Entropy generation and MHD analysis of a nanofluid with peristaltic three dimensional cylindrical enclosures
Bibliographic record
Abstract
Purpose Entropy generation in nanofluids with peristaltic scheme occupies a primary consideration in the sense of its application in clinical, as well as the industrial field in terms of improved thermal conductivity of the original fluid. Three-dimensional cylindrical configurations are the most realistic and commonly used geometries which incorporate most of the experimental equipment. In the current study, three-dimensional cylindrical enclosures have been assumed to receive the results of entropy generation occurring due to viscous dissipation, heat transfer of nanofluid and mass concentration of nanoparticles through peristaltic pumping. Applications of the study can be found in peristaltic micro-pumps and novel drug delivery mechanism in pharmacological engineering. Design/methodology/approach The equations of interest have been structured under physical constraints of lubrication theory and dimensionless strategy. Finalized relations involve highly complicated partial differential equations whose solutions are tabulated through some perturbation procedure and expression of pressure rise is manipulated by a numerical technique through built-in command NIntegrate on Mathematical tool “Mathematica.” Findings It is evaluated that entropy production goes linear with the greater magnitudes of Brownian motion but inverse characteristics have been sorted against thermophoresis factor. Originality/value To the best of authors’ knowledge, this study does not exist in literature yet and it contains a new innovative idea.
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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 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".