Numerical Study of the Effect of Magnetic Field on Nanofluid Heat Transfer in Metal Foam Environment
Bibliographic record
Abstract
The magnetic field can act as a suitable control parameter for heat transfer and fluid flow. It can also be used to maximize thermodynamic efficiency in a variety of fields. Nanofluids and porous media are common methods to increase heat transfer. In addition to improving heat transfer, porous media can increase pressure drop. This research is a computational simulation of the impacts of a magnetic field induced into a cylinder in a porous medium for a volume fraction of 0.2 water/Al2O3 nanofluid with a diameter of 10 μm inside the cylinder. For a wide variety of controlling parameters, simulations have been made. The fluid flow in the porous medium is explained using the Darcy-Brinkman-Forchheimer equation, and the nanofluid flow is represented utilizing a two-phase mixed approach as a two-phase flow. In addition, simulations were run in a slow flow state using the finite volume method. The mean Nusselt number and performance evaluation criteria (PEC) were studied for different Darcy and Hartmann numbers. The results show that the amount of heat transfer coefficient increases with increasing the number of Hartmann and Darcy. In addition, the composition of the nanofluid in the base fluid enhanced the PEC in all instances. Furthermore, the PEC has gained its highest value at the conditions relating to the permeable porous medium.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".