Effects of circular corners and aspect-ratio on entropy generation due to natural convection of nanofluid flows in rectangular cavities
Bibliographic record
Abstract
In this paper, entropy generation induced by natural convection of cu-water nanofluid in rectangular cavities with different circular corners and different aspect-ratios were numerically investigated. The governing equations were solved using a finite volume approach and the SIMPLE algorithm was used to couple the pressure and velocity fields. The results showed that the total entropy generation increased with the increase of Rayleigh number, irreversibility coefficient, aspect ratio or solid volume fraction while it decreased with the increase of the corner radius. It should be noted that the best way for minimizing entropy generation is decreasing Rayleigh number. This is the first priority for minimizing entropy generation. The other parameters such as radius, volume fraction, etc are placed on the second priority. However, Bejan number had an inverse trend compared with total entropy generation. As an exception, Bejan number and total entropy number had the same trend whenever solid volume fraction increased. Moreover, Nusselt number increased as Rayleigh number, solid volume fraction or aspect ratio increased whereas it decreases with the increase of corner radius.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".