The impact of heterogeneous pin based micro-structures on flow dynamics and heat transfer in micro-scale heat exchangers
Bibliographic record
Abstract
Overheating is the most important limiting factor for efficient performance of miniature electronic devices. Porous microfluidic systems are recently introduced as a promising remedy to this problem. Increasing the heat removal using porous microfluidic systems comes at the cost of increased hydrodynamic friction in the device. In this study, we focus on the flow dynamics in microchannels with embedded heterogeneous porous structures to identify effective parameters to make porous patterns with less friction while maintaining a high heat transfer rate. The heterogeneous porous structures are defined using columns of pins with different pin sizes. We analyze the flow dynamics and heat transfer using quantitative and qualitative flow patterns, energy distribution, and particle tracking analyses. We find that the structure of the porous medium plays an important role in the hydrodynamic flow distribution and as a result on the overall heat transfer characteristics. While higher heat transfer rates in homogeneous porous media are proportional to higher friction, heterogeneous porous media revealed more complex flow dynamics. It was shown that an optimized distribution of the pins in the microchannel can lead to the systems where the heat transfer increases and, at the same time, the frictions decrease. We show that the columns at either end of the porous medium are the ones that affect flow dynamics and heat transfer the most.
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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) | 0.000 | 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".