Swimming of microbes in entropy optimized nano‐bioconvective flow of Prandtl–Erying fluid
Bibliographic record
Abstract
Abstract Microbes swimming in a fluid that contains nanoparticles is an intriguing characteristic having ramifications in biomedicine, petroleum science, biofuels, and biotechnology applications. This study gives a theoretical evaluation of the bioconvection phenomena with swimming microorganisms in a Prandtl–Erying nanofluid constructed by an exponential stretched surface, given the amazing applications of bioconvection and nanoparticles. Additionally, the problem is modeled by considering intriguing phenomena such as thermophoretic particle deposition, Darcy–Forchheimer medium, exothermic/endothermic process, and activation energy vitality. The leading problem comprises nonlinear, coupled, partial differential expressions. To run the appraisal process, the controlling problem is transfigured into dimensionless patterns through the usual transformations. A computational finite difference approach is used to quantify the numerical evaluation of fabricated flow problems. To obtain the parametric constraints, stability and convergency were also assessed. Improved visualizations (streamlines, isothermal line, iso‐concentration, iso‐microorganisms) of ongoing flow fields are also illustrated. It is unveiled that the augmentation in velocity ratio factor improves nanofluid velocity and its related boundary layer wideness. The concentration of microbes and nanoparticles is reduced against the bio‐Lewis number and Lewis number precisely. The rate of change in heat transfer is the highest for the presence/absence of the thermophoresis factor. Moreover, Entropy production and Bejan number display the reverse impact for the Brinkman number. The change in entropy rate is 30.60% for the presence/absence of microbes' diffusion parameter. This evaluation could help reduce energy waste and improve the performance and efficiency of industrial and engineering appliances like nuclear power plants, and solar energy production.
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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 | 0.001 | 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".