Experimental investigation of bubble growth and detachment in stagnant liquid column using image – based analysis
Bibliographic record
Abstract
An experimental study has been carried out to characterize bubble formation, growth, and detachment mechanisms in a stagnant liquid column. Both bubble frequency and bubble detachment size were measured in different gas flow rates, injector diameters and orientations, submergence height, and liquid properties. Experiments were performed for air injection flow rate ranges between 200 mlph and 1200 mlph using needle diameters of 1.6, 1.19, 1.07, and 0.84 mm submerged in liquids with viscosities of 0.001, 0.1, 0.35, and 1 Pa.s. The data for bubble formation was obtained using a high-speed imaging technique. The results show that the bubble diameter at the departure increases as the needle diameter, liquid viscosity, and gas flow rate increase. In addition, the decrease in the submergence height results in a larger bubble at the departure. In order to analyze the changes in bubble detachment characteristics, a force modelling on a growing bubble was proposed. The experimental data were utilized for training a feed-forward back propagation neural network system to estimate the bubble detachment diameter. They were also used to propose a correlation to predict bubble diameter at the departure. The proposed correlation is found to be in the range of ± 8% of the obtained experimental data.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".