Heat and nanofluid transfer in baffled channels of different outlet models
Bibliographic record
Abstract
The paper is concerned with the effects of baffled obstacles on steady turbulent Al2O3-H2O nanofluid flow and heat transfer characteristics through channels in different outlet models. The first channel has an outlet as its entrance (case A). The second (case B), third (case C), and fourth (case D) channels have narrow, upper, lower, and central exits, with 45 per cent of their entrance, respectively. These effects are investigated with the help of CFD in a 2D model. The numerical data show improvements in the heat transfer rate of about 45. 071, 58.404, 82.413, and 92.433 per cent for cases A, B, C, and D compared to the smooth channel using the same solid volume fraction of Al2O3 nanoparticle, respectively. Among the most effective channels on heat transfer is case D, about 37.658, 21.356, and 9.348 per cent compared to cases A, B, and C, respectively for the maximum value of Reynolds number.
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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 | 0.001 | 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".