The Effect of Nozzle Shape and Configuration on Bubble Formation in a Liquid Cross Flow
Bibliographic record
Abstract
Gas injection into a liquid cross flow from a nozzle causes bubble formations which have potential applications in industry such as chemical plants, waste water treatment and bio- and nuclear-reactors. The purpose of this study is to experimentally investigate the effects of nozzle shape and configuration with respect to the liquid cross-flow direction, on the bubbly flow characteristics such as bubble formation, detached bubble size and frequency at different gas and liquid flow rates. The experiments were conducted in a Plexiglas two-dimensional rig using a high speed camera. High speed imaging and an image processing algorithm were used to track each individual bubble and to quantify the bubble growth as well as the detachment frequency and the bubble velocity. Back light shadowgraphy which utilizes a low intensity diffuse light source to illuminate the background was used to image bubbles. Nozzles were mounted in the test section which was designed such that the flow in this section has a two-dimensional profile. The results showed that the bubble size increases with an increase in GLR (gas to liquid flow rates ratio). Furthermore, the bubble formations and detached bubble size were strongly influenced by the nozzle shape and configuration.
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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 | 0.003 |
| 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".