Experimental and Numerical Investigations of Bubble Dynamics in Porous and Non-Porous Media
Bibliographic record
Abstract
In this study an experimental work is conducted to investigate the shape and speed of an air bubble in a pipe filled with different viscous fluids and porous media. The experimental results are also compared with the Computational Fluid Dynamics (CFD) simulation. Multiphase flows are complex due to the infinitely deformable nature of interface in gas/liquid flows. If one of the phases is gas acts as dispersed phase in the form of bubble, then the complexity will arise from the non-uniform distribution of bubbles in the pipe cross-section and axial distance. Inclusion of different viscous fluids simulating the industrial scale hydrocarbon properties brings added challenge in understating the bubble rise, coalescence and breakup dynamics. Moreover, bubble rise and change of shape of bubble in porous media will bring additional complexity in the flow dynamics. The pipe used in the experiment and CFD was 11.6 cm ID and a length of 100 cm. Three situations were tested: i) an air bubble rising in stagnant water, ii) an air bubble rising in moving water, and iii) an air bubble rising in a stagnant water but filled with porous media with porosity of 27%. Preliminary CFD results indicate that an air bubble has an average velocity of 0.2468 m/s and 0.2524 m/s in stagnant water and moving water, respectively, which is very close to experimental results.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".