Heat Transfer Enhancement Using Ferro-Nanofluid with Magnetic Field in Tube Having Inserted Twisted Tube
Bibliographic record
Abstract
This study investigates heat transfer enhancement through the application of ferronanofluid in conjunction with twisted tape inserts, with particular emphasis on the effect of escalating ferro-nanofluid concentration.The influence of a magnetic field on the ferro-nanofluid and its subsequent impact on heat transfer rates is also simulated.To gain a deeper understanding of the stream field, computational fluid dynamics studies are undertaken, with the Cartesian direction being the focal point of mathematical methodologies.The numerical model and mesh are constructed using ANSYS.The numerical results elucidate the heat transfer improvement process utilizing twisted tape and ferro-nanofluid under various conditions: rotational cycles, magnetic field intensity, and ferro-nanofluid concentration.Moreover, the influence of these conditions on pressure, temperature, and fluid velocity is assessed when deploying ferro-nanofluid in conjunction with a magnetic field for heat transfer enhancement.The findings reveal that, irrespective of the operating conditions, fluid pressure tends to decrease while the fluid temperature and velocity increase with the intensification of the magnetic field, the concentration of ferro-nanofluid, and the rotational cycles.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".