Carbon Foam: New Generation of Enhanced Surface Compact Recuperators for Gas Turbines
Bibliographic record
Abstract
The potential of porous carbon foam is explored in the context of compact recuperators for microturbine applications. Porous carbon foam has an open, interconnected pore structure and an extremely high solid-phase conductivity, which render the material a viable alternative in compact heat exchanger design. The material is also mechanically stable, non-corrosive and relatively inert to temperatures up to approximately 500°C, which make it particularly attractive for high-temperature non-oxydizing and moderate temperature oxidizing applications. Hydrodynamic and thermal engineering models are proposed based on recent work applied to air-water heat exchangers. The models are developed based on a unit-cube geometric model for carbon foam, a heat transfer model and well-established convective correlations that are extended to account for the effects of the carbon foam. The present calculations suggest that the use of carbon foam in a relatively simple configuration results in a significant reduction in thermal resistance accompanied by a rise in the hydrodynamic resistance. These preliminary results suggest that very compact heat transfer devices could be developed. With further investigation it is felt that the hydrodynamic resistance could be reduced while preserving the heat transfer performance resulting in very high-performance, compact heat transfer devices.
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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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".