Different nanofluids effect on bubble characteristics at the isothermal bubble column
Bibliographic record
Abstract
Abstract In this study, the effect of different nanofluids such as SiO 2 , Fe 3 O 4 , and Al 2 O 3 on bubble characteristics is studied experimentally. The nanoparticles concentration for the SiO 2 nanofluid is 0.05 wt% and for other nanofluids is 0.005 wt%. Bubbles are formed by injection of air at a constant gas flow rate (between 600‐1200 mL/h) into a stagnant isothermal liquid column. Experimental data of formation, growth, and detachment of the air bubbles were recorded by a high‐speed digital camera (1200 fps), and the image processing method was used to analyze the bubble characteristics. In the present study, bubble characteristics such as diameter, size, aspect ratio, and detachment frequency were studied in four different liquids. The results show that a bubble has the biggest size in the pure water and adding nanoparticles to the pure water decreases bubble size. Also, the variation of detachment frequency has an inverse relation with the bubble size behaviour. Between different nanoparticles, Fe 3 O 4 has maximum and SiO 2 has minimum effect on bubble features. By adding Fe 3 O 4 to pure water , bubble diameter decreases nearly 7%‐8% and bubble detachment frequency increases nearly 15%‐20%. In order to analyze the variation of the bubble characteristics, a force modelling on bubbles during the growth was proposed. Moreover, a correlation has been proposed for the prediction of bubble diameter at detachment time in three different nanofluids and in the pure water. The mean absolute error of this correlation is 2.65% and has a good agreement with experimental results.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.002 | 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 teacher head, 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".