Effects of Liquid Properties on Pressure Drop of Two-Phase Gas-Liquid Flows Through a Microchannel
Bibliographic record
Abstract
Adiabatic experiments were conducted to measure pressure drop for single-phase liquid and gas-liquid two-phase flows through a circular microchannel with an internal diameter of 100 μm. In order to study the effects of liquid properties on the pressure drop, aqueous solutions of ethanol with different mass concentrations (4.8, 9.5, 49 and 100 wt%) in distilled water and distilled water were used as the working liquid, while nitrogen gas was used for the gas phase. The surface tension of the working liquid ranged from 0.023 N/m (100 wt% ethanol) to 0.072 N/m (water), and viscosity from 0.9 mPa·s (water) to 3.4 mPa·s (49 wt% ethanol aqueous solution). For the single-phase flow experiments, the friction factor data were obtained for each working liquid used, over a Reynolds number range of 2 < Re < 800. For the two-phase flow experiments, pressure drop data were collected over 0.2 < jG < 7 m/s for the superficial gas velocity and 0.1 < jL < 1 m/s for the superficial liquid velocity. For single-phase flows, friction factor data were shown to be in reasonable agreement with conventional theory. Furthermore, early transition from laminar to turbulent flow was not observed over the present experimental flow conditions. For two-phase flows, Lockhart & Martinelli’s correlation was found to be capable of predicting the present pressure drop data irrespective of the working liquid tested, if an appropriate constant needed in the correlation is adopted.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".