The Effect of Thermal Contact Resistance at Porous-Solid Interfaces in Finned Metal Foam Heat Sinks
Bibliographic record
Abstract
A numerical study on the effect of thermal contact resistance and its impact on the performance of finned aluminum foam heat sinks has been conducted. Calculations are based on the solution of the volume-averaged mass, momentum, and energy equations under conditions of local thermal nonequilibrium using a finite-volume-based computational fluid dynamics code for conjugate fluid/porous/solid domains. Numerical results have been obtained for a wide range of contact resistances at the porous-solid interfaces, up to the limit of an effectively infinite resistance. As the contact resistance is increased to such high levels, the heat transfer is found to asymptote as conduction into the solid constituent of the foam is completely blocked. Even without conduction into the solid, a convective enhancement is obtained due to the presence of the foam material. It is reasoned that this is due to the thinning of the momentum boundary layers as a result of the presence of the porous material, which acts as a momentum sink. As a result of the thinner boundary layers, the flow speed near the finned surfaces and base is increased, which serves to increase the rate of convection from these surfaces. It is also found that for most reasonable interface materials, such as thermal epoxies, the impact of thermal contact resistance on the heat transfer performance in comparison to that for an ideal bond is small.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| 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".