A Novel Approach of Heat Rate Enhancement in Rectangular Channels with Thin Porous Layer at the Channel Walls
Bibliographic record
Abstract
Heat transfer enhancement is a topic of great interest nowadays due to its different applications in industries. A porous material also known as metallic foam plays a major role in heat enhancement at the expense of pressure drop. The flow in channels demonstrates the usefulness of this technology in heat extraction. In our current study, a porous strip attached to the walls of the channels is proposed as an alternative for heat enhancement. The thickness of the porous strip was varied for different Reynolds numbers. By maintaining a laminar regime and using water as a fluid, we determined an optimum thickness of porous material leading to the highest performance evaluation criterion. In our current study, with the aspect ratio being the porous strip thickness over the channel width, an aspect ratio of 0.2 is found to be the alternative. A 40% increase in heat enhancement is detected in the presence of a porous strip when compared to a clear channel case for a Reynolds number equal to 200, which improves further as the Reynolds number increases accordingly.
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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 | 0.000 |
| 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".