Rheological behaviour of oil and water emulsions and their flow characterization in horizontal pipes
Bibliographic record
Abstract
Abstract In this work, the rheological behaviour of oil and water emulsions and their flow characterization were studied using a Haake RS6000 rheometer and a lab scale flow loop. The rheological properties of the synthetic emulsions were investigated at oil volume fractions from 0.1–1.0 L/L and shear rates from 0.001–1000 s−1 at a system temperature of 20 °C. The emulsions flow, formed by the SMV static mixer installed before the test section, was observed in the 2‐m long horizontal pipes with 25 and 50 mm inner diameters at different mixture flow rates from 0.01–7.00 m3/h. A comparison between rheological measurements and laminar flow data shows that the values of the Sauter mean droplet diameters formed by the SMV static mixer are approximately one order of magnitude bigger than those measured by the rheometer. For oil‐in‐water emulsions with low oil content, the stress‐strain relationships obtained in the rheometer are more suitable for predicting the transport characteristics of the emulsions with a large pipe diameter than those with a small pipe diameter. In addition, the phase inversion points in the pipe flow are closer to those measured at high shear rates in the rheometer. Therefore, the apparent viscosity obtained by the rheology measurements at high shear rates could be introduced for accurate calculations of the mixture Reynolds number and the friction factor in emulsions flow.
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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 | 0.000 | 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".