Forced Convection of Al2O3–Cu, TiO2–SiO2, FWCNT–Fe3O4, and ND–Fe3O4 Hybrid Nanofluid in Porous Media
Bibliographic record
Abstract
Adding nanoparticles to fluid has led to a new class of fluids named as nanofluids. Different concentrations and its effective cooling have attracted many engineering applications to test this new fluid. Lately, important heat enhancement has been observed by dispersing two distinct nanoparticles in the regular fluid. This type of hybrid nanofluid has led researchers to study its effectiveness in the cooling process. Here, we experimentally studied the forced convection of Al2O3–Cu hybrid nanofluid in porous media at a constant flow rate and heating condition. The numerical code after being calibrated with the experimental results is used to predict the effectiveness in cooling by using a set of hybrid fluid of TiO2–SiO2, MWCNT–Fe3O4, and ND–Fe3O4 at different concentrations. In the experiment, we used water and a water–ethylene glycol mixture as base fluids. The results revealed that the hybrid fluid contributed to heat enhancement levied increased pumping power. However, the index of efficiency, obtained by combining the Nusselt number and pressure drop, indicated that the best hybrid fluid for such an application is ND–Fe3O4 in the water–ethylene glycol mixture.
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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) | 0.000 | 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".