Darcy–Forchheimer dynamics of hybrid nanofluid due to a porous Riga surface capitalizing Cattaneo–Christov theory
Bibliographic record
Abstract
Due to superior heat transport offered by hybrid nanofluids compared to traditional nanofluids, an investigation on the dynamics of a hybrid nanofluid consisting of (Fe3O4+Al2O3)/water over a porous Riga stretching surface using the double-diffusion theory of Cattaneo–Christov is investigated. The hybrid mixture (Fe3O4+Al2O3) is placed in a Darcy–Forchheimer-porous-medium, and the significance of Brownian and thermo diffusions are also taken into account. The thermophysical attributes of solid particles and base fluid are used to model the problem along with the basic transport equations of fluid dynamics. Similarity transformations are used to make the transport equations dimensionless. The final version of the coupled boundary value problem is then numerically solved via RKFST (Runge–Kutta–Fehlberg method based on shooting technique). The post-processing of the solution involves computing the velocity field, skin-friction, Nusselt number, Sherwood number, temperature field, streamlines, isotherms, and concentration field against various estimations of the involved parameters. The streamlines and isotherms are observed to be more effective for the hybrid nanofluid than the nanofluid, whereas the temperature is reduced for the hybrid nanofluid compared to the nanofluid. Temperature is reduced with the development of thermal-relaxation multiplier, while concentration is also declined with the higher estimation of concentration-relaxation multiplier.
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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 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".