Flow and heat transfer of nanofluid in a channel partially filled with porous media considering turbulence effect in pores
Bibliographic record
Abstract
The flow and heat transfer of Al2O3–water nanofluid in a channel partially filled with porous media is investigated numerically. The turbulence effect in the porous media is taken under consideration in this article. A simple case is simulated first to evaluate the accuracy of the results in comparison with the available data. The turbulent kinetic energy profile is investigated at a flow cross section. The results show that the maximum turbulent kinetic energy occurs in the clear fluid region in the vicinity of the porous media region. The turbulent kinetic energy is a decreasing function of the porosity of the porous medium. The effect of porosity on the variation of turbulent kinetic energy decreases with the increase in the porosity of the porous medium. The turbulent kinetic energy in clear fluid and porous media regions decreases with the increase in nanofluid concentration from 0.01 to 0.03, and it increases with the increase in nanofluid concentration from 0.03 to 0.05. The temperature of the nanofluid increases with the increase in the nanofluid concentration and decrease in the porosity of porous media. It is shown that for this case, with the increase in nanofluid concentration and porosity of porous media, the skin friction coefficient increases and the Nusselt number decreases.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".