Rotational impact on nanoscale particles Fe<sub>2</sub>O<sub>4</sub>, NiZnFe<sub>2</sub>O<sub>4</sub>, MnZnFe<sub>2</sub>O<sub>4</sub> suspended in C<sub>2</sub>H<sub>6</sub>O<sub>2</sub> confined between two stretchable disks: a computational study
Bibliographic record
Abstract
This communication addresses the fluid and heat transfer flanked by two stretchable rotating disks influenced by the induced magnetic field. The fluid flows due to the rotation of the stretchable disks enclosing a nanofluid having nanoparticles of three distinct ferrite compounds. The mathematical formulation of the problem is performed in a cylindrical coordinate system. Similarity analysis is applied for the sake of simplification. The velocity, pressure, induced magnetic field, and temperature distributions accompanied by surface drag force and heat flux are computed numerically by employing implicit finite difference algorithm. Effects of important emerging parameters are addressed through graphs and tables. Some pivotal findings include that magnetic parameter and reciprocal magnetic Prandtl number contribute to increasing fluid pressure near the lower disk and the opposite trend is reported in the vicinity of the upper disk. The present investigation is a benchmark problem that has a promising future in geophysics, oil refinery, and the chemical processing industry.
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 | 0.001 | 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) | 0.002 | 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".