Mixed Convection of Hybrid Nanofluids in an Annulus
Bibliographic record
Abstract
In this study, mixed convection in an annulus formed by two horizontal isothermal cylinder surfaces and filled with hybrid nanofluids was examined with Galerkin weighted residual finite element method. The outer cylinder is rotating and inner cylinder is stationary. Influence of Rayleigh number, angular rotational speed of the outer cylinder, eccentricity of the inner cylinder, solid volume fractions of different nanoparticles (alumina, copper, hybrid particles between 0 and 0.02) on the fluid flow and heat transfer characteristics were analyzed. It was observed that average heat transfer enhances with Rayleigh number, solid volume fractions of nanoparticles and eccentricity ratio and reduces as the angular rotational speed of the outer cylinder increases. Adding nanoparticles was found to be advantageous for lower values of Rayleigh number and higher values of angular rotational speed. At the highest volume fraction of Cu nanoparticles, average Nusselt number increases by 31.75 % when the inner cylinder center moves in +y direction. Nanofluid with hybrid nanoparticles gives heat transfer rates which are higher than that of with alumina and lower than that of with copper nanoparticles for the same volume fraction.
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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".