Temperature and Entropy Generation Analyses Between and Inside Rotating Cylinders Using Copper–Water Nanofluid
Bibliographic record
Abstract
Entropy generation is squarely linked with irreversibility, and consequently with exergy destruction within a thermal system. This study concerns with the temperature distribution, and local and volumetric averaged entropy generation rates within a cylindrical system with two solid co-rotating inner and outer parts and the middle nanofluid flow part. Temperature-dependent thermal conductivities for solid materials are included within the modeling. To obtain the temperature formula within all three sections, a combined analytical–numerical solution technique is applied. An exact analytical solution is also obtained, when constant thermal conductivities for solid materials are assumed. The resultant data from the analytical–numerical solution technique is verified against the investigated exact solution. Thereafter, the velocity and temperature fields from the combined analytical–numerical solution technique are incorporated into the entropy generation formulations to obtain the local and volumetric averaged entropy generation rates. Using abovementioned procedure, the effects of thermophysical parameters such as nanoparticles volume concentration, Brinkman number, thermal conductivity parameter ratios, outer temperature boundary condition, internal heat generation rates and velocity ratios on the temperature field, and entropy generation rates are investigated.
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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 | 0.001 |
| 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".