Numerical study of hydrodynamic and thermal behavior of Al2O3/Water nanofluid and Al2O3-Cu/Water hybrid nanofluid in a confined impinging slot jet using two-phase mixed model
Bibliographic record
Abstract
This study utilizes the two-phase mixture model to conduct a numeric study of Al2O3/Water and Al2O3-Cu/Water (hybrid) nanofluid's hydrodynamic and thermal behavior in a laminar confined impinging slot jet in 50 ≤ Re ≤ 300 and nanoparticles volume fractions (NVF) ranging from 0 - 2%. This study considers various aspect ratios (H/W), including 2, 4, and 6, to investiate the confining effects. This paper gives a comparative analysis of nanofluids and hybrid nanofluids in terms of the parameters concerning the flow: Reynolds and local Nusselt number (Nux), average Nusselt number (Nuavg), flow lines' contour, and temperature distribution under similar geometric conditions and Reynolds number. In comparison with nanofluids, the hybrid nanofluids have higher local Nusselt number on the entire target surface, this advantage of hybrid nanofluid attribute to higher thermal conductivity of them. The average Nusselt numbers of nanofluids and hybrid nanofluids plotted at different Refor various aspect ratios(H/W=2,4), and the effect of aspect ratio and momentum are explained. Furthermore, pumping power of both fluid analysed for all nanoparticles volume fraction (0 - 2%) at different Reynolds number. The result shows that pumping power of hybrid nanofluid is higher than base fluid and nanofluid, because the dynamic viscosity of hybrid nanofluid is higher than base fluid (water) and nanofluid. Besides; the study identified some correlations in the hybrid nanofluids regarding the stagnation point and the average Nusselt numbers. Presumably, these correlations are valid under certain conditions: 50≤ Re ≤ 300, 2 ≤ H/W≤ 6, and volume fracture (0 - 2%).
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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) | 0.001 | 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".