Effect of Uncertainties in Physical Properties on Entropy Generation Between Two Rotating Cylinders With Nanofluids
Bibliographic record
Abstract
In this paper, the effects of uncertainties in physical properties on predicting entropy generation for a steady laminar flow of Al2O3–ethylene glycol nanofluid (0≤φ≤6 %) between two concentric rotating cylinders are investigated. For this purpose, six different models by combining of three relations for thermal conductivity (Bruggeman, Hamilton–Crosser, and Yu–Choi) and two relations for dynamic viscosity (Brinkman and Maiga et al.) are applied. The governing equations with reasonable assumptions in cylindrical coordinates are simplified and solved to obtain analytical expressions for average entropy generation (NS)ave and average Bejan number (Be)ave. The results show that, when the contribution of heat transfer to entropy generation for the base fluid is dominant, a critical radius ratio (ΠC) can be determined at which all six models predict the reduction in entropy generation with increases of volume fraction of nanoparticles. It is also found that, when the contribution of viscous effects to entropy generation is adequately high for the base fluid (φ=0), all models predict the increase of entropy generation with increases of particle loading.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 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".