Forced Convection With Micro-Porous Channels and Nanofluid: Experimental and Numerical Study
Bibliographic record
Abstract
This paper will investigate the heat transfer enhancement potential of micro-porous channels and nanofluid concentrations. The test blocks are two and three channels, that have 10 and 20 PPI foam metal inserts. The working fluids used are nanofluid with 0.6% alumina and distilled water. There are three flow rates used for the experiment, 0.1, 0.2 and 0.3 USGPM. The maximum average Nusselt number is 135.5, thus having the best rate removal of thermal energy. The pressure drop is an important result because it indicates how much pumping power is required. A lower pressure drop requires less power, which reduces operating costs. The lowest pressure drop is 0.97. Another observation, the temperature distribution has optimal results for the cases with nanofluid with 0.6% alumina and three-channels at a flow rate of 0.3 USGPM. Finally, the experimental and numerical studies are in good agreement with an average relative error of 3.57%.
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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 | 0.001 | 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".