Ferrofluidic plug flow heat transfer enhancement
Bibliographic record
Abstract
Overheating of power electronic devices has become a significant issue due to their continued miniaturization and increased heat flux that needs to be dissipated. Microchannel heat sinks utilising two-phase flow are capable of very high heat transfer rates and represent a possible means of cooling such devices. In this paper, we focus on two-phase liquid-liquid plug flow using water-based ferrofluid (magnetic nanofluid) plugs as the dispersed phase and silicone oil as the continuous phase. An external magnetic field was applied to generate enhanced mixing of the microfluidic flow. We show that material properties of the ferrofluid plug influence heat transfer properties of the microfluidic flow, and demonstrate that cooling performance is further enhanced by the application of an external magnetic field, which induces mixing. We also show that microchannel heat transfer using a ferrofluid is superior to that using de-ionized water as the dispersed phase for two-phase liquid-liquid plug flow.
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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) | 0.002 | 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".