Power-Law Nanofluid Flow over a Stretchable Surface Due to Gyrotactic Microorganisms
Bibliographic record
Abstract
This study aims to learn more about how the flow of a power-law nanofluid’s mixed bio-convective stagnation point flow approaching a stretchable surface behaves with the presence of a passively controlled boundary condition. The governing equations incorporate the motile bacterium and nanoparticles, and the current model includes Brownian motion and thermophoresis effects. The governing equations are transformed into ordinary differential equations, which are then numerically solved using the Runge–KuttaFehlberg (RKF) with the shooting technique. The controlling parameters are chosen as follows: the velocity ratio parameter, ε, is taken between 0.1 and 1.5; the mixed convection parameter, λ, is considered in the range 0–3; the buoyancy ratio parameter is considered in the range between 0.1 and 4; the bio-convection parameter, Rb, is taken in the range 0–1; nanofluid parameters are taken in the range 0.1–0.7; the bioconvection Schmidt number is considered in the range 0.1–3; the Prandtl number is taken between 1–4; and the Schmidt number is taken between 1 and 3. The Nusselt number, skin friction, and nanoparticle volume fraction profiles are shown graphically to observe the impact of several parameters under consideration. Both the Schmidt number and the Brownian motion parameter are shown to significantly increase the Sherwood number. Thermophoresis, however, has been proven to lower the Sherwood number. Furthermore, the bioconvection constant and Peclet number both help to slow down the rate of mass transfer. The presented theoretical investigation has a considerable role in engineering, where nanofluid flow is applied to organize a bioconvection process to develop power generation and mechanical energy. One of the more essential features of bioconvection is the aggregation of nanoparticles with motile microorganisms requested to augment the stability, heat, and mass transmission.
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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".