Irreversibility process characteristics of variant viscosity and conductivity on hybrid nanofluid flow through Poiseuille microchannel: A special case study
Bibliographic record
Abstract
The model based upon the Poiseiulle flow of hybrid nanofluid within a micro channel for the inclusion of varying viscosity and thermal conductivity. The suggested model is designed for the use of variable properties of the hybrid nanofluid embedding with the metal and oxide nanoparticles such as Cu and Al2O3 submerged in the base fluids i.e. water and Ethylene glycol (EG). For the preparation nanofluid the base fluid contains a combination of 20% water and 80% of EG. In addition to that, the interpretation of entropy generation due to the thermal irreversibility process of the system is conducted. The suitable choice of the similarity transformation is used for the dimensional form of the present problem to distort into non-dimensional form. Further, the numerical treatment is made employing Runge-Kutta-Fehlberg technique for the solution of the set of transformed equations. The physical behavior of the contributing parameters on the flow phenomena along with the Entropy and Bejan number are presented through graphs. The tabular result depicts the numerical results of the rate coefficients for these parameters. Finally, the comparative study is carried out to validate the current result with the earlier work that shows a greater concurrency.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".