Effect of thermal boundary conditions on heat transfer performance of liquid–liquid Taylor flow through a microchannel with obstruction
Bibliographic record
Abstract
Abstract Two‐phase Taylor flow through microchannels has attracted the attention of many researchers because of its enhanced heat transfer characteristics. The heat transfer rate of two‐phase flow is higher than the basic primary fluid flow through the same microchannel. This higher heat transfer rate is further improved by droplet manipulation techniques along with various thermal boundary conditions, which is the aim of the present work. In this novel work, the numerical investigation was carried out on liquid–liquid Taylor flow and heat transfer characteristics through a 2D rectangular microchannel with an obstruction in the path. The effect of capillary number, size, and position of the obstruction on heat transfer behaviour of Taylor flow was also analyzed. The height and length of the microchannel were taken as 100 and 3000 μm, respectively. Water and mineral oil were taken as working fluids. Results show that the Nusselt number of Taylor flow with obstruction increases by 76% compared to single‐phase flow and significantly increases over the Taylor flow without obstruction. Further, the study explored the effect of modulated wall temperature on Taylor flow heat transfer for optimum parameters of capillary number, size, and position of the obstruction and an improvement of 290% was achieved in heat transfer compared to that of single‐phase flows.
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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 | 0.001 |
| 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".