Thermal performance of finned aluminum heat sink filled with ERG aluminum foam: Experimental and numerical approach
Bibliographic record
Abstract
The rapid improvements in electronic devices have led to a high demand for effective cooling techniques. The purpose of this study was to investigate the heat transfer characteristics and performance of different aluminum heat sinks filled with aluminum foam for an Intel core i7 processor. The aluminum foam heat sinks were subjected to water flow covering the non-Darcy flow regime (300-600 Reynolds numbers). The bottom side of the heat sinks was heated with a heat flux between 8.5 and 13.8 W/cm2. Three different heat sinks were examined in this study. Models A, B, and C contained two, three and four channels, respectively. Each channel gap was filled with ERG aluminum foam. The distributions of the local surface temperature and the local Nusselt number were measured for each heat sink design. The experimental data were compared with the numerical results. The average Nusselt number was obtained for the range of Reynolds numbers, and an empirical correlation of the average Nusselt number as a function of the Reynolds number was derived for each heat sink. The pressure drop across the characteristics of each heat sink design was measured. The thermal performance of each aluminum foam heat sink was evaluated based on the average Nusselt number and the required pumping power. The experimental results revealed that model B achieved the highest average Nusselt number compared with models A and C. However, model C had the highest surface to volume ratio; the thermal boundary layers, which are formed on adjacent fin surfaces inside the aluminum foam, interface with each other causing a reduction in the overall heat transfer. The numerical results were in good agreement with experimental data of local Nusselt number and local temperature with maximum relative errors of 2% and 1%, respectively.
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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".