Entropy optimized assisting and opposing non-linear radiative flow of hybrid nanofluid
Bibliographic record
Abstract
A numerical treatment on flow and heat transfer of radiative hybrid nanofluid comprising of Al2O3 and Cu nanoparticles and water as base fluid past an isothermal stretched cylinder set in a porous medium is conducted. The Al2O3 - Cu/water hybrid nanofluid has higher thermal conductivity than single Al2O3 and Cu and better heat transfer efficiency with low concentration. Therefore, practical applications of hybrid nanofluids in heat transfer systems such as solar collectors, heat pipes, heat exchangers, mini channel heat sink, and others could have a significant impact for its better chemical stability, mechanical resistance, physical strength, and augmented thermal conductivity. Both assisting and opposing flows are taken into consideration. Entropy optimization analysis is explored elaborately. Having transformed into non dimensional form through use of similarity variables, governing equations are solved by bvp4c solver in Matlab software. The outcomes of the numerical solution are that inclusion of more and more porous matrix whittles down non-linear radiative flow of Al2O3 - Cu/water hybrid nanofluid, and growth of curvature parameter peters out drag coefficient and heat transfer rate under influence of assisting and opposing flows. Besides, entropy generation rate is significantly higher for Al2O3 - Cu/water hybrid nanofluid than individual Al2O3 - water nanofluid or Cu - water nanofluid in both assisting and opposing flows.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".