Mixed convective viscous dissipative flow of Casson hybrid nanofluid with variable thermal conductivity at the stagnation zone of a rotating sphere
Bibliographic record
Abstract
Abstract Mixed convection flows across the revolving bodies have eminent applications in science and technology, such as fibre coating, polymer deposition, centrifugal blood pumps, rotatory machinery, and so forth. In the current work, magnetohydrodynamic (MHD) flow and heat transfer characteristics of Casson hybrid nanofluid (Ag/MgO as nanoparticles) over the rotating sphere at the stagnation zone are being studied. Moreover, an analysis of heat transmission is conducted by considering the influence of thermal radiation, temperature‐dependent thermal conductivity, magnetic field, and viscous dissipation. The relevant partial differential equations are reformed into ordinary differential equations by appropriate transformations, which are solved using the successive linearization method (SLM). The thermal field, velocity components in x and z directions, heat transfer rate, and skin friction coefficient are computed for various physical quantities like rotation parameter mixed convection parameter, radiation parameter, Eckert number, Casson parameter, magnetic parameter, variable thermal conductivity parameter, and so forth. The current findings align well with the literature in a limiting sense. Thermal enhancement in hybrid nanofluid is observed for the viscous dissipation parameter (Ec), thermal conductivity parameter (), and radiation parameter (Rd). The degree of heat transfer rises from 12.8% to 20% when the Casson parameter's value () increases. Also, a decrease of approximately 33% is depicted between the peak values of the velocity magnitude with an increase in rotation parameter.
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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".