Heat transfer mechanism in liquid-liquid slug flow inminichannels
Bibliographic record
Abstract
The capability of Taylor slugs to enhance heat transfer rate compared to that obtained from single-phase flow is welldocumented. This study numerically investigated the hydrodynamics characteristics and heat transfer mechanism in liquid-liquid Taylor flow. A novel analysis method was introduced by comparing fluid flow and heat transfer parameters distributed at the axial and radial planes of the channel. In order to investigate transport phenomena, the unit cell length and frequency of slug generation over a wide range of void/phase fractions were examined. The simulations showed that the viscosity difference between the phases is a critical parameter of the slug frequency; a higher amount increases the frequency much more. Conversely, a lower viscosity ratio between the phases allows water slugs to expand axially more. The higher temperature gradient and recirculation in the liquid plug region enhance the heat transfer rate leading to the highest cooling performance over the channel wall within a unit cell. The results also showed the significant importance of establishing shorter slugs, which not only improves the cooling performance in the slugs but also enhances the heat transfer rate in the liquid plug region. The developed numerical model was verified by the results found in the literature showing good agreement.
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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".