Forced Convection Heat Transfer and Hydraulic Losses in Graphitic Foam
Bibliographic record
Abstract
Experiments and computations are presented to quantify the convective heat transfer and the hydraulic loss that is obtained by forcing water through blocks of graphitic foam (GF) heated from one side. Experiments have been conducted in a small-scale water tunnel instrumented to measure the pressure drop and the temperature rise of water passing through the foam and the base temperature and heat flux into the foam block. The experimental data were then used to calibrate a thermal non-equilibrium finite-volume model to facilitate comparisons between GF and aluminum foam. Comparisons of the pressure drop indicate that both normal and compressed aluminum foams are significantly more permeable than GF. Results of the heat transfer indicate that the maximum possible heat dissipation from a given surface is reached using very thin layers of aluminum foam due to the inability of the foam to entrain heat into its internal structure. In contrast, graphitic foam is able to entrain heat deep into the foam structure due to its high extended surface efficiency and thus much more heat can be transferred from a given surface area. The higher extended surface efficiency is mainly due to the combination of moderate porosity and higher solid-phase conductivity.
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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 | 0.000 | 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".