Hydrodynamic analysis of nanofluid’s convective heat transfer in channels with extended surfaces
Bibliographic record
Abstract
The effects of nanoparticles (NPs) on heat transfer in extended surface channels have been analyzed using a two-component (TC) model. The results show that unlike the single-component model, the TC model leads to more accurate predictions of the system’s heat transfer performance as a result of the direct influence of the NPs’ distribution on the hydrodynamics. It is found that the average Nusselt number varies non-monotonically with the block’s heights, and the trend is explained by the interplay between heat transfer mechanisms and the hydrodynamics. A similar non-monotonic trend observed in the case of the friction factor has been explained by the variations of the concentration- and temperature-dependent viscosity of the nanofluids. A guideline for an optimum design based on the combination of the variation of average Nusselt number and friction factor with respect to the geometrical parameters has also been presented.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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) | 0.000 | 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 teacher head, 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".