Numerical investigation of the effects of geometric parameters of heaters on mixed covection in a lid‐driven cavity filled with different nanofluids
Bibliographic record
Abstract
Abstract In this paper, the effects of the thicknesses and locations of two rectangular heaters, located on the bottom and one side of on an enclosure, on mixed convection of nanofluid flows in a lid‐driven cavity are numerically investigated. The enclosure is simultaneously heated partially by these two heaters which have similar or different thicknesses and also filled with different nanofluids containing nanoparticles of Cu, Ag, Al2O3, and TiO2 within the base fluid of water. A finite volume approach by the SIMPLE algorithm is used to solve the governing equations. The effects of different Rayleigh numbers ( ), Reynolds numbers ( ), solid volume fractions ( ), heater lengths ( ), heater locations ( ) and heater thicknesses ( ) on the streamlines, isotherms and the average Nusselt number along two heaters are studied accurately. Also, variations of average Nusselt number of two heaters are considered whenever one heater is fixed and the other heater moves along on the wall. Moreover, variations of the length of one heater on the average Nusselt number are also studied whenever the length of the other heater is fixed. In addition, variations of the thickness of one heater on the average Nusselt number are studied whenever the thickness of the other heater is fixed.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".