Experimental Measurements and Numerical Computation of Nano Heat Transfer Enhancement Inside a Porous Material
Bibliographic record
Abstract
Abstract The rapid rate of improvement in electronic devices has led to an increased demand for effective cooling techniques. The purpose of this study is to investigate the heat transfer characteristics of an aluminum metallic foam for use with an Intel core i7 processor. The metal foams used have a porosity of 0.91 and different permeabilities ranging from 10 pores per inch (PPI) to 40 PPI. The flow rate at the entrance of the porous cavity varied from 0.22 USGPM to 0.1 USGPM. The fluid consists of water with aluminum nanoparticles having a concentration from 0.1% to 0.5%. The heat fluxes applied at the bottom of the porous test cell vary from 13.25 W/cm2 to 8.34 W/cm2. It has been observed that nanofluid and forced convection improves heat extraction. These observations lead to the conclusion that heat enhancement is possible with nanofluid and it is enhanced further in the presence of a high flow rate. However, it was detected experimentally, verified numerically, and agreed upon by different researchers that higher heat extraction is found for a nanofluid concentration of 0.2%. This observation is independent of the porous permeability or applied heat flux. It has also been shown that heat enhancement in the presence of nanofluid is evident, when experimental results were compared to water.
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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".