Free convective heat transfer efficiency in Al2O3–Cu/water hybrid nanofluid inside a rectotrapezoidal enclosure
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Bibliographic record
Abstract
Purpose This paper aims to investigate numerically the free convective heat transfer efficiency inside a rectotrapezoidal enclosure filled with Al2O3–Cu/water hybrid fluid. The bottom wall of the cavity is uniformly heated, the upper horizontal wall is insulated, and the remaining walls are considered cold. A new thermophysical relation determining the thermal conductivity of the hybrid nanofluid has been established, which produced results those match with experimental ones. Design/methodology/approach The governing partial differential equations are solved using the finite element method of Galerkin type. The simulated results in terms of streamlines, heat lines and isotherms are displayed for various values of the model parameters, which govern the flow. Findings The Nusselt number, friction factor and the thermal efficiency index are also determined for the pertinent parameters varying different ratios of the hybrid nanoparticles. The simulated results showed that thermal buoyancy significantly controls the heat transfer, friction factor and thermal efficiency index. The highest thermal efficiency is obtained for the lowest Rayleigh number. Practical implications This theoretical study is significantly relevant to the applications of the hybrid nanofluids electronic devices cooled by fans, manufacturing process, renewable energies, nuclear reactors, electronic cooling, lubrication, refrigeration, combustion, medicine, thermal storage, etc. Originality/value The results showed that nanoparticle loading intensified the rate of heat transfer and thermal efficiency index at the expense of the higher friction factor or higher pumping power. The results further show that the heat transmission in Al2O3–Cu/water hybrid nanofluid at a fixed value of intensified $\phi_{hnf}$ compared to the Al2O3/water nanofluid when an amount of higher conductivity nanoparticles (Cu) added to it. Besides, the rate of heat transfer in Cu/water nanofluid declines when the lower thermal conductivity Al2O3 nanoparticles are added to the mixture.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it