The role of radiation and bioconvection as an external agent to control the temperature and motion of fluid over the radially spinning circular surface: A theoretical analysis via Chebyshev spectral approach
Bibliographic record
Abstract
The usage of nanoparticles is effectively increased in industries because of their high thermal performance. Moreover, the bioconvection phenomenon in nanomaterials leads to innovative biotechnology applications, such as biofuels, biosensors, the petroleum industry, etc. Because of the nanoparticle's exceptional performance and bioconvection phenomenon, the magnetohydrodynamics bioconvection Reiner–Rivlin nanofluid flow is considered over the rotatory stretchable disk contains the motile gyrotactic microorganisms. The heat and mass transport phenomenon with thermal radiation and activation energy is also investigated under convective‐Nield's boundary conditions. The governing partial differential equations (PDEs) are transmuted with specific similarity transformation into ordinary differential equations (ODEs). The obtained ODEs are solved numerically through the assistance of the Chebyshev spectral collocation method. The effects of the flow parameters on the boundary layer profiles are reported graphically. The graphical illustration elucidates that the dimensionless parameters have significantly affected the nondimensional boundary layer profiles. The fluid velocity, temperature, concentration of nanoparticles, and motile density of microorganisms are effectively controlled through the proper alteration of the pertinent parameters. The thermophoresis parameter decreases the heat and mass transport rates, whereas the Brownian motion parameter helps to increase them. Finally, the current research can successfully fill a gap in the existing literature.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".