Thermal and species transportation of Eyring-Powell material over a rotating disk with swimming microorganisms: applications to metallurgy
Bibliographic record
Abstract
Transportation of heat and mass for the bio-convective magneto-hydrodynamic flow of Eyring-Powel nanofluid flowing over a rotating disk is inspected in the current exploration which has applications in different industries. i.e. To enhance the thermal performance of the system, mixture of bioconvective is used along with nanofluids. Flow is produced due to the stretching of rotating disk. Phenomenon of heat and mass are developed by using traditional Fourier and Fick's laws respectively. Viscous dissipation is included in the thermal transport expression. Buongiorno model is considered to capture the involvement of Brownian motion and thermophoresis aspects by the presence of nanofluid. Boundary layer approximation is used to develop the expressions for the momentum, heat, mass and swimming gyrotactic microorganism's profiles. The derived boundary layer equations are converted into set of ordinary differential equations by engaging an appropriate transformation. These converted equations are solved numerically with the help of shooting method. Various graphs are prepared in order to inspect the bearing of influential parameters. Moreover, skin friction, heat transfer, mass transfer and density of motile microorganism are presented with the help of graphs and tables.
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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".