
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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 | 0.000 |
| 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 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".