Modeling ForcedConvection in Finned Metal Foam Heat Sinks
Bibliographic record
Abstract
Abstract A numerical study has been undertaken to explore the details of forced convection heat transfer in finned aluminum foam heat sinks. Calculations are made using a finite-volume computational fluid dynamics (CFD) code that solves for the flow and heat transfer in conjugate fluid/porous/solid domains. The results indicate that using unfinned blocks of porous aluminum results in low convective heat transfer due to the relatively low effective thermal conductivity of the porous aluminum. The addition of aluminum fins to the heat sink significantly enhances the heat transfer with only a moderate pressure drop penalty. The convective enhancement is maximized when thermal boundary layers between adjacent fins merge together and become nearly developed for much of the length of the heat sink. It is found that the heat transfer enhancement is due to increased heat entrainment into the aluminum foam by conduction. A model for the equivalent conductivity of the finned/foam heat sinks is developed using extended surface theory. This model is used to explain the heat transfer enhancement as an increase in equivalent conductivity of the device. The model is also shown to predict the heat transfer for various heat sink geometries based on a single CFD calculation to find the equivalent conductivity of the device. This model will find utility in characterizing heat sinks and in allowing for quick assessments of the effect of varying heat sink properties.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".