Synergistic effect of active-passive methods using fins surface roughness and fluid flow for improving cooling performance of heat sink heat pipes
Bibliographic record
Abstract
The continued depreciation of electronic components presents severe challenges for thermal management. Regarding this issue, the current experimental study targets to propose and assess two passive cooling (using a heat sink and rough surface) and two active cooling (air and water cooling) for improving the cooling efficiency of aluminum heat sink heat pipes (HSHPs). In roughening process, the fins are chemically etched using a lab-made simple, cost-efficient, and environmental-friendly method to achieve better cooling performance synergistically. Scanning electron microscopy and atomic force microscopy characterizations are executed to investigate the heat sink surfaces’ micro/nano roughened structure. Current results and related comparative studies of the three cooling modules (typical, liquid-based, and liquid-based micro/nano roughened HSHPs) are presented as well, where effects of constant/intermittent heat fluxes (4000–12000 W/m2) and the volume flow rates of testing fluid on heat transfer characteristics, thermal resistance, and temperature behavior are disclosed. Based on findings, at a constant heat flux, roughening the HSHP fins led to an enhancement in cooling through fins and a reduction in cooling through the water. Moreover, it is found that the modified HSHP without testing fluid and with water at volume flow rates of 100 and 200 ml/min decreases the thermal resistance by 11.9, 13.7, and 3.6%, in order, compared with the typical HSHP.
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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".