Investigation of Cooling Performance of a Swirl Microchannel Heat Sink by Numerical Simulation
Bibliographic record
Abstract
High heat fluxes have been created by the semiconductor devices due to the high power generation and shrank size. The large heat flux causes the circuit to exceed its allowable temperature and may experience both working efficiency loss and irreversible damage due to excess in their temperatures. In this paper, a swirl microchannel heat sink is designed to dissipate the large heat flux from the devices. The numerical simulation is carried out to investigate the cooling performance. Uniform heating boundary condition is applied and single phase water is selected as coolant. The present micro heat sink applies multiple swirl microchannels positioned in a circular flat plate to enhance the heat convection by creating the secondary flow at high Reynolds numbers. Copper is selected as the material of heat sink. The channel depth and width are fixed as 0.5 mm and 0.4 mm, respectively. The heat is injected into the system from the bottom of heat sink at the heat fluxes from 10 to 60 W/cm2. Flow is supplied from the top of micro heat sink through a jet hole with a diameter of 2 mm and enters swirl microchannels at the volume flow rates varying from 47 to 188 ml/min. The cooling performances of swirl microchannel heat sinks with different curvatures and channel numbers are evaluated based on the targets of low maximum temperature, temperature gradient and pressure drop.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".