Impact of Micropillar Density Distribution on the Capillary Limit of Heat Pipes
Bibliographic record
Abstract
Abstract This paper shows the performance enhancement of heat pipes by tailoring the density distribution of micropillar wicks to minimize viscous pressure loss while maintaining sufficient capillary pumping. In a heat pipe, capillarity and permeability are linked, since small pores create higher capillary pumping while unfortunately inducing more pressure drop along the heat pipe. This pressure loss accumulates along the heat pipe, leading to a non-uniform pressure difference between the liquid and vapor. Therefore, we do not need a uniform capillary pressure to withstand this difference. This provides the opportunity to spatially tailor the wick structure, aiming for a high capillarity to pump the liquid, but a low permeability to induce less pressure loss. Our study offers a compromise between capillarity and permeability by designing the density distribution of the pillar wick structure. This density distribution, which was not studied before, will be shown to enhance the heat pipe performance. The theoretical models show that a tailored density distribution can enhance the heat pipe performance by a factor of 1.5. To support this result, ‘rate of rise’ measurements along a pillar array demonstrate that the liquid pressure loss in a tailored density array are less compared to a constant pillar density.
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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".