Physical prospective of induction of ferro-constituents in buoyancy-driven magneto-nanofluid under the impact of magnetic dipole
Bibliographic record
Abstract
Fulfilment of energy in various sectors of society is the demand of today’s world. Nowadays, researchers are working hard to propose such technologies to eradicate energy losses. Several improvements have been presented by the research community in this regard. After their struggle and hard work, researchers believed that this energy crisis could be overcome with the aid of fluids. So a procedure is executed to subject the fluid flows to a magnetic field. Furthermore, they also disclosed that induction of ferro-elements in a fluid under the existence of magnetic fields, known as ferrofluid, is a remarkable technique to overcome this huge social, economic, and technological problem. So the motto of the current literature is to investigate the elevated thermophysical features of viscous fluid by the inclusion of ferro-particles. To achieve said purpose, nickel zinc ferrite particles (NiZnFe2O4) are added to ethyl alcohol (C2H5OH) in the presence of magnetic dipole. Flow is induced by a two-dimensional linearly elongated sheet. The governing momentum, energy, and thermal transport equations describing the thermomechanical aspects of ferrofluid is modelled in the form of partial differential expressions. Similar variables are needed to transfer the coupled partial expressions into ordinary differential system. Extensive computational interpretation of influencing parameters like ferromagnetic interaction parameter, viscous dissipation, and Curie temperature parameter on associated profiles is accomplished using the shooting method in collaboration with the Runge–Kutta–Fehlberg method. It is found that the primary function of the magnetic dipole is to reduce the velocity within the boundary layer and it tends to enrich the thermal attitude. Diminishing of the momentum distribution of ferro-particles is established in the presence of a magnetic dipole. Inclusion of mixed convection decreases the momentum, whereas an increase is induced in the thermal profile. Decrease of convective heat transfer magnitude is observed in the presence of magnetic dipole.
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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) | 0.001 | 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".