Thermal and entropy generation analysis of hybrid nanofluid flow through stretchable rotating system with heat source/sink
Bibliographic record
Abstract
This study presents the thermal-fluid transport and entropy generation of a hybrid nanofluid consisting of titanium oxide(TiO2) and gallic oxide(GO), flowing past a stretchable rotating system with a heat source/sink subjected to nonlinear radiation. The nanofluid transport past the rotating disk embedded in a porous medium is described utilizing a higher-order partial nonlinear coupled differential model simplified into an ordinary nonlinear model utilizing Karman’s transformation. This is analyzed utilizing the fourth fifth-order Runge Kutta Fehlberg method (RKF-45) and validated against other literatures for a simple condition that proves satisfactory. The analysis illustrates the impact of nanoparticle volume concentration on entropy generation. This shows that the increasing volume of nanoparticles concentration increases entropy, with the entropy of the hybrid nanofluid lower in relation to the nanofluid. At volume concentration of nanoparticles 0<ϕ<0.2 reveals an entropy magnitude of 7J/K at the lower disk, as the fluid approaches mid plate entropy steadily drops to 3J/K. Thereafter, the magnitude of entropy – as the nano mixture approaches the upper disk – steadily rises to 8J/K. The effect of nanoparticle volume concentration further depicts an increase in Bejan’s number. The study provides good insight into useful and practical applications, including turbines, power generating systems, and blood centrifuges, among others.
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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) | 0.000 | 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".