Pipeline Flow Behavior of Water‐in‐Oil Emulsions with and without a Polymeric Additive in the Aqueous Phase
Bibliographic record
Abstract
Abstract New experimental results are presented on the pipeline flow behavior of water‐in‐oil (W/O) emulsions with and without a polymeric additive in the aqueous phase. The emulsions were prepared from three different oils of different viscosities (2.5 mPa s for EDM‐244, 6 mPa s for EDM‐Monarch, and 5.4 mPa s for Shell Pella, at 25 °C). The W/O emulsions prepared from EDM‐244 and EDM‐Monarch oils (without any polymeric additive in the dispersed aqueous phase) exhibited drag reduction behavior in turbulent flow. The turbulent friction factor data of the emulsions fell well below the Blasius equation. The W/O emulsions prepared from EDM‐244 oil exhibited stronger drag reduction as compared with the EDM‐Monarch oil. The W/O emulsions prepared from Shell Pella oil exhibited negligible drag reduction in turbulent flow and their friction factor data followed the Blasius equation. The Shell Pella emulsions were more stable than the EDM‐244 and EDM‐Monarch emulsions. When left unstirred, the EDM‐244 and EDM‐Monarch emulsions quickly coalesced into separate oil and water phases whereas the Shell Pella emulsions took a significantly longer time to phase separate. The Shell Pella oil emulsions were also milkier than the EDM emulsions. The addition of a polymer to the dispersed aqueous phase of the W/O emulsions had a significant effect on the turbulent drag reduction behavior.
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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".