Pressure Drop of Liquid-Liquid Taylor Flow in Mini-Scale Tubing
Bibliographic record
Abstract
In this paper, the pressure drop of liquid-liquid segmented flow in small-scale tubing is investigated with experimental and analytical methods. A theoretical model is developed for describing the total pressure drop as a function of slug length and Capillary number. The experiments are conducted with low Reynolds number flows in horizontal, straight mini-scale tubes. A segmented (Taylor) flow is created using several low viscosity silicone oils (1, 3, 5 cSt) and water with a wide range of flow rates. The experimental setup allows the independent variation of liquid slug lengths. The liquids are injected into the mini-scale tubes at a variable (pulsed) flow rate for one liquid, and a constant flow rate for another liquid. The variation of liquid types and flow rates causes numerous combinations of Prandtl, Reynolds, and Capillary numbers to be tested. The theoretical and experimental data is presented in terms of the dimensionless groups fRe or ΔP* and Le* to predict pressure drop in liquid-liquid Taylor flow. The new experimental data agrees well with the new theoretical model of Taylor flow in miniscale tubes. The results of this paper indicate the pressure drop for Taylor flow is higher than in single-phase flow, likely due to the interfacial effects in liquid slugs.
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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 | 0.002 |
| 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 | 0.001 |
| 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".