Experimental investigation of liquid–liquid flow in an inclined pipe using dimensionless numbers
Bibliographic record
Abstract
Abstract Two‐phase flow is a common phenomenon in the energy industry, where flow patterns significantly affect heat transfer and pressure drop in different systems. However, there is no unique or comparable flow map because of its dependency on dimensional parameters. Therefore, an analysis using dimensionless numbers makes the results comprehensive. To do so, a series of liquid–liquid flow experiments (1296 experiments) were conducted in a transparent pipe at the different velocities of the phases. The flow patterns were captured using a high‐speed camera. The experiments were performed at eight different inclinations within the range of −20 to +20 degrees. Six flow patterns are observed at different inclinations; stratified flow with mixing at the interface (STMI), dispersion of water in oil (Dw/o), dispersion of oil in water (Do/w), dual continuous (DC), slug, and wavy stratified (WST), where the first five flow patterns are presented in the upward flow and the two last flow patterns disappear in some of the downward flow. The pattern of boundaries for each flow pattern in the upward flow shows dependency on inclination, while in the downward flow condition, a rather general format can be applied to most of the patterns. The analysis illustrates that gravity and buoyancy forces are the dominating forces in the system compared to other forces, such as viscous, inertia, and interfacial tension, which are due to the inclination of the pipe.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".