High Efficiency Low Profile Heat Sink for Air Cooling of Microelectronics
Bibliographic record
Abstract
Cooling of electronics has received considerable attention recently. Simplicity and easy maintenance make direct air cooling a most attractive approach in cooling of electronics. Various heat sinks are often mounted to the microelectronic components in order to enhance thermal performance so that the components can be maintained below the respective temperature limits. The high thermal efficiency low profile cell-fins heat sink is developed to meet the need of high power electronics in the limited space between printed circuit boards often occurred in any microelectronic equipment. For the purpose of comparison, convectional heat sinks, including extrusion fins and plain fins are included in the evaluation. A CFD analysis is performed to characterize the performance of the individual heat sinks. A copper heat sink is mounted to a component on the printed circuit board (PCB) which is cooled by the air with the velocities at 1.5, 2.5, 3.5, and 5.5 m/sec, respectively. In order to simulate the real board condition, the flow by-pass which is an important phenomenon to the performance of the heat sink is included in the analysis. The purpose of this study is to evaluate the thermal performance of the individual heat sinks, with special interests in this newly developed cell fin heat sink at various flow velocities. In addition, the effects of the flow by-pass as well as heat sink flow leakage on the heat sink performance will also be examined in details.
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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 | 0.001 |
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".